Deep learning enabled segmentation of medical images with missing modalities

Abstract

A first training sample having a first modality configuration and a second training sample having a second modality configuration are generated based on a training image. The first and second modality configuration having a different combination of modalities. A segmentation model is trained based on the first and second training sample to perform a first task of operating on the first modality configuration and a second task of operating on the second modality configuration. The segmentation model is trained to generate an output segmentation map based on outputs from a first subset of parameters in the segmentation model and a second subset of parameters in the segmentation model. The training of the segmentation model includes adjust the parameters of the segmentation model to determine, for each of the first and second task, a different combination of weights for combining the outputs of the first and second subset of parameters.

Description

DEEP LEARNING ENABLED SEGMENTATION OF MEDICAL IMAGES WITH MISSING MODALITIESCROSS REFERENCE TO RELATED APPLICATION

[0001] This application claims priority to U.S. Provisional Application No. 63 / 561,918, entitled “DEED LEARNING ENABLED SEGMENTATION OF MEDICAL IMAGES WITH MISSING MODALITIES” and filed on March 6, 2024, the disclosure of which is incorporated herein by reference in its entirety.TECHNICAL FIELD

[0002] The present disclosure generally relates to machine learning and more specifically to deep learning based techniques for segmentation of medical images in the absence of one or more modalities.INTRODUCTION

[0003] Medical imaging refers to techniques and processes for obtaining data characterizing a subject’s internal anatomy and pathophysiology including, for example, images created by the detection of radiation either passing through the body (e.g. x-rays) or emitted by administered radiopharmaceuticals (e.g. gamma rays from intravenously administered radioactive tracers). By revealing internal anatomical structures obscured by other tissues such as skin, subcutaneous fat, and bones, medical imagining is integral to numerous medical diagnosis and / or treatments. Examples of medical imaging modalities include 2-dimensional imaging such as x- ray plain films, bone scintigraphy, and thermography. Examples of 3-dimensional imaging modalities include magnetic resonance imaging (MRI), computed tomography (CT), cardiac sestamibi scanning, and positron emission tomography (PET). In some cases, a single medical image can combine multiple imaging modalities. For example, in cases where single gantry incorporates a positron emission tomography (PET) scanner and a computed tomography (CT)scanner), the resulting scans are combined into a single superposed (e.g., co-registered) image (e.g., a PET-CT scan) in which the spatial distribution of metabolic activities depicted in the PET scan is aligned with the anatomical structures depicted in the CT scan. Within a single type of medical images, different imaging protocols can also give rise to different imaging modalities. For instance, variations in the sequence in which pulses of radio frequency (RF) energy are applied during the acquisition of MRI scans may yield different modality MRI scans such as Ti-weighted scans, T2-weighted scans, proton density (PD) weighted scans, fluid attenuated inversion recovery (FLAIR) scans, and / or the like.SUMMARY

[0004] Methods, systems, and articles of manufacture, including computer program products, are provided for deep learning based techniques for segmenting a medical image in the absence of one or more imaging modalities. For example, according to various implementations of the present disclosure, a multimodal medical image that combines multiple scans of imaging different modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), optical coherence tomography (OCT), microscopy, X-ray, and ultrasound, may be segmented in instances where at least one such imaging modalities are missing. In some cases, a magnetic resonance (MRI) scan may be segmented in the absence of one or more MRI modalities such as a Ti-weighted scan, a T2-weighted scan, a proton density (PD) weighted scan, a fluid attenuated inversion recovery (FLAIR) scan, and / or the like.

[0005] In one aspect, there is provided a system for segmenting a medical image in the absence of one or more imaging modalities. The system may include at least one data processor and at least one memory. The at least one memory may store instructions, which when executed by the at least one data processor, result in operations comprising: generating, based at least on atraining image having a plurality of modalities, a first training sample having a first modality configuration and a second training sample having a second modality configuration, the first modality configuration having a different combination of modalities than the second modality configuration; training, based at least on the first training sample and the second training sample, a segmentation model to perform a first task of operating on the first modality configuration and a second task of operating on the second modality configuration, the segmentation model being trained to generate an output segmentation map based on a combination that includes a first output from a first subset of parameters in the segmentation model and a second output from a second subset of parameters in the segmentation model, the training of the segmentation model includes adjusting one or more parameters of the segmentation model to determine, for each of the first task and the second task, a different combination of weights for combining the first output of the first subset of parameters and the second output of the second subset of parameters; and segmenting an input image by at least applying the segmentation model to generate, based at least on a modality configuration of the input image, a segmentation map localizing one or more regions of interest (ROI) present in the input image.

[0006] In another aspect, there is provided a computer-implemented method for segmenting a medical image in the absence of one or more imaging modalities. The method may include: generating, based at least on a training image having a plurality of modalities, a first training sample having a first modality configuration and a second training sample having a second modality configuration, the first modality configuration having a different combination of modalities than the second modality configuration; training, based at least on the first training sample and the second training sample, a segmentation model to perform a first task of operating on the first modality configuration and a second task of operating on the second modalityconfiguration, the segmentation model being trained to generate an output segmentation map based on a combination that includes a first output from a first subset of parameters in the segmentation model and a second output from a second subset of parameters in the segmentation model, the training of the segmentation model includes adjusting one or more parameters of the segmentation model to determine, for each of the first task and the second task, a different combination of weights for combining the first output of the first subset of parameters and the second output of the second subset of parameters; and segmenting an input image by at least applying the segmentation model to generate, based at least on a modality configuration of the input image, a segmentation map localizing one or more regions of interest (ROI) present in the input image.

[0007] In another aspect, there is provided a computer program product for segmenting a medical image in the absence of one or more imaging modalities. The computer program product may include a non-transitory computer readable medium storing instructions, which when executed by at least one data processor, result in operations. The operations may include: generating, based at least on a training image having a plurality of modalities, a first training sample having a first modality configuration and a second training sample having a second modality configuration, the first modality configuration having a different combination of modalities than the second modality configuration; training, based at least on the first training sample and the second training sample, a segmentation model to perform a first task of operating on the first modality configuration and a second task of operating on the second modality configuration, the segmentation model being trained to generate an output segmentation map based on a combination that includes a first output from a first subset of parameters in the segmentation model and a second output from a second subset of parameters in the segmentation model, the training of the segmentation model includes adjusting one or more parameters of the segmentationmodel to determine, for each of the first task and the second task, a different combination of weights for combining the first output of the first subset of parameters and the second output of the second subset of parameters; and segmenting an input image by at least applying the segmentation model to generate, based at least on a modality configuration of the input image, a segmentation map localizing one or more regions of interest (ROI) present in the input image.

[0008] In another aspect, there is provided a system for segmenting a medical image in the absence of one or more imaging modalities. The system may include at least one data processor and at least one memory. The at least one memory may store instructions, which when executed by the at least one data processor, result in operations comprising: generating a first training sample having a first modality configuration by at least dropping a first modality from a modality configuration of a training image having a plurality of modalities; generating a second training sample having a second modality configuration by at least dropping a second modality from the modality configuration of the training image; training, based at least on the first training sample and the second training sample, a segmentation model, the training includes adjusting one or more parameters of the segmentation model to perform a first task of operating on the first modality configuration and a second task of operating on the second modality configuration, the segmentation model being trained to generate an output segmentation map based on a combination that includes a first output from a first subset of parameters in the segmentation model and a second output from a second subset of parameters in the segmentation model, the training of the segmentation model includes adjusting at least one gating network in the segmentation model to determine, for each of the first task and the second task, a different combination of weights for combining the first output of the first subset of parameters and the second output of the second subset of parameters; and segmenting an input image by at least applying the segmentation modelto generate, based at least a modality configuration of the input image, a segmentation map localizing one or more regions of interest (RO I) present in the input image.

[0009] In another aspect, there is provided a computer-implemented method for segmenting a medical image in the absence of one or more imaging modalities. The method may include: generating a first training sample having a first modality configuration by at least dropping a first modality from a modality configuration of a training image having a plurality of modalities; generating a second training sample having a second modality configuration by at least dropping a second modality from the modality configuration of the training image; training, based at least on the first training sample and the second training sample, a segmentation model, the training includes adjusting one or more parameters of the segmentation model to perform a first task of operating on the first modality configuration and a second task of operating on the second modality configuration, the segmentation model being trained to generate an output segmentation map based on a combination that includes a first output from a first subset of parameters in the segmentation model and a second output from a second subset of parameters in the segmentation model, the training of the segmentation model includes adjusting at least one gating network in the segmentation model to determine, for each of the first task and the second task, a different combination of weights for combining the first output of the first subset of parameters and the second output of the second subset of parameters; and segmenting an input image by at least applying the segmentation model to generate, based at least a modality configuration of the input image, a segmentation map localizing one or more regions of interest (ROI) present in the input image.

[0010] In another aspect, there is provided a computer program product for segmenting a medical image in the absence of one or more imaging modalities. The computer program productmay include a non-transitory computer readable medium storing instructions, which when executed by at least one data processor, result in operations. The operations may include: generating a first training sample having a first modality configuration by at least dropping a first modality from a modality configuration of a training image having a plurality of modalities; generating a second training sample having a second modality configuration by at least dropping a second modality from the modality configuration of the training image; training, based at least on the first training sample and the second training sample, a segmentation model, the training includes adjusting one or more parameters of the segmentation model to perform a first task of operating on the first modality configuration and a second task of operating on the second modality configuration, the segmentation model being trained to generate an output segmentation map based on a combination that includes a first output from a first subset of parameters in the segmentation model and a second output from a second subset of parameters in the segmentation model, the training of the segmentation model includes adjusting at least one gating network in the segmentation model to determine, for each of the first task and the second task, a different combination of weights for combining the first output of the first subset of parameters and the second output of the second subset of parameters; and segmenting an input image by at least applying the segmentation model to generate, based at least a modality configuration of the input image, a segmentation map localizing one or more regions of interest (ROI) present in the input image.

[0011] In some variations, one or more features disclosed herein including the following features can optionally be included in any feasible combination.

[0012] In some variations, the segmentation model includes a gating network that determines, based at least on the modality configuration of the input image, the combination ofweights for combining the first output of the first subset of parameters and the second output of the second subset of parameters.

[0013] In some variations, the segmentation model generates the segmentation map by at least determining a weighted summation in which a first weight from the combination of weights is applied to the first output of the first subset of parameters and a second weight from the combination of weights is applied to the second output of the second subset of parameters.

[0014] In some variations, the gating network, the first subset of parameters, and the second subset of parameters form a mixture-of-expert (MoE) layer in the segmentation model.

[0015] In some variations, the segmentation model includes one or more additional mixture-of-expert (MoE) layers, and each additional mixture-of-expert (MoE) layer includes a different gating network.

[0016] In some variations, each of the first training sample and the second training sample are generated by least dropping one or more different modalities of the plurality of modalities present in the training image.

[0017] In some variations, the first training sample is generated, based at least on a training image, to include a first modality but not a second modality present in a third modality configuration of the training image.

[0018] In some variations, generating the first training sample includes generating an input tensor having a first channel corresponding to the first modality and a second channel corresponding to the second modality, and the first channel of the first input tensor is set to zero in order to indicate an absence of the first modality.

[0019] In some variations, the training the segmentation model to perform the first task of operating on the first modality configuration includes training the segmentation model to adapt to an absence of the first modality when segmenting the first training sample.

[0020] In some variations, the second training sample is generated, based at least on the training image, to include the second modality but not the first modality present in the third modality configuration of the training image.

[0021] In some variations, a third training sample having the third modality configuration is generated based at least on the training image. The segmentation model is trained, based at least on the third training sample, to perform a third task of operating on the third modality configuration. The training of the segmentation model further includes adjusting the one or more parameters of the segmentation model to determine, for the third task, a combination of weights for combining the first output of the first subset of parameters and the second output of the second subset of parameters

[0022] In some variations, the combination of weights is further determined to combine the first output of the first subset of parameters, the second output of the second subset of parameters, and / or a third output of a third subset of parameters.

[0023] In some variations, the training image is a magnetic resonance imaging (MRI) scan, and the first modality and the second modality comprise a different one of a T1 -weighted scan, a T2-weighted scan, a proton density (PD) weighted scan, and a fluid attenuated inversion recovery (FLAIR) scan.

[0024] In some variations, the training image is a computed tomography (CT) scan, and the first modality and the second modality comprise a different one of a sequential CT scan, aspiral CT scan, an electron beam tomography scan, a dual energy CT scan, and a CT perfusion scan.

[0025] In some variations, the training image is a positron emission tomography (PET) scan, and the first modality and the second modality comprise scans acquired using different radiotracers.

[0026] In some variations, the training image is a multimodal medical image, and the first modality and the second modality comprise a different one of a magnetic resonance imaging (MRI) scan, a positron emission tomography (PET), a computed tomography (CT) scan, an X-ray, a microscopy image, an optical coherence tomography (OCT) scan, and an ultrasound.

[0027] In some variations, the segmentation model is a machine learning model in which the first subset of parameters and the second subset of parameters form a layer of the machine learning model.

[0028] In some variations, an input tensor having a plurality of channels corresponding to a plurality of modalities is generated based at least on the input image. The generating of the input tensor includes setting, to zero, all intensity values of a plurality of pixels in a channel associated with a modality absent from the first modality configuration of the input image. The input image is segmented by at least applying the segmentation model to operate on the input tensor

[0029] In some variations, the input tensor includes a k quantity of fixed channels for a k quantity of modalities.

[0030] In some variations, at least one training sample is generated for inclusion in a training set for each of a 2fc— 1 quantity of possible modality configurations arising from the kquantity of modalities. The segmentation model is trained, based at least on the training set, to adapt to the 2k— 1 quantity of possible modality configurations.

[0031] In some variations, the training of the segmentation model includes adjusting one or more parameters of the segmentation model to reduce a difference between the output segmentation map generated by the segmentation model for each training sample and a corresponding ground-truth segmentation map

[0032] In some variations, the training of the segmentation model includes adjusting the one or more parameters of the segmentation model to increase a uniformity of an average combination of weights across the first task and the second task.

[0033] In some variations, the training of the segmentation model includes adjusting one or more parameters of the segmentation model to increase a variance or sparsity between individual weights in each combination of weights for combining the first output of the first subset of parameters and the second output of the second subset of parameters.

[0034] In some variations, the training of the segmentation model includes adjusting one or more parameters of the segmentation model to increase a similarity between a first output segmentation map of the segmentation model operating on a modality configuration without one or more modalities to a second output segmentation map of the segmentation model operating on another modality configuration having the one or more modalities.

[0035] In some variations, the segmentation model generates the segmentation map by at least assigning, to each pixel in the segmentation map, a value indicating a probability of the pixel depicting at least a portion of a region of interest (RO I) present in the input image.

[0036] In some variations, the region of interest (ROI) includes one or more of a tissue, an organ, a lesion, and / or a tumor.

[0037] In some variations, the value is a binary value or a value from a continuous range of values.

[0038] In some variations, the training image and the input image are two-dimensional images or three-dimensional volumes comprising a series of two-dimensional images.

[0039] In some variations, the training of the segmentation model includes: applying the segmentation model to segment a training image having a plurality of modalities; applying the segmentation model to segment the training image absent at least one modality of the plurality of image; and adjusting one or more parameters of the segmentation model to reduce a difference between a first output of the segmentation model operating on the training image having the plurality of modalities and a second output of the segmentation model operating on the training image absent the at least one modality of the plurality of modalities.

[0040] Implementations of the current subject matter can include methods consistent with the descriptions provided herein as well as articles that comprise a tangibly embodied machine-readable medium operable to cause one or more machines (e.g., computers, etc.) to result in operations implementing one or more of the described features. Similarly, computer systems are also described that may include one or more processors and one or more memories coupled to the one or more processors. A memory, which can include a non-transitory computer-readable or machine-readable storage medium, may include, encode, store, or the like one or more programs that cause one or more processors to perform one or more of the operations described herein. Computer implemented methods consistent with one or more implementations of the current subject matter can be implemented by one or more processors residing in a single computing system or multiple computing systems. Such multiple computing systems can be connected and can exchange data and / or commands or other instructions or the like via one or more connections,including, for example, to a connection over a network (e.g. the Internet, a wireless wide area network, a local area network, a wide area network, a wired network, or the like), via a direct connection between one or more of the multiple computing systems, etc.

[0041] The details of one or more variations of the subject matter described herein are set forth in the accompanying drawings and the description below. Other features and advantages of the subject matter described herein will be apparent from the description and drawings, and from the claims. While certain features of the currently disclosed subject matter are described for illustrative purposes in relation to deep learning techniques for segmenting magnetic resonance imaging (MRI) scans, it should be readily understood that such features are not intended to be limiting. The claims that follow this disclosure are intended to define the scope of the protected subject matter.DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings, which are incorporated in and constitute a part of this specification, show certain aspects of the subject matter disclosed herein and, together with the description, help explain some of the principles associated with the disclosed implementations. In the drawings,

[0043] FIG. 1 depicts a system diagram illustrating an example a segmentation system, consistent with implementations of the current subject matter;

[0044] FIG. 2A depicts a schematic diagram illustrating an example of modality dropout training of a segmentation model, in accordance with some example embodiments;

[0045] FIG. 2B depicts a schematic diagram illustrating an example of an input tensor, in accordance with some example embodiments;

[0046] FIG. 3A depicts a block diagram illustrating an example of a conventional residual block, in accordance with some example embodiments;

[0047] FIG. 3B depicts a block diagram illustrating an example of a residual block having a mixture-of-experts (MoE) layer, in accordance with some example embodiments;

[0048] FIG. 4 depicts a schematic diagram illustrating an example of a mixture-of- experts (MoE) layer, in accordance with some example embodiments;

[0049] FIG. 5 depicts a flowchart illustrating an example of a process for multi-modality medical image segmentation, in accordance with some example embodiments;

[0050] FIG. 6 depicts a flowchart illustrating an example of a process for training a segmentation model having one or more mixture-of-experts (MoE) layers to segment medical scans having different modality configurations, in accordance with some example embodiments;

[0051] FIG. 7 depicts a flowchart illustrating an example of a self-distillation process for training a segmentation model having one or more mixture-of-experts (MoD) layers to segment medical scans having different modality configurations, in accordance with some example embodiments;

[0052] FIG. 8 depicts a flowchart illustrating an example of a process for taskconditional segmentation of medical scans with a segmentation model having one or more mixture- of-experts (MoE) layers, in accordance with some example embodiments; and

[0053] FIG. 9 depicts a block diagram illustrating an example of a computing system, in accordance with some example embodiments.

[0054] When practical, like labels are used to refer to same or similar items in the drawings.DETAILED DESCRIPTION

[0055] A medical image may undergo segmentation in order to identify one or more regions of interest (ROI) present therein. In some cases, a region of interest (ROI) may correspond to at least a portion of an anatomical structure such as a tissue (e.g., a group of cells with similar structures and a share function), an organ (e.g., a group of tissues performing a specific function), or an abnormality (e.g., lesions, tumors, and / or the like). In cases where a single type of region of interest (ROI) may be present in the medical image (e.g., single-class or single-label segmentation), the segmenting of the medical image may include assigning, to each pixel therein, a value indicative of a probability that the pixel depicts at least a portion of the region of interest (ROI). Alternatively, in cases where different types of regions of interest (ROI) may be present in the medical image (e g., multi-class or multi-label segmentation), the segmenting of the medical image may include assigning, to each pixel in the medical image, multiple values, each of which indicative of a probability that the pixel depicts a corresponding type of region of interest. In some cases, the medical image may be segmented to localize the one or more regions of interest (ROI) for a variety of clinical tasks including, for example, diagnosis, treatment planning, quantitative analysis, and / or the like. As described in more details below, segmentation performance, or the ability to correctly identify the pixels depicting the one or more regions of interest (ROI), may be improved if segmentation is performed based on a medical image that combines individual scans of different imaging modalities at least because different imaging modalities may contribute complementary information.

[0056] In some example embodiments, a segmentation model may be applied to segment a multimodal medical image. As used herein, the term “multimodal medical image” may refer to a medical image that combines scans of two or more different imaging modalities or, alternatively, two or more different sub-modalities. For example, in some cases, a single multimodal medicalimage may include scans of two or more imaging modalities such as two or more of a positron emission tomography (PET) scan, a computed tomography (CT) scan, a magnetic resonance imaging (MRI) scan, an optical coherence tomography (OCT) scan, a microscopy image, an X- ray, and an ultrasound. Alternatively, in this context, the term “multimodal medical image” may also refer a medical image that combines scans of two or more different modalities (or different sub-modalities) of a single imaging modality. For instance, in some cases, the multimodal medical image may combine scans of two or more MRI modalities (e.g., a T1 -weighted scan, a T2- weighted scan, a proton density (PD) weighted scan, and a fluid attenuated inversion recovery (FLAIR) scan), PET modalities (e.g., a first PET scan acquired using one radiotracer and a second PET acquired using a different radiotracer), or CT modalities (e.g., a sequential CT scan, a spiral CT scan, an electron beam tomography scan, a dual energy CT scan, and a CT perfusion scan). In the case of multiple sclerosis (MS), a chronic autoimmune disease characterized by demyelination and atrophy of the brain and spinal cord, disease activity and progression may be assessed by characterizing lesions depicted in scans from a combination of MRI modalities including, for example, pre-contrast T1 -weighted (Tlw), post-contrast T2-weighted (T2w), proton density (PD), and fluid-attenuated inversion recovery (FLAIR).

[0057] Although the inclusion of multiple imaging modalities may improve segmentation performance, conventional segmentation models require a fixed imaging modality configuration (or combination of imaging modalities) as inputs. That is, a segmentation model trained on training images having one modality configuration (or combination of imaging modalities) may be unable to operate on input images having a different modality configuration(or combination of modalities) at inference time. For example, where the segmentation model is trained on a combination of T1 -weighted (Tlw) scans, T2-weighted (T2w) scans, and FLAIRscans, the same segmentation model may be unable to operate on input images whose modality configuration is missing one or more of Tl-weighted (Tlw) scans, T2-weighted (T2w) scans, andFLAIR scans. However, consistency in the modality configuration of input images may be difficult to achieve in practice. In the case of magnetic resonance imaging (MRI), scans originating from different MRI facilities (or sites) are likely to exhibit different modality configurations (or combinations of modalities) due to the prevalence of different imaging protocols. Thus, a first MRI scan acquired at a first site may have a first modality configuration (e g., a first combination of imaging modalities that includes a Ti-weighted scan, a T2-weighted scan, and a FLAIR scan) while a second MRI scan acquired at a second site may have a second modality configuration (e.g., a second combination of modalities that includes a T2-weighted scan and a FLAIR scan but not a Ti-weighted scan). A segmentation model that is trained to operate on a single modality configuration may have limited clinical utility. For instance, if the segmentation model is trained to operate on the first modality configuration from the first site, that same segmentation model may be unable to operate on the second modality configuration from the second site. Training a different segmentation model for every possible modality configuration requires too much computational resources to be a practical solution. Where there are a k quantity of modalities, for example, a total of 2k— 1 segmentation models may be trained, validated, and tested in order to account for all 2k— 1 possible combinations of the k quantity of modalities.

[0058] While training segmentation models for every possible modality configuration is impractical, other conventional approaches to reconciling different modality configurations also fail to adequately accommodate multi-site data. For example, selecting a set of common modalities that is present in all medical images may require discarding one or more modalities that are present in some modality configurations but not others. In the foregoing example, the Ti-weighted scans included in the MRI scans captured at the first site may be discarded at least because that modality is absent from the second modality configuration of the MRI scans captured at the second site. Doing so may diminish the performance of the segmentation model by limiting the quantity of information that is present in the inputs ingested by the segmentation model. For instance, the segmentation model may be required to segment one or more regions of interest (e.g., tissue, organ, lesions, tumors, and / or the like) present in the first MRI scan without the benefit of the complementary information provided by the Ti-weighted scans. Alternatively, selecting the modality configuration with more modalities (e.g., the first modality configuration in the foregoing example) may reduce the overall quantity of data available to train the segmentation model at least because those medical images with fewer modalities (e g., the second MRI scan in the foregoing example) are discarded. Moreover, this approach limits the clinical utility of the segmentation model because the segmentation model is able to operate on the modality configuration with more modalities (e.g., the first modality configuration in the foregoing example) but not the modality configuration with fewer modalities (e.g., the second modality configuration in the foregoing example).

[0059] Various embodiments of the present disclosure overcome the limitations associated with segmenting multimodal medical images with conventional segmentation models. In some example embodiments, a segmentation controller may perform segmentation of medical images by at least applying a multi-modality segmentation model that has been trained to operate on a variety of modality configurations. Training a single multi-modality segmentation model to operate on multiple modality configurations may increase the clinical utility of the segmentation model without the resource burden imposed by the naive multi-model solution noted earlier. For example, instead of training a 2k— 1 quantity of single modality segmentation models toaccommodate the 2k— 1 quantity of possible modality configurations arising from a k quantity of imaging modalities, a single multi -modality segmentation model may be trained to adapt to the 2k— 1 quantity of possible modality configurations. In some cases, in order to adapt to the 2k— 1 quantity of possible modality configurations, the multi-modality segmentation model may be trained to leverage all k quantity of modalities as well as to adjust to the absence of one or more modalities. For instance, in the foregoing MRI example, the multi-modality segmentation model may be trained to operate on the first modality configuration, which includes being trained to leverage the complimentary information included in Ti-weighted scans, T2-weighted scans, and FLAIR scans. Furthermore, the multi-modality segmentation model may also be trained to adapt to the absence of Ti-weighted scans such that the multi-modality segmentation model is also able to operate on the second modality configuration.

[0060] In some example embodiments, to train the multi-modality segmentation model to adapt to multiple modality configurations, the segmentation controller may generate training samples that exhibit a variety of modality configurations. With the foregoing MRI example, for instance, the segmentation controller may generate, based at least on the first MRI scan, a first training sample that includes a Ti-weighted scan, a Tz-weighted scan, and a FLAIR scan. Furthermore, the segmentation controller may generate, based at least on the first MRI scan, a second training sample in which one or more of the Ti-weighted scan, T2-weighted scan, and FLAIR scan are dropped (or masked out). In some cases, the multi-modality segmentation model may be trained, based on a training dataset that includes the first training sample and the second training sample, to perform multiple individual tasks, each of which corresponding to a different combination of Ti-weighted scans, T2-weighted scans, and FLAIR scans. For instance, in some cases, the segmentation model may be trained to perform a first task of operating on the firstmodality configuration, which includes learning to leverage the complimentary information provided by Ti-weighted scans, T2-weighted scans, and FLAIR scans. Furthermore, the segmentation model may be trained to perform a second task of operating on the second modality configuration, which includes learning to adapt to the absence of Ti-weighted scans.

[0061] That the training dataset is being generated to include training samples of different modality configurations means that the segmentation controller is able to utilize medical images having a variety of different modality configuration when generating training samples. For example, in some cases, in addition to the first MRI scan having the first modality configuration, the segmentation controller may also generate, based at least on the second MRI scan having the second modality configuration, a third training sample. In some cases, the third training sample may be generated without Ti-weighted scans. As such, in some cases, the multi -modality segmentation model may be further trained, based at least on the third training sample, to perform the second task of operating on the second modality configuration and localize one or more regions of interest (ROI) without relying on the complimentary information provided by Ti-weighted scans. It should be appreciated that the ability to use different modality configuration medical images to generate training samples may increase the quantity of training samples available for training the multi-modality segmentation model and improve the performance of the multimodality segmentation model. For instance, the training samples generated based on the second MRI scan, which has fewer modalities than the first MRI scan, may increase the quantity of training samples that train the segmentation model to adjust to the absence of one or more modalities. Contrastingly, conventional solutions that harmonize different modality configurations by discarding MRI scans with fewer modalities, such as the aforementioned secondMRI scan, may yield fewer training samples for the multi-modality segmentation model.

[0062] In some example embodiments, the training of the segmentation model may include adjusting one or more parameters (e.g., weights, biases, and / or the like) of the segmentation model to reduce (or minimize) a loss function quantifying the error in the output of the segmentation model. For example, in cases where the segmentation model outputs a segmentation mask identifying a first plurality of pixels depicting at least a portion of a region of interest (RO I) depicted in a training sample, the loss in the output of the segmentation model may include a difference between the segmentation mask and a ground truth segmentation mask identifying a second plurality of pixels depicting one or more actual regions of interest present in the training sample. In some cases, the training of the segmentation model may also include reducing (or minimizing) a loss function quantifying a self-distillation loss.

[0063] In cases where the training of the multi-modality segmentation model is formulated as optimizing a multi-task objective in which each modality configuration is considered a related but separate task, the parameters of the multi-modality segmentation model may be adjusted to simultaneously accommodate multiple tasks. However, in cases where the parameters of the multi-modality segmentation model are shared across multiple tasks, such as the first task of operating on the first modality configuration and the second task of operating on the second modality configuration, the multi-modality segmentation model may underperform relative to single task segmentation models dedicated to specific modality configurations due to conflicts between different tasks. For example, in some cases, training the multi-modality segmentation model to perform the second task of operating on the second modality configuration may interfere with the ability of the multi-modality segmentation model to perform the first task of operating on the first modality configuration. In some cases, this interference may arise due to the multimodality segmentation model having insufficient capacity (or quantity of parameters) toaccommodate the different adjustments made for each individual task. As such, the adjustments made to the parameters of the multi-modality segmentation model for the first task of operating on the first modality configuration may be disrupted by subsequent adjustments made to the parameters of the multi-modality segmentation model when the multi-modality segmentation model is trained to also perform the second task of operating on the second modality configuration.

[0064] In some example embodiments, the performance of the multi-modality segmentation model across different tasks of operating on different modality configurations may be improved by reducing the extent of parameter sharing across separate tasks. For example, in some cases, the multi-modality segmentation model may include one or more mixture-of-experts (MoE) layers with each mixture-of-experts (MoE) layer having a set of N experts. In some cases, each expert may correspond to a subset of the parameters (e.g., a convolution layer) in the multimodality segmentation model. Moreover, in some cases, each mixture-of-experts (MoE) layer may include a gating network trained to combine the outputs of the constituent set of N experts based on the modality configuration of the medical image ingested as input by the multi-modality segmentation model. For instance, in some cases, each of the 2k— 1 quantity of possible modality configurations may be associated with a different modality code. As described in more details below, the gating network for a mixture-of-experts (MoE) layer having a set of N experts may determine, based at least on the modality code of the input medical image, a plurality of weights (e.g., an / V-dimensional vector of weights) to combine the outputs of the set of N experts to generate the output of the mixture-of-experts (MoE) layer.

[0065] FIG. 1 depicts a system diagram illustrating an example of a segmentation system 100, consistent with implementations of the current subject matter. Referring to FIG. 1, the segmentation system 100 may include a segmentation controller 110, one or more imaging devices120, and a client device 130. The segmentation controller 110, the one or more imaging devices 120, and the client device 130 may be communicatively coupled via a network 140. The network 140 may be a wired network and / or a wireless network including, for example, a wide area network (WAN), a local area network (LAN), a virtual local area network (VLAN), a public land mobile network (PLMN), the Internet, and / or the like. The client device 130 may be a processor-based device including, for example, a smartphone, a tablet computer, a wearable apparatus, a virtual assistant, an Internet-of-Things (loT) appliance, and / or the like. In some cases, the segmentation controller 110, the one or more imaging devices 120, and / or the client device 130 may be contained within and / or operate on a same platform and / or device. For example, in some cases, the client device 130 may form a part of, include, and / or be coupled to the one or more imaging devices 120.

[0066] Referring again to FIG. 1, in some cases, the one or more imaging devices 120 may generate one or more images 125 including, for example, a training image 125a, an input image 125b, and / or the like. In some cases, each of the one or more images 125 may be a two- dimensional image formed by a matrix of pixels. Alternatively, each of the one or more images 125 may be a three-dimensional volume formed from a series of two-dimensional images (or slices), with each voxel in the three-dimensional volume corresponding to a pixel in one of the constituent two-dimensional images (or slices). As described in more details below, in some cases, the one or more imaging devices 120 may generate each of the one or more images 125 to include scans of one or more different imaging modalities including, for example, magnetic resonance imaging (MRI), positron emission tomography (PET), computed tomography (CT), X-ray, ultrasound, microscopy, optical coherence tomography (OCT), and / or the like. It should be appreciated that the modality configuration of each of the one or more images 125 may correspond to the imaging modalities of the constituent scans.

[0067] In some example embodiments, the one or more imaging devices 120 may capture different modalities of a same type of medical images. For example, in some cases, the one or more imaging devices 120 may include one or more magnetic resonance imaging (MRI) scanners that generate each of the one or more images 125 to include one or more different MRI modalities including, for example, Ti-weighted scans, T2-weighted scans, proton density (PD) scans, fluid attenuated inversion recovery (FLAIR) scans, and / or the like. Different MRI modalities may be characterized by at least some difference in the appearance of the anatomical features depicted therein, with scans of different MRI modalities providing complimentary information. For instance, fluids (e.g., urine, cerebrospinal fluids, and / or the like) appear with low intensity in Ti- weighted scans and high intensity in T2-weighted scans while white matter brain tissue appears with hyperintensity in Ti-weighted scans but hypointensity in T2-weighted scans. Different imaging protocols, including variations in the sequence in which pulses of radio frequency (RF) energy are applied to generate the one or more images 125, may give rise to different modality configurations. In the example shown in FIG. 1, the one or more imaging devices 120 may apply one imaging protocol to generate the training image 125a to exhibit a first modality configuration 150a while the one or more imaging devices 120 (or a different imaging device) may apply a different imaging protocol to generate the input image 125b to exhibit a second modality configuration 150b having a different combination of MRI modalities than the first modality configuration 150a.

[0068] In some cases, in addition to or instead of one or more MRI scanners, the one or more imaging devices 120 may include one or more positron emission tomography (PET) scanners that generate each of the one or more images 125 to include one or more different PET modalities, each of which being associated with a different radiotracer. For example, in some cases, each ofthe one or more images 125 may include a first scan acquired using a first radiotracer (e.g.,18F- fluoro-deoxyglucose (18F-FDG)), a second scan acquired using a second radiotracers (e.g.,nC- acetate), a third scan acquired using a third radiotracer (e.g.,1'C-choline). and / or the like.

[0069] In some cases, the one or more imaging device 120 may include one or more computed tomography (CT) scanners that generates each of the one or more images 125 to include one or more scans of different CT modalities including, for example, a sequential CT scan, a spiral CT scan, an electron beam tomography scan, a dual energy CT scan, and a CT perfusion scan.

[0070] In some example embodiments, the one or more imaging devices 120 may include (e.g., in a single or multiple gantries) two or more different types of imaging devices including, for example, a magnetic resonance imaging (MRI) scanner, a computed tomography (CT) scanner, a positron emission tomography (PET) scanner, an optical coherence tomography (OCT) scanner, a microscopy camera, an ultrasound, an X-ray, and / or the like. Accordingly, in some cases, the one or more images 125 may be generated to include two or more types of medical images, with each type of medical images constituting a different imaging modality in this context. For example, in some cases, the one or more imaging device 120 may include a positron emission tomography (PET) scanner and a computed tomography (CT) scanner capable of acquiring positron emission tomography (PET) scans and computed tomography (CT) scans during a same session. The one or more images 125 may each include a positron emission tomography (PET) scan and computed tomography (CT) scan combined into a single superposed (e.g., co-registered) image (e.g., a PET-CT scan) in which the spatial distribution of metabolic activities depicted in the positron emission tomography (PET) scan is aligned with the anatomical structures depicted in the computed tomography (CT) scan.

[0071] Referring again to FIG. 1, in some example embodiments, the analysis controller 110 may include a dropout engine 111, a segmentation model 113, and an assessment engine 115. In some cases, the dropout engine 111 may generate, based at least on the training images 125a from the one or more imaging devices 120, a training dataset of training samples having different modality configurations. As described in more details below, the dropout engine 111 may generate one or more training samples in the training dataset by at least dropping (or masking out) one or more of the modalities present in the training image 125a such that at least some of the training samples derived therefrom exhibit a different modality configuration than the training image 125a. Moreover, in some cases, the analysis controller 110 may train, based at least on the training dataset, the segmentation model 1 13 to adapt to a variety of different modality configurations when segmenting, for example, the input image 125b to localize one or more regions of interest (ROI) present therein. For example, in some cases, the segmentation model 113 may segment the input image 125b and generate a segmentation mask in which each pixel in the input image 125b is assigned one or more values (e.g., a binary value, a value from a range such as [0,1], and / or the like), each of which indicating a probability that the pixel depicts at least a portion of a corresponding type of region of interest (e g., a tissue, an organ, a lesion, a tumor, and / or the like) present in the input image 125b. It should be appreciated that the segmentation model 113 may be capable of adapting to the second modality configuration 150b of the input image 125b even though one or more modalities are absent from the second modality configuration 150b of the input image 125b.

[0072] FIG. 2A depicts a schematic diagram illustrating an example of modality dropout training of the segmentation model 113, in accordance with some example embodiments.Referring to FIG. 2A, in some cases, the one or more imaging devices 120 may generate thetraining image 125a to include a first modality Ml, a second modality M2, and a third modality M3. The dropout engine 111 may generate, based at least on the training image 125a, a training dataset 200 for training the segmentation model 1 13. For example, in instances where there are a k quantity of modalities, the dropout engine 111 may drop (or mask out) one or more of the k quantity of modalities to generate samples that exhibit one or more of the 2k— 1 possible modality configurations. Moreover, in instances where the training image 125a is represented as a tensor having K quantity channels, each of which corresponding to one of the k quantity of modalities, the dropping (or masking out) of one or more modalities may include setting the corresponding channels in the tensor to zero. It should be appreciated that in addition to scans in one or more modalities, each of the first sample 210a, the second sample 210b, and the third sample 210c may include a ground truth segmentation map in which each constituent pixel is associated with one or more values (e.g., a binary value, a value from a range such as [0,1], and / or the like), each of which indicating the probability of the pixel depicting at least a portion of an actual region of interest (RO I) present in the training image 125a.

[0073] To further illustrate, FIG. 2A shows that the dropout engine 111 may generate, for inclusion in the training dataset 110, a first sample 210a having the same first modality configuration 150a as the training image 125a (e.g., the first modality Ml, the second modality M2, and the third modality M3). Furthermore, FIG. 2A shows that the dropout engine 111 may also generate samples by at least dropping (or masking out) one or more of the modalities included in the training image 125a such that the training dataset 200 includes one or more samples having a different modality configuration than the training image 125a (e.g., modality configurations with one or more missing modalities). For example, in some cases, the dropout engine 111 may generate a second sample 210b by at least dropping (or masking out) the second modality M2 suchthat the second sample 210b exhibits the second modality configuration 150b, which includes the first modality Ml and the third modality M3 but not the first modality Ml. Alternatively and / or additionally, FIG. 2A shows that the dropout engine 111 may generate, for inclusion in the training dataset 200, a third sample 210c by at least dropping (or masking out) the first modality Ml and the third modality M3 such that the third sample 210c exhibits a third modality configuration 150c that includes the second modality M2 but neither the first modality Ml nor the third modality M3.

[0074] FIG. 2B depicts a schematic diagram illustrating an example of an input tensor 250, in accordance with some example embodiments. In some example embodiments, the segmentation model 113 may ingest, during training, validation, testing, and / or inference, one or more inputs in the form of the input tensor 250. For example, in the example shown in FIG. 2A, one or more of the first sample 210a, the second sample 210b, and the third sample 210c may be rendered in the form of the input tensor 250. Moreover, the input image 125b may be rendered in the form of the input tensor 250 prior to being ingested by the segmentation model 113. As shown in FIG. 2A, in some cases, the input tensor 250 may include one or more fixed channels, each of which corresponding to a different modality. In instances where there are a k quantity of modalities, the input tensor 250 may include a k quantity of fixed channels. For instance, the example of the input tensor 250 shown in FIG. 2B includes a first channel corresponding to the first modality Ml, a second channel corresponding to the second modality M2, and a third channel corresponding to the third modality M3. For each modality that is present in a medical image, the corresponding channel in the input tensor 250 may include the intensity values of each pixel (or voxel) in the medical image. Contrastingly, in cases where one or more modalities are absent from the medical image, the corresponding channels (e.g., the intensity values of the constituent pixels (or voxels)) may be set to zero. Accordingly, the second channel in the input tensor 250 of thesecond sample 210b may be set to zero in order to reflect the absence of the second modality M2 from the second sample 210b while the first channel and the third channel in the input tensor 250 of the third sample 210c may be set to zero in order to reflect the absence of the first modality Ml and the third modality M3 from the third sample 210c.

[0075] In some example embodiments, the segmentation controller 110 may train, based at least on the training dataset 200, the segmentation model 113 to operate on multiple modality configurations. For example, in some cases, the segmentation controller 110 may train the segmentation model 113 to operate on the first modality configuration, which includes leveraging the complimentary information present in the first modality Ml, the second modality M2, and the third modality M3. Furthermore, the segmentation model 113 may be trained, based at least on the training dataset 200, to adapt to the second modality configuration 150b and the third modality configuration 150c, which includes adapting to the absence of one or more of the first modality Ml, the second modality M2, and the third modality M3. In some cases, the training of the segmentation model 113 may include adjusting one or more parameters (e.g., weights, biases, and / or the like) of the segmentation model 113 to reduce (or minimize) a loss function. As described in more details below, this loss function may quantify a segmentation loss associated with the segmentation model 113 segmenting medical images, a regularization loss associated with the operations of one or more gating networks within the segmentation model 113, and a selfdistillation loss associated with the difference in the output of the segmentation model 113 for modality configurations with fewer modalities.

[0076] In some example embodiments, the segmentation model 113 may include one or more mixture-of-experts (MoE) layers, each of which having a set of N experts. In some cases, each expert may correspond to a subset of parameters in the segmentation model 113, such as theparameters forming one or more layers (e.g., convolution layers) of the segmentation model 113.For example, in some cases, the segmentation model 113 may include a first expert corresponding to a first subset of parameters, a second expert corresponding to a second subset of parameters, and a third expert corresponding to a third subset of parameters. As described in more details below, every one of the N experts in a mixture-of-experts (MoE) layer in the segmentation model 113 may participate in the segmentation of a medical image but their outputs may contribute differently to the output of the segmentation model 113. For instance, in some cases, the segmentation model 113 may include at least one gating network (p trained to combine the outputs of the N experts in accordance with the modality configuration of the medical image being segmented by the segmentation model 113. For example, in some cases, the gating network <p may combine the outputs from the N experts such that the output of those experts (e.g., in each individual mixture-of-expert (MoE) layer) that specialize in the modality configuration of the medical image may contribute more to the overall output (e.g., segmentation map) of the segmentation model 113 than those experts not specializing in the modality configuration of the medical image.

[0077] To further illustrate, FIGS. 3A-B depict block diagrams illustrating a comparison between the architecture of a conventional residual block 300 and that of residual block 325 having a mixture-of-experts (MoE) layer 350, in accordance with some example embodiments. As shown in FIG. 3A, the conventional residual block 300 may be a standard pre-activation residual block in which the same convolution layers are shared across multiple different tasks, each of which being associated with a different modality configuration. With the architecture of the conventional residual block 300, the same set of parameters (e.g., weights, biases, and / or the like) may be adjusted to accommodate multiple tasks. However, the adjustments made to the parameters forthe first task of operating on the first modality configuration 150a, for example, may be disrupted by subsequent adjustments made to the parameters during training to perform the second task of operating on the second modality configuration 150b. Accordingly, a segmentation model containing the conventional residual block 300 may underperform compared to single task segmentation models dedicated to each of the first task and the second task.

[0078] Contrastingly, FIG. 3B shows an example of the residual block 325 having at least one mixture-of-experts (MoE) layer 350, in accordance with some example embodiments. A more detailed depiction of the architecture of the mixture-of-experts layer 350 is further shown in FIG. 4. As shown in FIG. 4, the mixture-of-experts layer 350 may include an N quantity of experts Ej, E2, ... , EN. In some cases, each of the N quantity of experts EltE2, ... , ENmay include a subset of the parameters in the segmentation model 113 (e.g., the parameters forming one or more layers in the segmentation model 113) trained, for example, based on the training dataset 200, to specialize in one or more tasks. In some cases, each one of the experts EltE2, ... , ENmay be trained to specialize in one or more of the 2k— 1 possible modality configurations. For example, in some cases, the first expert Ermay be a first subset of parameters (e.g., in a first layer of the segmentation model 113) while the second expert E2may be a second subset of parameters (e.g., in a second layer of the segmentation model 113). It should be appreciated that the N quantity of experts may correspond to the 2k— 1 quantity of possible modality configurations. For instance, in some cases, each individual one of the N quantity of experts EltE2, ..., ENmay be trained to specialize in a single task of operating on a single one of the 2^ — 1 possible modality configurations. However, it should be appreciated that N may take on a value (e.g., a user-defined value) that does not necessarily correlate with the 2k— 1 quantity of possible modality configurations. Thus, various implementations of the present disclosure also contemplate having one or more of the same expertsto specialize in multiple tasks and for multiple experts to specialize in one or more of the same tasks. Moreover, in cases where the segmentation model 113 includes multiple mixture-of-experts (MoE) layers, the value of N may vary or remain the same across different mixture-of-experts (MoE) layers in the segmentation model 113.

[0079] Referring again to FIG. 4, in some cases, the mixture-of-experts layer 350 may include a gating network < > that combines, based at least on a modality code m, the outputs of the N quantity of experts E1,E2, ..., EN. In some cases, the gating network <p may determine a combination of weights, such as an -dimensional vector (p(m) of weights, that corresponds to the modality code m. It should be appreciated that the values of the weights may correspond to the extent to which each of the N experts E^ , E2, ... , ENspecialize in the modality configuration having the modality code m. That is, the gating network <p determines whether an expert specialize in a particular modality configuration by at least assigning a corresponding weight to the output of the expert. As described in more details below, the training of the segmentation model 113 may include training the gating network to learn which ones of the N experts EltE2, ... , ENspecialize in different modality configurations by generating a corresponding combination of weight (e.g., the A-dimensional vector < >(m)). Applying this A-dimensional weight vector to the outputs of the N quantity of experts EltE2, ... , ENmay generate a weighted summation of the outputs of each individual experts in which with greater contributions from outputs of those experts specializing in the task of operating on the modality configuration having the modality code m than from the outputs of those experts not specializing in the task of operating on the modality configuration having the modality code m. Although FIG. 4 shows an example of the mixture-of-experts layer 350 in which the gating network <p is exclusive to the mixture-of-experts layer 350, it is also possible for the gating network (f> to be shared by multiple mixture-of-experts (MoE) layers. Whena single gating network <p is shared between two or more mixture-of-experts (MoE) layers, the gating network (p may be further trained to determine, for each mixture-of-experts (MoE) layer, a separate combination of weights (e.g., separate IV-dimensional weight vector < >(m)) for combining the outputs of the constituent experts.

[0080] It should be appreciated that implementing the mixture-of-experts layer 350 in the manner shown may improve the performance of the segmentation model 113 across multiple tasks by at least reducing the extent to which the parameters of the segmentation model 113 is shared across different tasks. For example, in instances where the segmentation model 113 includes the conventional residual block 300, the parameters of the segmentation model 113 may be shared across multiple tasks including, for example, the first task of operating on the first modality configuration 150a, the second task of operating on the second modality configuration 150b, and the third task of operating on the third modality configuration 150c. That is, the extent to which the outputs of different parameters (or different subsets of parameters) in the segmentation model 113 contribute to the overall output of the segmentation model 113 may not vary based on the task being performed by the segmentation model 113. Contrastingly, according to some example embodiments described herein, the segmentation model 113 may be implemented to include the residual block 325, which includes the mixture-of-experts (MoE) layer 350. In some cases, each expert within the mixture-of-layer (MoE) layer 350 may correspond to a subset of parameters within the segmentation model 113. However, even though every expert (or subset of parameters) in the mixture-of-experts (MoE) layer 350 may participate in the segmentation of a medical image, the gating network < > may determine how much their respective outputs contribute to the overall output of the segmentation model 113. For instance, as described in more details below, the gating network <p being trained to generate the weight vector <p(m) to increase anuniformity in a mean combination of weights across different tasks such that the N quantity of experts Ex, E2, ... , ENare equally active on expectation over the different tasks. Furthermore, the gating network may be trained to generate the weight vector < >(m) to increase a variance between the weights assigned to different experts such that the output of one or more experts specializing in the modality configuration of each medical image encountered by the segmentation model 113 contributes more to the output of the segmentation model 113 than the output of those not specializing in that modality configuration. It should be appreciated that parameter sharing may be reduced at least because different experts (or different subsets of parameters) may contribute differently to the overall output of the segmentation model 113 depending on the task being performed by the segmentation model 113.

[0081] In some example embodiments, the segmentation model 113 may be implemented as a machine learning model including, for example, a deep learning model such as a convolutional neural network (e.g., a fully convolutional neural network and / or the like). In the example shown in FIG. 3B, the segmentation model 113 may include a stem convolution layer (e g., a 3 x 3 x 3 convolution layer) projecting the quantity of input tensor channels (e.g., a K quantity channels corresponding to a k quantity of modalities) to a number of fixed channels (e.g., Nch= 24). Each subsequent encoding block, which perform a down-sampling operation (e.g., using strided convolution), may be followed by a pre-activation residual block. Following the encoding blocks, one or more decoder blocks may perform an upsampling operation (e.g., linear upsampling) followed by a pre-activation residue block. In some cases, skip connections (e.g., concatentation style skip connections) may be used between encoding blocks and decoding blocks of equal resolution. Each pre-activation residual block may include one or more convolution layers(e.g., two 3 x 3 x 3 convolution layers) and an activation function (e.g., Rectified Linear Unit(ReLU) activation function with group normalization). As noted, the segmentation model 113 may include one or more mixture-of-experts (MoE) layers, such as the mixture-of-experts layer 350 shown in FIGS. 3B and 4. It should be appreciated that the one or more mixture-of-experts (MoE) layers may be disposed at any location within the segmentation model 113.

[0082] FIG. 5 depicts a flowchart illustrating an example of a process 500 for multimodality medical image segmentation, in accordance with some example embodiments. Referring to FIGS. 1-2, 3A-B, and 4-5, the process 500 may be performed by the segmentation controller 110 to train and apply, for example, the segmentation model 113 to segment one or more medical images. In some cases, the segmentation controller 110 may perform the process 500 in scenarios where the segmentation model 113 may encounter, as inputs, medical images having different modality configurations. For example, in some cases, the segmentation controller 110 may perform the process 500 such that the medical images exhibiting different modality configurations may be used to generate training samples for training the segmentation model 113. In some cases, a medical image having one modality configuration may be used to generate one or more training samples having a different modality configuration altogether. Furthermore, once trained, the segmentation model 113 may be applied to segment medical images having different modality configurations. For instance, in some cases, the segmentation controller 110 may perform the process 500 to train the segmentation model 113 to perform the first task of operating on the first modality configuration, which may include learning to leverage the complimentary information provided by a first combination of modalities. Moreover, the segmentation controller 110 may perform the process 500 to train the segmentation model 113 to also perform the second task of operating on the second modality configuration 150b, which may include learning to adapt to asecond combination of modalities that excludes one or more of the modalities present in the first modality configuration 150a.

[0083] In some cases, performing the process 500 to train the segmentation model 113 to adapt to different modality configurations may be advantageous at least because the resulting segmentation model 113 is more flexible than conventional segmentation models trained to operate on a single, fixed modality configuration. The segmentation model 113 may also achieve better segmentation performance on multi-site data than conventional, fixed modality configuration segmentation models trained without medical images with one or more missing modalities. In some cases, performing the process 500 to train the segmentation model 113 to adapt to different modality configurations may also be computationally efficient than training a separate segmentation model for every possible modality configuration. The latter approach may require training, validating, and testing a total of 2k— 1 segmentation models if there are a k quantity of modalities, which is not a practical solution when k is a large number. As described in more details below, the segmentation model 113 may be implemented with a mixture-of-expert (MoE) architecture such that the performance of segmentation model 113 across multiple modality configurations is comparable to or, in some cases, even better than the performance of single task segmentation models dedicated to individual modality configurations.

[0084] At 502, the segmentation controller 110 may generate, based at least on a training image having a plurality of modalities, a first training sample having the first modality configuration and a second training sample having a second modality configuration. In some example embodiments, the dropout engine 111 of the segmentation controller 110 may generate, based at least on the training image 125a having the first modality configuration, the first sample 210a having the same first modality configuration 150a as the training image 125a, the secondsample 210b having the second modality configuration 150b, and the third sample 210c having the third modality configuration 150c. As described in more details below, the second sample 210b and the third sample 210c may be generated by dropping (or masking out) one or more of the modalities present in the training image 125a. For example, in cases where the first modality configuration 150a of the training image 125a includes the first modality Ml, the second modality M2, and the third modality M3, the second sample 210b may be generated by dropping (or masking out) the second modality M2 while the third sample 210c may be generated by dropping (or masking out) the third modality M3.

[0085] To further illustrate, consider the training image 125a as being a multi-modality image x with a corresponding label y (e.g., a ground-truth segmentation mask in which each pixel is associated with one or more values indicating the probability of the pixel depicting at least a portion of one or more actual regions of interest (ROI) present in the training image 125a). In some cases, the image x may be represented as a tensor with K channels (e.g., x = [x1, ... , x / f]). The generating of the second sample 210b and the third sample 210c may correspond to the dropout engine 111 performing modality dropout to set one or more tensor channels (e.g., one or more random tensor channels) to zero and generating one or more corresponding modalitydropped images x, as shown in Equation (1) below. x(x, m) = [x'm1, ..., xKmK] (1) wherein m G {0,l] denotes a modality code vector (e.g., a random modality code vector) of length K configuring the absence or presence of each of the K modalities. In some cases, the same label y (e.g., the same ground-truth segmentation mask) may be associated with each of the modality-dropped images x. Accordingly, it should be appreciated that modality dropout mayhave the effect of augmenting the single multi-modality image x into multiple modality-dropped images x.

[0086] At 504, the segmentation controller 110 may train, based at least on the first training sample and the second training sample, a segmentation model to perform a first task of operating on the first modality configuration and a second task of operating on the second modality configuration. In some example embodiments, the segmentation controller 110 may train the segmentation model 113 to perform multiple tasks including, in instances where there are a 2fc-1quantity of possible modality configurations, a 2k— 1 quantity of different tasks of operating on each of the 2k— 1 quantity of modality configurations. For example, in some cases, the segmentation controller 110 may train, based at least on the first sample 210a and the second sample 210b, the segmentation model 113 to perform the first task of operating on the first modality configuration 150a and the second task of operating on the second modality configuration 150b. In some cases, the segmentation model 113 may be trained to generate an output segmentation map based on a combination that includes a first output from a first subset of parameters in the segmentation model 113 and a second output from a second subset of parameters. The training of the segmentation model 113 may include adjusting one or more parameters (e.g., weights, biases, and / or the like) of the segmentation model 113 to determine, for each of the first task and the second task, a different combination of weights for combining the first output of the first subset of parameters and the second output of the second subset of parameters.

[0087] In some cases, even though the first subset of parameters may specialize in the first task and the second subset of parameters may specialize in the second task, the training of the segmentation model 113 may include training the segmentation model 113 to perform each of the first task and the second task by at least adjusting the first subset of parameters as well as thesecond subset of parameters. However, in some cases, adjustments made to the first subset of parameters when the segmentation model 113 is trained to perform the first task may be disrupted when the segmentation model 113 is further trained to perform the second task. Parameter sharing across the first task and the second task (or the negative effects thereof) may be reduced (or minimized) by at least training the segmentation model 113 to vary, depending on whether the segmentation model 113 is performing the first task or the second task, the extent to which the first output of the first subset of parameters and the second output of the second subset of parameters contribute to the output of the segmentation model 113. Accordingly, in cases where the segmentation model 113 is performing the first task, interference from the second subset of parameters may be reduced (or minimized) by at least reducing (or minimizing) the extent to which the second output from the second subset of parameters contributes to the output of the segmentation model 113. Contrastingly, when the segmentation model 113 is performing the second task, the extent to which the first output from the first subset of parameters may be reduced (or minimized) in order to reduce (or minimize) interference from the first subset of parameters.

[0088] In some cases, the output segmentation map of the segmentation model 113 may further include contributions from a third output of a third subset of parameters. For example, in some cases, this third subset of parameters may be trained to specialize in the first task, the second task, and / or a third task of operating on the third modality configuration 150c. Accordingly, in some cases, the segmentation model 113 may be trained to combine the first output of the first subset of parameters, the second output of the second subset of parameters, and a third output of the third subset of parameters when generating the output segmentation map. For instance, in some cases, the training of the segmentation model 113 may include adjusting the one or more parameters (e.g., weights, biases, and / or the like) of the segmentation model 113 to determine, foreach of the first task, the second task, and / or the third task, a different combination of weights for combining the first output of the first subset of parameters, the second output of the second subset of parameters, and the third output of the third subset of parameters.

[0089] Contrastingly, some conventional approaches may require training, validating, and testing a separate segmentation model for each individual task. Where there are 2fc-1quantity of possible modality configurations, for instance, a 2k— 1 quantity of segmentation models may be trained to operate on a different one of the 2k— 1 quantity of possible modality configurations. Accordingly, various implementations of the approach described herein, which includes training the segmentation model 113 to adapt to multiple different modality configuration, is a more efficient solution that imposes less computational burden. For example, in some cases, instead of the same parameters (e.g., the same convolution layers) being shared across multiple tasks, the segmentation model 113 may include the mixture-of-experts layer 350 in which a different one of the N experts, each being a subset of parameters in the segmentation model 113, is trained to specialize in a different task, such as operating on one of the 2k— 1 quantity of possible modality configurations. Reducing the extent to which the parameters of the segmentation model 113 is shared across different tasks (e.g., of operating on different modality configurations) may improve the performance of the segmentation model 113 across different tasks by at least reducing the interference between different tasks without increasing the capacity (e.g., the quantity of parameters) of the segmentation model 113. For example, when the segmentation model 113 is deployed to segment various medical images (e.g., in operation 506), the segmentation model 113 may exhibit equal or, in some cases, better segmentation performance for medical images having the first modality configuration 150a, the second modality configuration 150b, and the third modality configuration 150c than single task segmentation models dedicated to those individualmodality configurations. That the segmentation model 113 achieves equal or superior segmentation performance through reduced parameter sharing instead of increased capacity (e.g., larger quantity of parameters) means that the segmentation model 113 is also computationally efficient.

[0090] At 506, the segmentation controller 110 may segment an input image by at least applying the segmentation model to generate, based at least on a modality configuration of the input image, a segmentation mask localizing one or more regions of interest present in the input image. In some example embodiments, the segmentation controller 110 may apply the segmentation model 113 to segment, for example, the input image 125b by at least generating a segmentation mask in which each pixel in the input image 125b is assigned one or more values (e.g., a binary value, a value from a range such as [0,1], and / or the like) indicating the probability of the pixel depicting at least a portion of one or more corresponding types of regions of interest (ROI) present in the input image 125b. Because the segmentation model 113 is trained to operate on multiple modality configurations, the segmentation model 113 may adapt to the modality configuration of the input image 125b even one or more modalities are absent therefrom. As described in more details below, the output of the segmentation model 113 segmenting the input image 125b may be generated by combining the outputs of the N quantity of experts EltE2, ENincluded one or more mixture-of-experts (MoE) layers in the segmentation model 113. For instance, in some cases, each mixture-of-experts (MoE) layer may include a gating network < > may be trained to generate an / V-dimensional vector of weights (p(m) corresponding to the modality code m of the input image 125b such that the A-dimensional vector of weights (p(m) may be applied to combine the outputs of the N quantity of experts E1(E2, ..., ENsuch that the outputs of those experts specializing in the modality configuration having the modality code m contributemore to the segmentation mask generated by the segmentation model 113 than the outputs of those experts not specializing in the modality configuration having the modality code m. Accordingly, in cases where the segmentation model 113 is operating on one particular modality configuration, interference from experts (or subsets of parameters) not specializing in that modality configuration may be reduced (or minimized) by at least reducing (or minimizing) the extent to which outputs from those experts (or subsets of parameters) contribute to the overall output of the segmentation model 113. Reducing interference between experts (or subsets of parameters) with different specializations may improve the performance of the segmentation model 113 across different modality configurations without increasing the capacity (e.g., the quantity of parameters) of the segmentation model 113.

[0091] FIG. 6 depicts a flowchart illustrating an example of a process for training a segmentation model having a mixture-of-experts (MoE) layer to segment medical images having different modality configurations, in accordance with some example embodiments. Referring to FIGS. 1-2, 3A-B, and 4-6, the process 600 may be performed by the segmentation controller 110 to train, for example, the segmentation model 113 to segment one or more medical images. In some cases, the process 600 may implement operation 504 of the process 500 shown in FIG. 5. For example, in some cases, the segmentation controller 110 may perform the process 600 in order to train the segmentation model 113 to perform multiple tasks, each of which corresponding to the segmenting of medical images having a different modality configuration. Moreover, in some cases, the segmentation engine 113 includes one or more mixture-of-experts layers, such as the mixture-of-experts layer 350 shown in FIGS. 3B and 4. Accordingly, the segmentation controller 110 may perform the process 600 in order to train the segmentation model 113 to combine the outputs of the multiple experts (or subsets of parameters in the segmentation model 113), witheach expert being trained to specialize in one or more specific tasks. Performing the process 600 to train the segmentation model 113 may be advantageous at least because doing so reduces (or minimizes) parameter sharing across task and thus avoid interference between different tasks, which occurs when the outputs of the parameters in the segmentation model 113 contribute equally to multiple different tasks. While parameter sharing is reduced (or minimized), the segmentation model 113 trained by performing the process 600 may nevertheless leverage complementary information across different imaging modalities, including by combining the outputs of individual experts (or subsets of parameters in the segmentation model 113) based on the specialty of each expert. As such, it should be appreciated that the segmentation model 113 trained by the performing of the process 600 may exhibit comparable or, in some cases, even better performance than separate single task segmentation models dedicated to individual modality configurations.

[0092] At 602, the segmentation controller 110 may adjust a segmentation model to at least reduce a first loss associated with the segmentation model segmenting medical images. In some example embodiments, the segmentation controller 110 may train the segmentation model 113, for example, through modality dropout, to adapt to multiple modality configurations. For example, in some cases, the training of the segmentation model 113 may include adjusting one or more parameters (e.g., weights, biases, and / or the like) of the segmentation model 113 to reduce (or minimize) the loss (or error) in the output of the segmentation model 113 operating on training samples having the first modality configuration 150a as well as training samples having the second modality configuration 150b, which is missing one or more of the modalities present in the first modality configuration 150a.

[0093] As noted, in instances where the segmentation model 113 includes at least one mixture-of-experts (MoE) layer having the first expert E±specializing in the first modalityconfiguration 150a and the second expert E2specializing in the second modality configuration150b, training the segmentation model 113 to perform each of the first task of operating on the first modality configuration 150a and the second task of operating on the second modality configuration 150b may include adjusting both the first subset of parameters of the first expert E1and the second subset of parameters of the second expert E2. Parameter sharing across the first task and the second task may be reduced (or minimized) by at least adjusting the parameters of the gating network (p to vary, based on whether the segmentation model 113 is performing the first task or the second task, the respective contributions from the first output of the first subset of parameters and the second output of the second subset of parameters. In some cases, the first subset of parameters, the second subset of parameters, and the parameters of the gating network <p may be adjusted in order to reduce (or minimize) the loss (or error) in the output of the segmentation model 113 performing the first task of operating on the first modality configuration 150a as well as the second task of operating on the second modality configuration 150b. That is, in addition to adjusting the first subset of parameters and the second subset of parameters, the parameters of the gating network <p may be adjusted such that the extent to which the first output from the first subset of parameters and the second output from the second subset of parameters contribute to the output of the mixture-of-experts (MoE) layer reduces (or minimizes) the loss (or error) in the outputs of the segmentation model 113. For example, in instances where a higher contribution from the first output of the first subset of parameters and / or a lower contribution from the second output of the second subset of parameters reduce (or minimize) the loss (or error) in the outputs of the segmentation model 113 operating on the first modality configuration 150a, the parameters of the gating network <p may be adjusted to such that the gating network <p generates a corresponding combination of weights (e.g., weight vector (m)).

[0094] In some cases, the output of the segmentation model 113 operating on the medical image included in each training sample may include a segmentation mask in which each pixel in the medical image is assigned one or more values (e.g., a binary value, a value from a range such as [0,1], and / or the like), each of which indicating the probability of the pixel depicting at least a portion of a corresponding type of region of interest (e.g., tissue, organ, lesion, tumor, and / or the like) present in the medical image. Accordingly, in some cases, the loss (or error) in the output of the segmentation model 113 for a training sample may include a difference between the segmentation mask generated by the segmentation model 113 for the constituent medical image and the corresponding ground-truth segmentation mask in which each pixel is assigned one or more values (e.g., a binary value, a value from a range such as [0,1], and / or the like) indicating the probability of the pixel depicting at least a portion of an actual region of interest present in that medical image. Moreover, the training of the segmentation model 113 may include reducing (or minimizing) this loss (or error) across multiple training samples exhibiting a variety of different modality configurations. For instance, in addition to being trained to leverage the complimentary information that is available when multiple modality configurations are present, the segmentation model 113 may be trained to adapt to the absence of one or more modalities.

[0095] To further illustrate, in some cases, the segmentation model 113 may be trained based on the multi -modality image x as well as one or more modality-dropped images x. Training the segmentation model 113 on the modality -dropped images x may be tantamount to discouraging the segmentation model 113 from relying on interactions between certain modalities. For example, in some cases, the segmentation model 113 may be trained on the modality-dropped images x in order to discourage the segmentation model 113 from relying on the complimentary information present in the dropped (or masked out) modalities. Letting (x, y) be a sample (e.g., randomsample) from the training dataset T> and assuming that the modality code m is uniformly sampled from the set M containing the 2k— 1 quantity of possible modality configurations, modality dropout training of the segmentation model 113 may include reducing (or minimizing) the dropped modality loss Lmddefined n Equation (2) below.wherein fedenotes the segmentation model 113 with the parameters 0 and I denotes the segmentation loss function.

[0096] In a batch setting in which a batch of images {(xb,yb}b=1~D is provided, the dropout engine 111 may generate a corresponding batchof modality dropped images, for example, by randomly and uniformly sampling a modality code mb~M for each image in the batch. It should be appreciated that in this batch setting, the dropped modality loss £mddefined in Equation (3) below may be reduced (or minimized) with contributions from both the original images x and the modality-dropped images x generated therefrom.

[0097] At 604, the segmentation controller 110 may adjust the segmentation model to reduce a second loss associated with combining outputs from multiple subsets of parameters in the segmentation model that are trained to specialize in different modality configurations. In some example embodiments, the segmentation model 113 may include one or more mixture-of-experts (MoE) layers, each of which having a set of N experts■ ■■, EW. In some cases, each one of the N experts may be trained to specialize in one or more tasks of operating on one or more corresponding modality configurations. As noted, while in some cases each one of the N experts may be trained to specialize in a different one of the 2fc— 1 quantity of possible modality configurations arising from a k quantity of modalities, it is also possible for more than one expertto specialize in the same modality configurations or for one expert to specialize in multiple modality configurations.

[0098] In some cases, the segmentation model 113 may include at least one gating network trained to learn the level of specialization associated with each expert. As noted, each mixture-of-experts (MoE) layer may be associated with a gating network < >, which may or may not be shared with other mixture-of-experts (MoE) layers in the segmentation model 113. Accordingly, in some cases, the training of the segmentation model 113 may include adjusting the gating network (p layer to combine, based at least on the modality code m, the outputs of the N experts in one or more mixture-of-experts (MoE) layers. For example, in some cases, the gating network of a mixture-of-experts (MoE) layer may generate a combination of weights, such as the weight vector (m), that includes a first weight for the first output from the first expert E±(or the first subset of parameters) in the mixture-of-experts (MoE) layer and a second weight for the second output from the second expert E2(or the second subset of parameters) of the mixture-of- experts (MoE) layer. In some cases, the first weight and the second weight may be different in order to reflect the difference in the extent to which each of the first expert E^ and the second expert E2specialize in the task of operating on the modality configuration having the modality code m. This difference in the first weight and the second weight may engender a corresponding difference in the extent to which the first output of the first expert E and the second output of the second expert E2contribute to the output of the mixture-of-expert (MoE) layer.

[0099] In some cases, depending on the modality configuration (e.g., the modality code m) of the medical image being segmented by the segmentation model 113, the outputs from different experts in a mixture-of-experts (MoE) layer in the segmentation model 113 may contribute differently to the overall output of that mixture-of-experts (MoE) layer. In this context,a first expert whose first output contributes more to the output of the mixture-of-experts (MoE) layer may be considered as being more specialized in the modality configuration of the medical image than a second expert whose output contributes less to the output of the mixture-of-experts (MoE) layer. As noted, each one of the N experts in a mixture-of-experts (MoE) layer may correspond to a subset of parameters in the segmentation model 113. For example, in some cases, a first subset of parameters in the segmentation model 113 may correspond to the first expert E^ while a second subset of parameters in the segmentation model 113 may correspond to the second expert E2. Training the segmentation model 113 may include adjusting one or more parameters of the segmentation model 113, including the first subset of parameters and the second set of parameters. Reduction (or minimization) of parameter sharing between the different tasks of operating on different modality configurations may be achieved by the gating network <p varying, depending on the modality configuration of the medical image being segmented by the segmentation model 113, the extent to which the output of each one of the N experts contributes to the output of the mixture-of-expert (MoE) layer. For instance, while the first subset of parameters corresponding to the first expert E1and the second subset of parameters corresponding to the second expert E2may both undergo adjustments while the segmentation model 113 is being trained to operate on a particular modality configuration, the first output of the first expert E may contribute more to the output of the mixture-of-experts (MoE) layer than the second output of the second expert E2to at least reduce (or minimize) the extent to which the adjustments made to the second subset of parameters interferes with the output of the segmentation model 113.

[0100] In some example embodiments, the training of the segmentation model 113 having the set of N experts (e.g., in the mixture-of-experts (MoE) layer 350) may further include adjusting the gating network (p to determine, based at least on the modality code m of each trainingsample, a combination of weights (e.g., the weight vector (pfni)) for combining the outputs from each of the N experts operating on the medical images included with each training sample. For example, as shown in Equation (4) below, the gating network < > may be parameterized as a linear layer operating on the modality code m followed by the softmax operation a to output an N- dimensional vector of weights.wherein is a matrix andis a bias learned through the training of the segmentation model 113.

[0101] Given the foregoing formulation of the gating network < >, the output foutof the mixture-of-experts (MoE) layer 350 may correspond to a weighted summation of the outputs of the individual experts EltE2, ...,EN, as shown in Equation (5) below. According to Equation (5), each one of the experts EltE2, ..., ENmay specialize in one or more tasks to reduce (or minimize) parameter sharing across different tasks. While Equation (5) requires N convolutions, that the convolutions may be linear, meaning that the expert parameters (e.g., weights, biases, and / or the like) may be mixed first before a single convolution is applied with the mixed parameters. Doing so may reduce the computational burden imposed by the inclusion of one or more mixture-of- experts (MoE) layers in the segmentation model 113. fout = n=l (f^nEMtn) (5)

[0102] In some example embodiments, the segmentation model 113 may be trained such that each one of the experts ElrE2, ... , ENspecialize in one or more tasks and is equally active on expectation over different tasks. This behavior may be explicitly encouraged by the inclusion of the regularization loss defined in Equation (6) below. It should be appreciated that in Equation (6), <pl(rri) may denote the vector of expert weights in response to modality code m at the Z-thmixture-of-experts (MoE) layer in the segmentation model 113 and cv is the coefficient of variation. Accordingly, Equation (6) below defines the regularization loss Lrlegat the Z-th mixture- of-experts (MoE) layer. The first term in Equation (6) may encourage uniformity in the mean (or average) of the combination of weights (e.g., a uniform mean expert weight vector < >(m)) across different tasks while the second term may encourage expert specialization by promoting a high variance (or sparsity) among the weights assigned to the N experts for a given task. In other words, the segmentation model 113, in particular the gating network in this case, may be adjusted to encourage uniform expert selection while avoid favoring any particular expert across different tasks. However, for any individual task, the segmentation model 113, in particular the gating network < >, may be adjusted to favor the output of some but not all of the experts ElrE2, ... , EN.

[0103] At 606, the segmentation controller 110 may adjust the segmentation model to reduce a third loss associated with a difference in a first output of the segmentation model operating a first modality configuration and a second output of the segmentation model operating on a second modality configuration having fewer modalities than the first modality configuration. In some example embodiments, the performance of the segmentation model 113 may be further improved by training the segmentation model 113 to reduce (or minimize) a self-distillation loss. In this context, the self-distillation loss may refer to a difference between the outputs of the segmentation model 113 operating on two or more modality configurations having different quantities of modalities. For example, in instances where the segmentation model 113 is trained to operate on the first modality configuration 150a and the second modality configuration 150b, reducing (or minimizing) the self-distillation loss may encourage the segmentation model 113 to reduce (or minimize) the difference between a first output of the segmentation model 113 operating on thefirst modality configuration 150a and a second output of the segmentation model 113 operating on the second modality configuration 150b, which contains fewer modalities than the first modality configuration 150a. In particular, in some cases, the reduction (or minimization) of the selfdistillation loss may encourage the segmentation model 113 to generate the second output to be more similar to the first output. This may be tantamount to further reducing (or minimizing) reliance on the modalities that are missing from the second modality configuration 150b such that the segmentation model 113 is able to generate the same (or similar) outputs with or without these modalities.

[0104] To further illustrate, when the segmentation model 113 is being trained to operate on the modality-dropped images x, this training may be guided by a supervisory signal associated with the outputs of the segmentation model 113 operating on images x, which includes more modalities than the modality-dropped images x. In some cases, the aforementioned selfdistillation loss may correspond to a difference between the outputs of the segmentation model 113 operating on the images x and the outputs of the segmentation model 113 operating on the modality-dropped images x. Accordingly, given the batch of images {(xh,yb)]=1and their modality-dropped counterpartsEquation (7) below defines the selfdistillation loss Lsd.wherein sg denotes the stop-gradient operator and the loss LTsignifies that the loss is computed on softened logits at a user defined temperature T.

[0105] In some cases, it should be appreciated that the training of the segmentation model 113 may include reducing (or minimizing) an overall loss function that includes the aforementioned dropped modality loss £md, regularization lossreg, and self-distillation loss Lsd.Equation (8) below defines the overall loss L as a function of the dropped modality loss Tmd, the regularization lossreg, and the self-distillation loss Lsd.wherein w(t) is a sigmoid-like time-dependent ramp-up factor that may, in some cases, assume a maximum value (e.g., 1) after completion of a certain quantity (e.g., one-third) of the total number of training epochs.

[0106] As noted, the segmentation model 113 may include one or more one or more mixture-of-experts layers, such as the mixture-of-experts layer 350 shown in FIGS. 3B and 4. The overall loss function (e.g., Equation (8)) may be reduced (or minimized) such that the gating network < > of each mixture-of-experts (MoE) layer generates a different combination of weights for each task performed by the segmentation model 113. For example, in some cases, the segmentation model 113 may be trained to perform the first task of operating on the first modality configuration 150a, the second task of operating on the second modality configuration 150b, and the third task of operating on the third modality configuration 150c. Each mixture-of-experts (MoE) layer in the segmentation network 113 may include a first expert corresponding to a first subset of parameters, a second expert corresponding to a second subset of parameters, and, in some cases, a third expert corresponding to a third subset of parameters. In some cases, each expert (or subset of parameters) may be trained to specialize in one or more tasks. Thus, depending on the task being performed by the segmentation model 113, the gating network < > of each mixture-of- experts (MoE) layer may generate a different combination of weights to combine the outputs of the first expert, the second expert, and, in some cases, the third expert. For instance, in some cases, the gating network of each mixture-of-experts (MoE) layer may generate a first combination of weights for the first task, a second combination of weights for the second task, and a thirdcombination of weights for the third task. When applied to the outputs of the individual experts, the different combination of weights generated by the gating network (p of each mixture-of-experts (MoE) layer may vary the extent to which the output of each expert in the layer contributes to the overall output of the segmentation model 113. Since each expert (or subset of parameters) may be trained to specialize in one or more tasks, the outputs of those experts (or subsets of parameters) specializing in a particular task may be weighted to contribute more to the overall output of the segmentation model 113 when the segmentation model 113 is performing that task. Contrastingly, the extent to which the outputs of the experts (or subset of parameters) not specializing in the task contributes to the overall output of the segmentation model 113 may be reduced (or minimized). Combining the outputs of experts in this manner may improve the performance of the segmentation model 113 across the first task, the second task, and the third task by at least reducing (or minimizing) the interference between different experts (or different subsets of parameters).

[0107] FIG. 7 depicts a flowchart illustrating an example of a process 700 for a selfdistillation process for training a segmentation model having one or more mixture-of-experts (MoD) layers to segment medical scans having different modality configurations, in accordance with some example embodiments. Referring to FIGS. 1-2, 3A-B, and 4-7, the process 700 may be performed by the segmentation controller 110 to train, for example, the segmentation model 113 to segment one or more medical images. In some cases, the process 700 may implement at least a portion of the operation 504 of the process 500 shown in FIG. 5 such as, for example, operation 606 of the process 600. In some cases, the process 700 may be performed to train the segmentation model 113 through self-distillation. Distillation is a technique for reducing the computational burden associated with a larger computation model by transferring its “knowledge” of the larger computation model, which are encoded in the parameters (e.g., weights, biases, and / or the like) ofthe model, to a smaller computation model. Nevertheless, conventional distillation methods require the larger computation model to be pretrained, which is computationally onerous task given the size of the model. Unlike conventional distillation methods that transfer knowledge from a larger, typically less computationally efficient “teacher” model to a smaller, typically more computationally efficient “student” model, various implementations of self-distillation disclosed herein includes a transfer of “knowledge” within a single model, such as the segmentation model 113, through multiple passes of each training image. As such, various implementations of selfdistillation disclosed herein provide the distinct advantage of obviating the pretraining of a larger, less computationally efficient “teacher” model. Instead, as described in more details below, in some cases, the output of the segmentation model 113 operating on a training image with its original modality configuration may be used to guide the subsequent training of the segmentation model 113 operating on the training sample with one or more fewer modalities.

[0108] At 802, the segmentation controller 110 may apply a segmentation model to segment a training image having a plurality of modalities. In some example embodiments, the segmentation controller 110 may apply the segmentation model 113 to segment a training image having multiple modalities. In the example shown in FIG. 2A, the segmentation model 113 may be applied to the segment the first sample 210a, which has the same first modality configuration 150a as the training image 125a. That is, in the example shown in FIG. 2A, the segmentation model 113 may be applied to segment the first sample 210a, which has the same three modalities Ml (e.g., a Ti-weighted scan), M2 (e.g., a T2-weighted scan), and M3 (e.g., a FLAIR scan) as the training image 125a. As such, when segmenting the first sample 210a, which may include identifying one or more regions of interest (e.g., tissue, organ, lesion, tumor, and / or the like) present in the training image 125a, the segmentation model 113 may be able to leveragecomplimentary information from all three modalities Ml (e.g., a Ti-weighted scan), M2 (e.g., aT2-weighted scan), and M3 (e.g., a FLAIR scan). The resulting output of the segmentation model 113, which may include a segmentation map localizing the one or more regions of interest (ROI) present in the training image 125a, may be considered more accurate than the output of the segmentation model 113 operating on an image with fewer modalities. Accordingly, in the context of self-distillation, the output of the segmentation model 113 operating on the first sample 210a, which includes all three modalities Ml (e.g., a Ti-weighted scan), M2 (e g., a T2-weighted scan), and M3 (e.g., a FLAIR scan) of the training image 125a, may constitute a so-called “teacher” signal for guiding the training of the segmentation model 113 to adapt to the absence of one or more modalities.

[0109] At 804, the segmentation controller 110 may apply the segmentation model to segment the training image absent at least one modality of the plurality of modalities. In some example embodiments, the segmentation controller 110 may apply the segmentation model 113 to segment the training image absent at least one of the original modalities present therein. In the example shown in FIG. 2A, the segmentation model 113 may be applied to the segment the second sample 210b, which is missing the second modality M2 (e.g., a T2-weighted scan) present in the original training image 125a. Alternatively and / or additionally, the segmentation model 113 may be applied to segment the third sample 210c, which is missing the first modality (e.g., a Ti- weighted scan) Ml as well as the third modality M3 (e.g., a FLAIR scan). In some cases, the segmentation model 113 may be trained to adapt to the absence of one or more of the modalities present in the training image 125a. As described in more details below, this adaptation may include generating an accurate output, such as a segmentation map localizing the one or more regions of interest (ROI) present in the training image 125a, despite the absence of one or more modalities.For example, in the case shown in FIG. 2A, the segmentation model 113 may be trained to generate, for the second sample 210b (or the third sample 210c), an output (e.g., segmentation map) that is as similar as possible to the output (e.g., segmentation map) generated for the first sample 210a. In some cases, training the segmentation model 113 to adapt to the absence of one or more modalities, such as the absence of the second modality M2 (e.g., a T2-weighted scan) in the second sample 210b or the first modality Ml (e.g., a Ti-weighted scan) and the third modality M3 (e.g., a FLAIR scan) in the third sample 210c, may be tantamount to discoursing the segmentation model 113 from relying on the complimentary information present in the missing modalities.

[0110] At 706, the segmentation controller 110 may adjust the segmentation model to reduce a difference between a first output of the segmentation model operating on the training image having the plurality of modalities and the second output of the segmentation model operating on the training image absent at least one modality of the plurality of modalities. In some example embodiments, the segmentation controller 110 may adjust the segmentation model 113, such as one or more parameters (e.g., weights, biases, and / or the like) of the segmentation model 113, to reduce (or minimize) self-distillation loss. As used herein, the term “self-distillation loss” may refer to the difference between the output (e.g., segmentation map) of the segmentation model 113 operating on a training sample with its original modality configuration and the output of the segmentation model 113 operating on the training sample absent one or more of the modalities present in its original modality configuration. In some cases, the self-distillation loss described herein may be defined as Equation (7) above. According to Equation (7), the self-distillation loss may be quantified for a batch of images {(b, y / ,)}=1and the corresponding modality-dropped counterparts {(xb(xb, mb, yb|)}=1, which is missing one or more of the modalities present in themodality configuration of the original images. In some cases, this self-distillation loss may quantify the ability of the segmentation model 113 to adapt to the absence of one or more modalities. In other words, when trained to reduce (or minimize) the self-distillation loss, the segmentation model 113 may be able to generate an accurate output (e.g., segmentation map) regardless of the modalities that are present (or absent) in the input image. For instance, by reducing (or minimizing) the self-distillation loss, the output (e.g., segmentation map) of the segmentation model 113 in the absence of one or more of the first modality Ml (e.g., a Ti-weighted scan), the second modality M2 (e.g., a T2-weighted scan), and the third modality M3 (e.g., a FLAIR scan) should be as similar as possible to the output of the segmentation model 113 when all three modalities are present.

[0111] FIG. 8 depicts a flowchart illustrating an example of a process 800 for taskconditional segmentation of medical images with a segmentation model having a mixture-of- experts, in accordance with some example embodiments. Referring to FIGS. 1-2, 3A-B, 5, and 8, the process 800 may be performed by the segmentation controller 110 applying the segmentation model 113 to segment one or more medical images which, in some cases, may include one or more scans of different modalities. In some cases, the segmentation model 113 may be trained to adapt to multiple different modality configurations, including modality configurations with fewer or missing modalities, which obviates the computation burden associated with training, validating, and testing multiple dedicated segmentation models capable of operating on a single modality configuration. Moreover, as described in more details below, the output of the segmentation model 113 may be determined by at least combining the outputs of the N quantity of experts Er, E2, ... , ENpopulating each of the one or more mixture-of-experts (MoE) layers in the segmentation model113. In some cases, instead of the output of the segmentation model 113 being an indiscriminatecombination of the outputs from various parameters (e.g., weights, biases, and / or the like) of the segmentation model 113, which have been adjusted to accommodate multiple tasks, the outputs of the N quantity of experts ElrE2, — , ENmay be combined to include a greater contribution from those experts trained to specialize in the modality configuration of the medical image being segmented by the segmentation model 113 than the experts not trained to specialize in that modality configuration. As such, it should be appreciated that the segmentation controller 110 performing the process 800 and applying the segmentation model 113 having one or more mixture- of-experts (MoE) layers may be advantageous at least because doing so not only reduce the computation burden associated with training and applying conventional segmentation models that have been trained to operate specific modality configurations but achieves comparable or even better segmentation performance.

[0112] At 702, the segmentation controller 110 may generate, based at least on an input image, an input tensor having a plurality of channels corresponding to a plurality of modalities. In some example embodiments, the segmentation controller 110 may generate, based at least on the input image 125b, an input tensor for ingestion by the segmentation model 113. For example, in some cases, the segmentation controller 110 may generate, based at least on the input image 125b, the input tensor 250, which includes a one or more fixed channels corresponding to one or more modalities. In instances where the input image 125b is missing scans of one or more of the modalities present in the input tensor 250, the segmentation controller 110 may generate the input tensor 250 by at least setting the one or more corresponding channels (e.g., the intensity values of the constituent pixels (or voxels)) to zero. For instance, in instances where the input image 125b includes a scan of the second modality M2 (e.g., a T2-weighted scan) and one of the third modalityM3 (e.g., a FLAIR scan) but not a scan of the first modality Ml (e.g., a Ti-weighted scan), the firstchannel corresponding to the first modality Ml (e.g., for the Ti-weighted scan) in the input tensor250 may be set to zero whereas the channels corresponding to the second modality M2 (e.g., for the T2-weighted scan) and the third modality M3 (e.g., for the FLAIR scan) may include the intensity values of the pixels (or voxels) in the corresponding scans.

[0113] At 704, the segmentation controller 110 may segment the input image by at least applying, to the input tensor, a segmentation model trained to operate on the plurality of modality configurations including by generating, based at least on a modality configuration of the input image, a segmentation map localizing one or more regions of interest (ROI) present in the input image. In some example embodiments, the segmentation controller 1 10 may segment the input image 125b by at least applying the segmentation model 113 to the input tensor 250 representative of the input image 125b. As noted, in some cases, the segmentation model 113 may be trained to adapt to multiple modality configurations, including those having one or more missing or absent modalities. For example, in some cases, the segmentation model 113 may include one or more mixture-of-experts (MoE) layers, each of which including a gating network <p and an N quantity of experts £1E2, ... , EN. In some cases, each of the N experts in a mixture-of-experts (MoE) layer may correspond to a subset of parameters (e.g., weights, biases, and / or the like) in the segmentation model 113 trained to specialize in one or more modality configurations. Moreover, the gating network (p in a mixture-of-experts (MoE) layer may be trained to combine, based at least on the modality code m corresponding to the modality configuration of the input image 125b, the outputs of the N quantity of experts Ei, E2- —>ENin that mixture-of-experts (MoE) layer. It should be appreciated that the output of the segmentation model 113, which may include a segmentation map localizing one or more regions of interest (e.g., tissue, organ, tumor, lesion, and / or the like) in theinput image 125b, may be generated based at least on the outputs of the one or more mixture-of- experts (MoE) layers.

[0114] To further illustrate, in some cases, every one of the N quantity of experts E1, E2, ... , ENin a mixture-of-experts (MoE) layer may operate on the input tensor 250 representative of the input image 125b to generate a plurality of different outputs for the input image 125b. The gating network may generate, based at least on the modality code m of the input image 125b, a combination of weights (e.g., an -dimensional weight vector (m)). Applying the combination of weights (e g., the IV -dimensional weight vector < >(m)) to the plurality of different outputs generated by the N quantity of experts E1(£2< ■may combine those outputs. For example, in some cases, the output of a mixture-of-experts (MoE) layer having the N quantity of experts E1(E2, ..., ENmay be determined based at least on a weighted summation of the individual outputs from each one of the N quantity of experts. As such, in some cases, the gating network (f> may generate the combination of weights (e g., the A-dimensional vector < >(m)) to include weights whose values modulate the extent to which the output from each of the N quantity of experts EltE2, ■■■ , ENcontributes to the output (e.g., the segmentation map) of the segmentation model 113. In some cases, the values of two or more weights may differ in order to reflect a difference in the extent to which two or more corresponding experts are trained to specialize in the modality configuration m of the input image 125b. For instance, in some cases, the combination of weights (e.g., the IV-dimensional vector < >(m)) may include higher weights for the outputs of one or more experts with a higher degree of specialty in the modality configuration m of the input image 125b than for the outputs of those experts with a lower degree of specialty in the modality configuration m of the input image 125b. Accordingly, applying the N- dimensional vector < >(m) may generate the output of the segmentation model 113 to include ahigher contribution from the outputs of some experts in the mixture-of-experts (MoE) layer than from the outputs of other experts in the mixture-of-experts (MoE) layer. Doing so may reduce (or minimize) interference between experts (or subsets of parameters) specializing in different modality configurations, thus improving the performance of the segmentation model 113 operating on multiple different modality configurations.

[0115] FIG. 9 depicts a block diagram illustrating a computing system 900 consistent with implementations of the current subject matter. Referring to FIGS. 1-9, the computing system 900 can be used to implement at least a portion of the segmentation controller 110, the one or more imaging devices 120, the client device 130, and / or any components therein.

[0116] As shown in FIG. 9, the computing system 900 can include a processor 910, a memory 920, a storage device 930, and input / output devices 940. The processor 910, the memory 920, the storage device 930, and the input / output devices 940 can be interconnected via a system bus 950. The computing system 900 may additionally or alternatively include a graphic processing unit (GPU), such as for image processing, and / or an associated memory for the GPU. The GPU and / or the associated memory for the GPU may be interconnected via the system bus 950 with the processor 910, the memory 920, the storage device 930, and the input / output devices 940. The memory associated with the GPU may store one or more images described herein, and the GPU may process one or more of the images described herein. The GPU may be coupled to and / or form a part of the processor 910. The processor 910 is capable of processing instructions for execution within the computing system 900. Such executed instructions can implement one or more components of, for example, the segmentation controller 110, the one or more imaging devices120, the client device 130, and / or the like. In some implementations of the current subject matter, the processor 910 can be a single-threaded processor. Alternately, the processor 910 can be a multi-threaded processor. The processor 910 is capable of processing instructions stored in the memory 920 and / or on the storage device 930 to display graphical information for a user interface provided via the input / output device 940.

[0117] The memory 920 is a computer readable medium such as volatile or non-volatile that stores information within the computing system 900. The memory 920 can store data structures representing configuration object databases, for example. The storage device 930 is capable of providing persistent storage for the computing system 900. The storage device 930 can be a floppy disk device, a hard disk device, an optical disk device, or a tape device, or other suitable persistent storage means. The input / output device 940 provides input / output operations for the computing system 900. In some implementations of the current subject matter, the input / output device 940 includes a keyboard and / or pointing device. In various implementations, the input / output device 940 includes a display unit for displaying graphical user interfaces.

[0118] According to some implementations of the current subject matter, the input / output device 940 can provide input / output operations for a network device. For example, the input / output device 940 can include Ethernet ports or other networking ports to communicate with one or more wired and / or wireless networks (e.g., a local area network (LAN), a wide area network (WAN), the Internet).

[0119] In some implementations of the current subject matter, the computing system 900 can be used to execute various interactive computer software applications that can be used for organization, analysis and / or storage of data in various (e.g., tabular) format (e.g., Microsoft Excel®, and / or any other type of software). Alternatively, the computing system 900 can be used to execute any type of software applications. These applications can be used to perform various functionalities, e.g., planning functionalities (e.g., generating, managing, editing of spreadsheetdocuments, word processing documents, and / or any other objects, etc.), computing functionalities, communications functionalities, etc. The applications can include various add-in functionalities or can be standalone computing products and / or functionalities. Upon activation within the applications, the functionalities can be used to generate the user interface provided via the input / output device 940. The user interface can be generated and presented to a user by the computing system 900 (e.g., on a computer screen monitor, etc.).

[0120] One or more aspects or features of the subject matter described herein can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs, field programmable gate arrays (FPGAs) computer hardware, firmware, software, and / or combinations thereof. These various aspects or features can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device. The programmable system or computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

[0121] These computer programs, which can also be referred to as programs, software, software applications, applications, components, or code, include machine instructions for a programmable processor, and can be implemented in a high-level procedural and / or object- oriented programming language, and / or in assembly / machine language. As used herein, the term “machine-readable medium” refers to any computer program product, apparatus and / or device,such as for example magnetic discs, optical disks, memory, and Programmable Logic Devices (PLDs), used to provide machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and / or data to a programmable processor. The machine-readable medium can store such machine instructions non-transitorily, such as for example as would a non-transient solid-state memory or a magnetic hard drive or any equivalent storage medium. The machine-readable medium can alternatively or additionally store such machine instructions in a transient manner, such as for example, as would a processor cache or other random access memory associated with one or more physical processor cores.

[0122] To provide for interaction with a user, one or more aspects or features of the subject matter described herein can be implemented on a computer having a display device, such as for example a cathode ray tube (CRT), a liquid crystal display (LCD), a light emitting diode (LED) monitor, or an organic light emitting diode (OLED) monitor for displaying information to the user and a keyboard and a pointing device, such as for example a mouse or a trackball, by which the user may provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well. For example, feedback provided to the user can be any form of sensory feedback, such as for example visual feedback, auditory feedback, or tactile feedback; and input from the user may be received in any form, including acoustic, speech, or tactile input. Other possible input devices include touch screens or other touch-sensitive devices such as single or multi-point resistive or capacitive track pads, voice recognition hardware and software, optical scanners, optical pointers, digital image capture devices and associated interpretation software, and the like.

[0123] The subject matter described herein can be embodied in systems, apparatus, methods, and / or articles depending on the desired configuration. The implementations set forth in the foregoing description do not represent all implementations consistent with the subject matter described herein. Instead, they are merely some examples consistent with aspects related to the described subject matter. Although a few variations have been described in detail above, other modifications or additions are possible. In particular, further features and / or variations can be provided in addition to those set forth herein. For example, the implementations described above can be directed to various combinations and subcombinations of the disclosed features and / or combinations and subcombinations of several further features disclosed above. In addition, the logic flows depicted in the accompanying figures and / or described herein do not necessarily require the particular order shown, or sequential order, to achieve desirable results. For example, the logic flows may include different and / or additional operations than shown without departing from the scope of the present disclosure. One or more operations of the logic flows may be repeated and / or omitted without departing from the scope of the present disclosure. Other implementations may be within the scope of the following claims.

Claims

CLAIMSWhat is claimed is:

1. A computer-implemented method, comprising: generating, based at least on a training image having a plurality of modalities, a first training sample having a first modality configuration and a second training sample having a second modality configuration, the first modality configuration having a different combination of modalities than the second modality configuration; training, based at least on the first training sample and the second training sample, a segmentation model to perform a first task of operating on the first modality configuration and a second task of operating on the second modality configuration, the segmentation model being trained to generate an output segmentation map based on a combination that includes a first output from a first subset of parameters in the segmentation model and a second output from a second subset of parameters in the segmentation model, the training of the segmentation model includes adjusting one or more parameters of the segmentation model to determine, for each of the first task and the second task, a different combination of weights for combining the first output of the first subset of parameters and the second output of the second subset of parameters; and segmenting an input image by at least applying the segmentation model to generate, based at least on a modality configuration of the input image, a segmentation map localizing one or more regions of interest (ROI) present in the input image.

2. The method of claim 1, wherein the segmentation model includes a gating network that determines, based at least on the modality configuration of the input image, the combination of weights for combining the first output of the first subset of parameters and thesecond output of the second subset of parameters.

3. The method of claim 2, wherein the segmentation model generates the segmentation map by at least determining a weighted summation in which a first weight from the combination of weights is applied to the first output of the first subset of parameters and a second weight from the combination of weights is applied to the second output of the second subset of parameters.

4. The method of any of claims 2 to 3, wherein the gating network, the first subset of parameters, and the second subset of parameters form a mixture-of-expert (MoE) layer in the segmentation model.

5. The method of claim 4, wherein the segmentation model includes one or more additional mixture-of-expert (MoE) layers, and wherein each additional mixture-of-expert (MoE) layer includes a different gating network.

6. The method of any of claims 1 to 5, wherein each of the first training sample and the second training sample are generated by least dropping one or more different modalities of the plurality of modalities present in the training image.

7. The method of any of claims 1 to 6, further comprising: generating, based at least on a training image, the first training sample to include a first modality but not a second modality present in a third modality configuration of the training image.

8. The method of claim 7, wherein generating the first training sample includes generating an input tensor having a first channel corresponding to the first modality and a second channel corresponding to the second modality, and wherein the first channel of the first input tensor is set to zero in order to indicate an absence of the first modality.

9. The method of any of claims 7 to 8, wherein the training the segmentation model to perform the first task of operating on the first modality configuration includes training the segmentation model to adapt to an absence of the first modality when segmenting the first training sample.

10. The method of any of claims 7 to 9, further comprising: generating, based at least on the training image, the second training sample to include the second modality but not the first modality present in the third modality configuration of the training image.

11. The method of any of claims 7 to 10, further comprising: generating, based at least on the training image, a third training sample having the third modality configuration; and training, based at least on the third training sample, the segmentation model to perform a third task of operating on the third modality configuration, the training of the segmentation model further includes adjusting the one or more parameters of the segmentation model to determine, for the third task, a combination of weights for combining the first output of the first subset of parameters and the second output of the second subset of parameters.

12. The method of claim 11, wherein the combination of weights is further determined to combine the first output of the first subset of parameters, the second output of the second subset of parameters, and / or a third output of a third subset of parameters.

13. The method of any of claims 7 to 12, wherein the training image is a magnetic resonance imaging (MRI) scan, and wherein the first modality and the second modality comprise a different one of a T1 -weighted scan, a T2-weighted scan, a proton density (PD) weighted scan, and a fluid attenuated inversion recovery (FLAIR) scan.

14. The method of any of claims 7 to 13, wherein the training image is a computed tomography (CT) scan, and wherein the first modality and the second modality comprise a different one of a sequential CT scan, a spiral CT scan, an electron beam tomography scan, a dual energy CT scan, and a CT perfusion scan.

15. The method of any of claims 7 to 14, wherein the training image is a positron emission tomography (PET) scan, and wherein the first modality and the second modality comprise scans acquired using different radiotracers.

16. The method of any of claims 7 to 15, wherein the training image is a multimodal medical image, and wherein the first modality and the second modality comprise a different one of a magnetic resonance imaging (MRI) scan, a positron emission tomography (PET), a computed tomography (CT) scan, an X-ray, a microscopy image, an optical coherence tomography (OCT) scan, and an ultrasound.

17. The method of any of claims 1 to 16, wherein the segmentation model is a machine learning model in which the first subset of parameters and the second subset of parameters form a layer of the machine learning model.

18. The method of any of claims 1 to 17, further comprising: generating, based at least on the input image, an input tensor having a plurality of channels corresponding to a plurality of modalities, the generating of the input tensor includes setting, to zero, all intensity values of a plurality of pixels in a channel associated with a modality absent from the first modality configuration of the input image; and segmenting the input image by at least applying the segmentation model to operate on the input tensor.

19. The method of claim 18, wherein the input tensor includes a k quantity of fixedchannels for a k quantity of modalities.

20. The method of claim 19, further comprising: generating, for each of a 2k— 1 quantity of possible modality configurations arising from the k quantity of modalities, at least one training sample for inclusion in a training set; and training, based at least on the training set, the segmentation model to adapt to the 2k— 1 quantity of possible modality configurations.

21. The method of any of claims 1 to 20, wherein the training of the segmentation model includes adjusting one or more parameters of the segmentation model to reduce a difference between the output segmentation map generated by the segmentation model for each training sample and a corresponding ground-truth segmentation map.

22. The method of any of claims 1 to 21, wherein the training of the segmentation model includes adjusting the one or more parameters of the segmentation model to increase a uniformity of an average combination of weights across the first task and the second task.

23. The method of any of claims 1 to 22, wherein the training of the segmentation model includes adjusting one or more parameters of the segmentation model to increase a variance or sparsity between individual weights in each combination of weights for combining the first output of the first subset of parameters and the second output of the second subset of parameters.

24. The method of any of claims 1 to 23, wherein the training of the segmentation model includes adjusting one or more parameters of the segmentation model to increase a similarity between a first output segmentation map of the segmentation model operating on a modality configuration without one or more modalities to a second output segmentation map of the segmentation model operating on another modality configuration having the one or moremodalities.

25. The method of any of claims 1 to 24, wherein the segmentation model generates the segmentation map by at least assigning, to each pixel in the segmentation map, a value indicating a probability of the pixel depicting at least a portion of a region of interest (ROI) present in the input image.

26. The method of claim 25, wherein the region of interest (ROI) includes one or more of a tissue, an organ, a lesion, and / or a tumor.

27. The method of any of claims 25 to 26, wherein the value is a binary value or a value from a continuous range of values.

28. The method of any of claims 1 to 27, wherein the training image and the input image are two-dimensional images or three-dimensional volumes comprising a series of two- dimensional images.

29. The method of any of claims 1 to 28, wherein the training of the segmentation model includes: applying the segmentation model to segment a training image having a plurality of modalities; applying the segmentation model to segment the training image absent at least one modality of the plurality of image; and adjusting one or more parameters of the segmentation model to reduce a difference between a first output of the segmentation model operating on the training image having the plurality of modalities and a second output of the segmentation model operating on the training image absent the at least one modality of the plurality of modalities.

30. A system, comprising:at least one data processor; and at least one memory storing instructions, which when executed by the at least one data processor, result in operations comprising the method of any of claims 1 to 29.

31. A non-transitory computer readable medium storing instructions, which when executed by at least one data processor, result in operations comprising the method of any of claims 1 to 29.

32. A computer-implemented method, comprising: generating a first training sample having a first modality configuration by at least dropping a first modality from a modality configuration of a training image having a plurality of modalities; generating a second training sample having a second modality configuration by at least dropping a second modality from the modality configuration of the training image; training, based at least on the first training sample and the second training sample, a segmentation model, the training includes adjusting one or more parameters of the segmentation model to perform a first task of operating on the first modality configuration and a second task of operating on the second modality configuration, the segmentation model being trained to generate an output segmentation map based on a combination that includes a first output from a first subset of parameters in the segmentation model and a second output from a second subset of parameters in the segmentation model, the training of the segmentation model includes adjusting at least one gating network in the segmentation model to determine, for each of the first task and the second task, a different combination of weights for combining the first output of the first subset of parameters and the second output of the second subset of parameters; andsegmenting an input image by at least applying the segmentation model to generate, based at least a modality configuration of the input image, a segmentation map localizing one or more regions of interest (ROI) present in the input image.

33. A system, comprising: at least one data processor; and at least one memory storing instructions, which when executed by the at least one data processor, result in operations comprising: generating a first training sample having a first modality configuration by at least dropping a first modality from a modality configuration of a training image having a plurality of modalities; generating a second training sample having a second modality configuration by at least dropping a second modality from the modality configuration of the training image; training, based at least on the first training sample and the second training sample, a segmentation model, the training includes adjusting one or more parameters of the segmentation model to perform a first task of operating on the first modality configuration and a second task of operating on the second modality configuration, the segmentation model being trained to generate an output segmentation map based on a combination that includes a first output from a first subset of parameters in the segmentation model and a second output from a second subset of parameters in the segmentation model, the training of the segmentation model includes adjusting at least one gating network in the segmentation model to determine, for each of the first task and the second task, a different combination of weights for combining the first output of the first subset of parameters and the second output of the second subset of parameters; andsegmenting an input image by at least applying the segmentation model to generate, based at least a modality configuration of the input image, a segmentation map localizing one or more regions of interest (RO I) present in the input image.

34. A non-transitory computer readable medium storing instructions, which when executed by at least one data processor, result in operations comprising: generating a first training sample having a first modality configuration by at least dropping a first modality from a modality configuration of a training image having a plurality of modalities; generating a second training sample having a second modality configuration by at least dropping a second modality from the modality configuration of the training image; training, based at least on the first training sample and the second training sample, a segmentation model, the training includes adjusting one or more parameters of the segmentation model to perform a first task of operating on the first modality configuration and a second task of operating on the second modality configuration, the segmentation model being trained to generate an output segmentation map based on a combination that includes a first output from a first subset of parameters in the segmentation model and a second output from a second subset of parameters in the segmentation model, the training of the segmentation model includes adjusting at least one gating network in the segmentation model to determine, for each of the first task and the second task, a different combination of weights for combining the first output of the first subset of parameters and the second output of the second subset of parameters; and segmenting an input image by at least applying the segmentation model to generate, based at least a modality configuration of the input image, a segmentation map localizing one or more regions of interest (ROI) present in the input image.

Images