Superpixel generation and use

Abstract

A method 700 for interpolation of one or more intermediate images from two or more images comprises interpolating 707 one or more intermediate images from the two or more images based on one or more inconsistent flow vectors corresponding to the two or more images. The intermediate images may be interpolated based on superpixels generated for the two or more images. The inconsistent motion vectors may be determined 701 based on comparison of forward optical flow data from a first image to a second image with backward optical flow data from the second image to the first. The plurality of superpixels may be identified 703 starting from coordinates of candidate pixels associated with inconsistent flow vectors using a gradient map and intensity information from a first image of the two or more images. A plurality of superpixels may be used 705 to fill in inconsistent flow vectors. A superpixel generation processor and system which uses inconsistent flow vectors is also disclosed.

Description

[0594] In at least one embodiment, software 3618 may include any number of different containers, where each container may execute an instantiation of an application. In at least one embodiment, each application may perform one or more processing tasks in an advanced processing and inferencing pipeline (e.g., inferencing, object detection, feature detection, segmentation, image enhancement, calibration, etc.). In at least one embodiment, for each type of computing device there may be any number of containers that may perform a data processing task with respect to imaging data 3608 (or other data types, such as those described herein). In at least one embodiment, an advanced processing and inferencing pipeline may be defined based on selections of different containers that are desired or required for processing imaging data 3608, in addition to containers that receive and configure imaging data for use by each container and / or for use by facility 3602 after processing through a pipeline (e.g., to convert outputs back to a usable data type for storage and display at facility 3602). In at least one embodiment, a combination of containers within software 3618 (e.g., that make up a pipeline) may be referred to as a virtual instrument (as described in more detail herein), and a virtual instrument may leverage services 3620 and hardware 3622 to execute some or all processing tasks of applications instantiated in containers.

[0595] . . In at least one embodiment, data may undergo pre-processing as part of data processing pipeline to prepare data for processing by one or more applications. In at least one embodiment, post-processing may be performed on an output of one or more inferencing tasks or other processing tasks of a pipeline to prepare an output data for a next application and / or to prepare output data for transmission and / or use by a user (e.g., as a response to an inference request). In at least one embodiment, inferencing tasks may be performed by one or more machine learning models, such as trained or deployed neural networks, which may include output models 3616 of training system 3604.

[0596] In at least one embodiment, tasks of data processing pipeline may be encapsulated in a container(s) that each represent a discrete, fully functional instantiation of an application and virtualized computing environment that is able to reference machine learning models. In at least one embodiment, containers or applications may be published into a private (e.g., limited access) area of a container registry (described in more detail herein), and trained or deployed models may be stored in model registry 3624 and associated with one or more applications. In at least one embodiment, images of applications (e.g., container images) may be available in a container registry, and once selected by a user from a container registry for deployment in a pipeline, an image may be used to generate a container for an instantiation of an application for use by a user’s system.

[0597] In at least one embodiment, developers may develop, publish, and store applications (e.g., as containers) for performing processing and / or inferencing on supplied data. In at least one embodiment, development, publishing, and / or storing may be performed using a software development kit (SDK) associated with a system (e.g., to ensure that an application and / or container developed is compliant with or compatible with a system). In at least one embodiment, an application that is developed may be tested locally (e.g., at a first facility, on data from a first facility) with an SDK which may support at least some of services 3620 as a system (e.g., system 3700 of FIG. 37). . In at least one embodiment, once validated by system 3700 (e.g., for accuracy, etc.), an application may be available in a container registry for selection and / or implementation by a user (e.g., a hospital, clinic, lab, healthcare provider, etc.) to perform one or more processing tasks with respect to data at a facility (e.g., a second facility) of a user.

[0598] In at least one embodiment, developers may then share applications or containers through a network for access and use by users of a system (e.g., system 3700 of FIG. 37). In at least one embodiment, completed and validated applications or containers may be stored in a container registry and associated machine learning models may be stored in model registry 3624. In at least one embodiment, a requesting entity- who provides an inference or image processing request - may browse a container registry and / or model registry 3624 for an application, container, dataset, machine learning model, etc., select a desired combination of elements for inclusion in data processing pipeline, and submit an processing request. In at least one embodiment, a request may include input data that is necessary to perform a request, and / or may include a selection of application(s) and / or machine learning models to be executed in processing a request. In at least one embodiment, a request may then be passed to one or more components of deployment system 3606 (e.g., a cloud) to perform processing of data processing pipeline. In at least one embodiment, processing by deployment system 3606 may include referencing selected elements (e.g., applications, containers, models, etc.) from a container registry and / or model registry 3624. In at least one embodiment, once results are generated by a pipeline, results may be returned to a user for reference (e.g., for viewing in a viewing application suite executing on a local, on-premises workstation or terminal).

[0599] In at least one embodiment, to aid in processing or execution of applications or containers in pipelines, services 3620 may be leveraged. In at least one embodiment, services 3620 may include compute services, artificial intelligence (AI) services, visualization services, and / or other service types. In at least one embodiment, services 3620 may provide functionality that is common to one or more applications in software 3618, so functionality may be abstracted to a service that may be called upon or leveraged by applications. In at least one embodiment, functionality provided by services 3620 may run dynamically and more efficiently, while also scaling well by allowing applications to process data in parallel (e.g., using a parallel computing platform 3730 (FIG. 37)). In at least one embodiment, rather than each application that shares a same functionality offered by a service 3620 being required to have a respective instance of service 3620, service 3620 may be shared between and among various applications. In at least one embodiment, services may include an inference server or engine that may be used for executing detection or segmentation tasks, as non-limiting examples. In at least one embodiment, a model training service may be included that may provide machine learning model training and / or retraining capabilities. . .

[0600] In at least one embodiment, where a service 3620 includes an AI service (e.g., an inference service), one or more machine learning models associated with an application for anomaly detection (e.g., tumors, growth abnormalities, scarring, etc.) may be executed by calling upon (e.g., as an API call) an inference service (e.g., an inference server) to execute machine learning model(s), or processing thereof, as part of application execution. In at least one embodiment, where another application includes one or more machine learning models for segmentation tasks, an application may call upon an inference service to execute machine learning models for performing one or more of processing operations associated with segmentation tasks. In at least one embodiment, software 3618 implementing advanced processing and inferencing pipeline may be streamlined because each application may call upon a same inference service to perform one or more inferencing tasks. [060 i ] In at least one embodiment, hardware 3622 may include GPUs, CPUs, graphics cards, an Al / deep learning system (e.g., an AI supercomputer, such as NVIDIA’s DGX supercomputer system), a cloud platform, or a combination thereof. In at least one embodiment, different types of hardware 3622 may be used to provide efficient, purpose-built support for software 3618 and services 3620 in deployment system 3606. In at least one embodiment, use of GPU processing may be implemented for processing locally (e.g., at facility 3602), within an Al / deep learning system, in a cloud system, and / or in other processing components of deployment system 3606 to improve efficiency, accuracy, and efficacy of game name recognition.

[0602] In at least one embodiment, software 3618 and / or services 3620 may be optimized for GPU processing with respect to deep learning, machine learning, and / or high-performance computing, as non-limiting examples. In at least one embodiment, at least some of computing environment of deployment system 3606 and / or training system 3604 may be executed in a datacenter one or more supercomputers or high performance computing systems, with GPU optimized software (e.g., hardware and software combination of NVIDIA’s DGX system). . In at least one embodiment, hardware 3622 may include any number of GPUs that may be called upon to perform processing of data in parallel, as described herein. In at least one embodiment, cloud platform may further include GPU processing for GPU-optimized execution of deep learning tasks, machine learning tasks, or other computing tasks. In at least one embodiment, cloud platform (e.g., NVIDIA’s NGC) may be executed using an Al / deep learning supercomputer(s) and / or GPU-optimized software (e.g., as provided on NVIDIA’s DGX systems) as a hardware abstraction and scaling platform. In at least one embodiment, cloud platform may integrate an application container clustering system or orchestration system (e.g., KUBERNETES) on multiple GPUs to enable seamless scaling and load balancing.

[0603] FIG. 37 is a system diagram for an example system 3700 for generating and deploying a deployment pipeline, in accordance with at least one embodiment. In at least one embodiment, system 3700 may be used to implement process 3600 of FIG. 36 and / or other processes including advanced processing and inferencing pipelines. In at least one embodiment, system 3700 may include training system 3604 and deployment system 3606. In at least one embodiment, training system 3604 and deployment system 3606 may be implemented using software 3618, services 3620, and / or hardware 3622, as described herein.

[0604] In at least one embodiment, system 3700 (e.g., training system 3604 and / or deployment system 3006) may implemented in a cloud computing environment (e.g., using cloud 3726). In at least one embodiment, system 3700 may be implemented locally with respect to a facility, or as a combination of both cloud and local computing resources. . In at least one embodiment, access to APIs in cloud 3726 may be restricted to authorized users through enacted security measures or protocols. In at least one embodiment, a security protocol may include web tokens that may be signed by an authentication (e.g., AuthN, AuthZ, Gluecon, etc.) service and may carry appropriate authorization. In at least one embodiment, APIs of virtual instruments (described herein), or other instantiations of system 3700, may be restricted to a set of public IPs that have been vetted or authorized for interaction.

[0605] In at least one embodiment, various components of system 3700 may communicate between and among one another using any of a variety of different network types, including but not limited to local area networks (LANs) and / or wide area networks (WANs) via wired and / or wireless communication protocols. In at least one embodiment, communication between facilities and components of system 3700 (e.g., for transmitting inference requests, for receiving results of inference requests, etc.) may be communicated over a data bus or data busses, wireless data protocols (Wi-Fi), wired data protocols (e.g., Ethernet), etc.

[0606] In at least one embodiment, training system 3604 may execute training pipelines 3704, similar to those described herein with respect to FIG. 36. In at least one embodiment, where one or more machine learning models are to be used in deployment pipelines 3710 by deployment system 3606, training pipelines 3704 may be used to train or retrain one or more (e.g., pretrained) models, and / or implement one or more of pre-trained models 3706 (e.g., without a need for retraining or updating). In at least one embodiment, as a result of training pipelines 3704, output model(s) 3616 may be generated. In at least one embodiment, training pipelines 3704 may include any number of processing steps37, Ai-assisted annotation 3610, labeling or annotating of imaging data 3608 to generate labeled data 3612, model selection from a model registry, model training 3614, training, retraining, or updating models, and / or other processing steps. In at least one embodiment, for different machine learning models used by deployment system 3606, different training pipelines 3704 may be used. In at least one embodiment, training pipeline 3704 similar to a first example described with respect to FIG. 36 may be used for a first machine learning model, training pipeline 3704 similar to a second example described with respect to FIG. 36 may be used for a second machine learning model, and training pipeline 3704 similar to a third example described with respect to FIG. 36 may be used for a third machine learning model. In at least one embodiment, any combination of tasks within training system 3604 may be used depending on what is required for each respective machine learning model. In at least one embodiment, one or more of machine learning models may already be trained and ready for deployment so machine learning models may not undergo any processing by training system 3604, and may be implemented by deployment system 3606.

[0607] In at least one embodiment, output model(s) 3616 and / or pre-trained model(s) 3706 may include any types of machine learning models depending on implementation or embodiment. In at least one embodiment, and without limitation, machine learning models used by system 3700 may include machine learning model(s) using linear regression, logistic regression, decision trees, support vector machines (SVM), Naive Bayes, k-nearest neighbor (Knn), K means clustering, random forest, dimensionality reduction algorithms, gradient boosting algorithms, neural networks (e.g., auto-encoders, convolutional, recurrent, perceptrons, Long / Short Term Memory (LSTM), Bi-LSTM, Hopfield, Boltzmann, deep belief, deconvolutional, generative adversarial, liquid state machine, etc.), and / or other types of machine learning models.

[0608] In at least one embodiment, training pipelines 3704 may include Ai-assisted annotation. In at least one embodiment, labeled data 3612 (e g., traditional annotation) may be generated by any number of techniques. In at least one embodiment, labels or other annotations may be generated within a drawing program (e.g., an annotation program), a computer aided design (CAD) program, a labeling program, another type of program suitable for generating annotations or labels for ground truth, and / or may be hand drawn, in some examples. In at least one embodiment, ground truth data may be synthetically produced (e.g., generated from computer models or renderings), real produced (e.g., designed and produced from real-world data), machine-automated (e.g., using feature analysis and learning to extract features from data and then generate labels), human annotated (e.g., labeler, or annotation expert, defines location of labels), and / or a combination thereof. In at least one embodiment, for each instance of imaging data 3608 (or other data type used by machine learning models), there may be corresponding ground truth data generated by training system 3604. In at least one embodiment, Ai-assisted annotation may be performed as part of deployment pipelines 3710; either in addition to, or in lieu of Ai-assisted annotation included in training pipelines 3704. In at least one embodiment, system 3700 may include a multi-layer platform that may include a software layer (e.g., software 3618) of diagnostic applications (or other application types) that may perform one or more medical imaging and diagnostic functions. .

[0609] In at least one embodiment, a software layer may be implemented as a secure, encrypted, and / or authenticated API through which applications or containers may be invoked (e.g., called) from an external environment(s) (e.g., facility 3602). In at least one embodiment, applications may then call or execute one or more services 3620 for performing compute, AI, or visualization tasks associated with respective applications, and software 3618 and / or services 3620 may leverage hardware 3622 to perform processing tasks in an effective and efficient manner.

[0610] In at least one embodiment, deployment system 3606 may execute deployment pipelines 3710. In at least one embodiment, deployment pipelines 3710 may include any number of applications that may be sequentially, non-sequentially, or otherwise applied to feedback data (and / or other data types) - including Ai-assisted annotation, as described above. In at least one embodiment, as described herein, a deployment pipeline 3710 for an individual device may be referred to as a virtual instrument for a device. In at least one embodiment, for a single device, there may be more than one deployment pipeline 3710 depending on information desired from data generated by a device. [0611 ] In at least one embodiment, applications available for deployment pipelines 3710 may include any application that may be used for performing processing tasks on feedback data or other data from devices. . In at least one embodiment, because various applications may share common image operations, in some embodiments, a data augmentation library (e.g., as one of services 3620) may be used to accelerate these operations. In at least one embodiment, to avoid bottlenecks of conventional processing approaches that rely on CPU processing, parallel computing platform 3730 may be used for GPU acceleration of these processing tasks.

[0612] In at least one embodiment, deployment system 3606 may include a user interface 3714 (e.g., a graphical user interface, a web interface, etc.) that may be used to select applications for inclusion in deployment pipeline(s) 3710, arrange applications, modify or change applications or parameters or constructs thereof, use and interact with deployment pipeline(s) 3710 during set-up and / or deployment, and / or to otherwise interact with deployment system 3606. In at least one embodiment, although not illustrated with respect to training system 3604, user interface 3714 (or a different user interface) may be used for selecting models for use in deployment system 3606, for selecting models for training, or retraining, in training system 3604, and / or for otherwise interacting with training system 3604.

[0613] In at least one embodiment, pipeline manager 3712 may be used, in addition to an application orchestration system 3728, to manage interaction between applications or containers of deployment pipeline(s) 3710 and services 3620 and / or hardware 3622. In at least one embodiment, pipeline manager 3712 may be configured to facilitate interactions from application to application, from application to service 3620, and / or from application or service to hardware 3622. In at least one embodiment, although illustrated as included in software 3618, this is not intended to be limiting, and in some examples pipeline manager 3712 may be included in services 3620. In at least one embodiment, application orchestration system 3728 (e.g., Kubernetes, DOCKER, etc.) may include a container orchestration system that may group applications into containers as logical units for coordination, management, scaling, and deployment. In at least one embodiment, by associating applications from deployment pipeline(s) 3710 (e.g., a reconstruction application, a segmentation application, etc.) with individual containers, each application may execute in a self-contained environment (e.g., at a kernel level) to increase speed and efficiency. 10614] In at least one embodiment, each application and / or container (or image thereof) may be individually developed, modified, and deployed (e.g., a first user or developer may develop, modify, and deploy a first application and a second user or developer may develop, modify, and deploy a second application separate from a first user or developer), which may allow for focus on, and attention to, a task of a single application and / or container(s) without being hindered by tasks of another application(s) or container(s). In at least one embodiment, communication, and cooperation between different containers or applications may be aided by pipeline manager 3712 and application orchestration system 3728. In at least one embodiment, so long as an expected input and / or output of each container or application is known by a system (e.g., based on constructs of applications or containers), application orchestration system 3728 and / or pipeline manager 3712 may facilitate communication among and between, and sharing of resources among and between, each of applications or containers. In at least one embodiment, because one or more of applications or containers in deployment pipeline(s) 3710 may share same services and resources, application orchestration system 3728 may orchestrate, load balance, and determine sharing of services or resources between and among various applications or containers. In at least one embodiment, a scheduler may be used to track resource requirements of applications or containers, current usage or planned usage of these resources, and resource availability. In at least one embodiment, a scheduler may thus allocate resources to different applications and distribute resources between and among applications in view of requirements and availability of a system. In some examples, a scheduler (and / or other component of application orchestration system 3728) may determine resource availability and distribution based on constraints imposed on a system (e.g., user constraints), such as quality of service (QoS), urgency of need for data outputs (e.g., to determine whether to execute real-time processing or delayed processing), etc.

[0615] In at least one embodiment, services 3620 leveraged by and shared by applications or containers in deployment system 3606 may include compute services 3716, AI services 3718, visualization services 3720, and / or other service types. In at least one embodiment, applications may call (e.g., execute) one or more of services 3620 to perform processing operations for an application. In at least one embodiment, compute services 3716 may be leveraged by applications to perform super-computing or other high-performance computing (HPC) tasks. In at least one embodiment, compute service(s) 3716 may be leveraged to perform parallel processing (e.g., using a parallel computing platform 3730) for processing data through one or more of applications and / or one or more tasks of a single application, substantially simultaneously. In at least one embodiment, parallel computing platform 3730 (e.g., NVIDIA’s CUDA) may enable general purpose computing on GPUs (GPGPU) (e.g., GPUs 3722). In at least one embodiment, a software layer of parallel computing platform 3730 may provide access to virtual instruction sets and parallel computational elements of GPUs, for execution of compute kernels. In at least one embodiment, parallel computing platform 3730 may include memory and, in some embodiments, a memory may be shared between and among multiple containers, and / or between and among different processing tasks within a single container. In at least one embodiment, inter-process communication (IPC) calls may be generated for multiple containers and / or for multiple processes within a container to use same data from a shared segment of memory of parallel computing platform 3730 (e.g., where multiple different stages of an application or multiple applications are processing same information). In at least one embodiment, rather than making a copy of data and moving data to different locations in memory (e.g., a read / write operation), same data in same location of a memory may be used for any number of processing tasks (e.g., at a same time, at different times, etc.). In at least one embodiment, as data is used to generate new data as a result of processing, this information of a new location of data may be stored and shared between various applications. In at least one embodiment, location of data and a location of updated or modified data may be part of a definition of how a payload is understood within containers.

[0616] In at least one embodiment, AI services 3718 may be leveraged to perform inferencing services for executing machine learning model(s) associated with applications (e.g., tasked with performing one or more processing tasks of an application). In at least one embodiment, AI services 3718 may leverage AI system 3724 to execute machine learning model(s) (e.g., neural networks, such as CNNs) for segmentation, reconstruction, object detection, feature detection, classification, and / or other inferencing tasks. In at least one embodiment, applications of deployment pipeline(s) 3710 may use one or more of output models 3616 from training system 3604 and / or other models of applications to perform inference on imaging data (e.g., DICOM data, RIS data, CIS data, REST compliant data, RPC data, raw data, etc.). In at least one embodiment, two or more examples of inferencing using application orchestration system 3728 (e.g., a scheduler) may be available. In at least one embodiment, a first category may include a high priority / low latency path that may achieve higher service level agreements, such as for performing inference on urgent requests during an emergency, or for a radiologist during diagnosis. In at least one embodiment, a second category may include a standard priority path that may be used for requests that may be non-urgent or where analysis may be performed at a later time. In at least one embodiment, application orchestration system 3728 may distribute resources (e.g., services 3620 and / or hardware 3622) based on priority paths for different inferencing tasks of AI services 3718.

[0617] In at least one embodiment, shared storage may be mounted to AI services 3718 within system 3700. In at least one embodiment, shared storage may operate as a cache (or other storage device type) and may be used to process inference requests from applications. In at least one embodiment, when an inference request is submitted, a request may be received by a set of API instances of deployment system 3606, and one or more instances may be selected (e.g., for best fit, for load balancing, etc.) to process a request. In at least one embodiment, to process a request, a request may be entered into a database, a machine learning model may be located from model registry 3624 if not already in a cache, a validation step may ensure appropriate machine learning model is loaded into a cache (e.g., shared storage), and / or a copy of a model may be saved to a cache. In at least one embodiment, a scheduler (e.g., of pipeline manager 3712) may be used to launch an application that is referenced in a request if an application is not already running or if there are not enough instances of an application. In at least one embodiment, if an inference server is not already launched to execute a model, an inference server may be launched. In at least one embodiment, any number of inference servers may be launched per model. In at least one embodiment, in a pull model, in which inference servers are clustered, models may be cached whenever load balancing is advantageous. In at least one embodiment, inference servers may be statically loaded in corresponding, distributed servers.

[0618] In at least one embodiment, inferencing may be performed using an inference server that runs in a container. In at least one embodiment, an instance of an inference server may be associated with a model (and optionally a plurality of versions of a model). In at least one embodiment, if an instance of an inference server does not exist when a request to perform inference on a model is received, a new instance may be loaded. In at least one embodiment, when starting an inference server, a model may be passed to an inference server such that a same container may be used to serve different models so long as inference server is running as a different instance. 10619] In at least one embodiment, during application execution, an inference request for a given application may be received, and a container (e.g., hosting an instance of an inference server) may be loaded (if not already), and a start procedure may be called. In at least one embodiment, pre-processing logic in a container may load, decode, and / or perform any additional pre-processing on incoming data (e.g., using a CPU(s) and / or GPU(s)). In at least one embodiment, once data is prepared for inference, a container may perform inference as necessary on data. In at least one embodiment, this may include a single inference call on one image (e.g., a hand X-ray), or may require inference on hundreds of images (e.g., a chest CT). In at least one embodiment, an application may summarize results before completing, which may include, without limitation, a single confidence score, pixel level-segmentation, voxel-level segmentation, generating a visualization, or generating text to summarize findings. In at least one embodiment, different models or applications may be assigned different priorities. For example, some models may have a real-time (TAT less than one minute) priority while others may have lower priority (e.g., TAT less than 10 minutes). In at least one embodiment, model execution times may be measured from requesting institution or entity and may include partner network traversal time, as well as execution on an inference service.

[0620] In at least one embodiment, transfer of requests between services 3620 and inference applications may be hidden behind a software development kit (SDK), and robust transport may be provide through a queue. In at least one embodiment, a request will be placed in a queue via an API for an individual application / tenant ID combination and an SDK will pull a request from a queue and give a request to an application. In at least one embodiment, a name of a queue may be provided in an environment from where an SDK will pick it up. In at least one embodiment, asynchronous communication through a queue may be useful as it may allow any instance of an application to pick up work as it becomes available. In at least one embodiment, results may be transferred back through a queue, to ensure no data is lost. In at least one embodiment, queues may also provide an ability to segment work, as highest priority work may go to a queue with most instances of an application connected to it, while lowest priority work may go to a queue with a single instance connected to it that processes tasks in an order received. In at least one embodiment, an application may run on a GPU-accelerated instance generated in cloud 3726, and an inference service may perform inferencing on a GPU. [06211 In at least one embodiment, visualization services 3720 may be leveraged to generate visualizations for viewing outputs of applications and / or deployment pipeline(s) 3710. In at least one embodiment, GPUs 3722 may be leveraged by visualization services 3720 to generate visualizations. In at least one embodiment, rendering effects, such as ray-tracing, may be implemented by visualization services 3720 to generate higher quality visualizations. In at least one embodiment, visualizations may include, without limitation, 2D image renderings, 3D volume renderings, 3D volume reconstruction, 2D tomographic slices, virtual reality displays, augmented reality displays, etc. In at least one embodiment, virtualized environments may be used to generate a virtual interactive display or environment (e.g., a virtual environment) for interaction by users of a system (e.g., doctors, nurses, radiologists, etc.). In at least one embodiment, visualization services 3720 may include an internal visualizer, cinematics, and / or other rendering or image processing capabilities or functionality (e.g., ray tracing, rasterization, internal optics, etc.).

[0622] In at least one embodiment, hardware 3622 may include GPUs 3722, AI system 3724, cloud 3726, and / or any other hardware used for executing training system 3604 and / or deployment system 3606. In at least one embodiment, GPUs 3722 (e.g., NVIDIA’s TESLA and / or QUADRO GPUs) may include any number of GPUs that may be used for executing processing tasks of compute services 3716, AI services 3718, visualization services 3720, other services, and / or any of features or functionality of software 3618. For example, with respect to AI services 3718, GPUs 3722 may be used to perform pre-processing on imaging data (or other data types used by machine learning models), post-processing on outputs of machine learning models, and / or to perform inferencing (e.g., to execute machine learning models). In at least one embodiment, cloud 3726, AI system 3724, and / or other components of system 3700 may use GPUs 3722. In at least one embodiment, cloud 3726 may include a GPU-optimized platform for deep learning tasks. In at least one embodiment, AI system 3724 may use GPUs, and cloud 3726 - or at least a portion tasked with deep learning or inferencing - may be executed using one or more AI systems 3724. As such, although hardware 3622 is illustrated as discrete components, this is not intended to be limiting, and any components of hardware 3622 may be combined with, or leveraged by, any other components of hardware 3622.

[0623] In at least one embodiment, AI system 3724 may include a purpose-built computing system (e.g., a super-computer or an HPC) configured for inferencing, deep learning, machine learning, and / or other artificial intelligence tasks. In at least one embodiment, AI system 3724 (e.g., NVIDIA’s DGX) may include GPU-optimized software (e.g., a software stack) that may be executed using a plurality of GPUs 3722, in addition to CPUs, RAM, storage, and / or other components, features, or functionality. In at least one embodiment, one or more AI systems 3724 may be implemented in cloud 3726 (e.g., in a data center) for performing some or all of Al-based processing tasks of system 3700.

[0624] In at least one embodiment, cloud 3726 may include a GPU-accelerated infrastructure (e.g., NVIDIA’s NGC) that may provide a GPU-optimized platform for executing processing tasks of system 3700. In at least one embodiment, cloud 3726 may include an AI system(s) 3724 for performing one or more of Al-based tasks of system 3700 (e.g., as a hardware abstraction and scaling platform). In at least one embodiment, cloud 3726 may integrate with application orchestration system 3728 leveraging multiple GPUs to enable seamless scaling and load balancing between and among applications and services 3620. In at least one embodiment, cloud 3726 may tasked with executing at least some of services 3620 of system 3700, including compute services 3716, AI services 3718, and / or visualization services 3720, as described herein. In at least one embodiment, cloud 3726 may perform small and large batch inference (e.g., executing NVIDIA’s TENSOR RT), provide an accelerated parallel computing API and platform 3730 (e.g., NVIDIA’s CUDA), execute application orchestration system 3728 (e.g., KUBERNETES), provide a graphics rendering API and platform (e.g., for ray-tracing, 2D graphics, 3D graphics, and / or other rendering techniques to produce higher quality cinematics), and / or may provide other functionality for system 3700.

[0625] In at least one embodiment, in an effort to preserve patient confidentiality (e.g., where patient data or records are to be used off-premises), cloud 3726 may include a registry - such as a deep learning container registry. In at least one embodiment, a registry may store containers for instantiations of applications that may perform pre-processing, post-processing, or other processing tasks on patient data. In at least one embodiment, cloud 3726 may receive data that includes patient data as well as sensor data in containers, perform requested processing for just sensor data in those containers, and then forward a resultant output and / or visualizations to appropriate parties and / or devices (e.g., on-premises medical devices used for visualization or diagnoses), all without having to extract, store, or otherwise access patient data. In at least one embodiment, confidentiality of patient data is preserved in compliance with HIPAA and / or other data regulations.

[0626] FIG. 38 includes an example illustration of a deployment pipeline 3710A for processing imaging data, in accordance with at least one embodiment. In at least one embodiment, system 3700 - and specifically deployment system 3606 - may be used to customize, update, and / or integrate deployment pipeline(s) 3710A into one or more production environments. In at least one embodiment, deployment pipeline 3710A of FIG. 38 includes a non-limiting example of a deployment pipeline 3710A that may be custom defined by a particular user (or team of users) at a facility (e.g., at a hospital, clinic, lab, research environment, etc.). In at least one embodiment, to define deployment pipelines 3710A for a CT scanner 3802, a user may select - from a container registry, for example - one or more applications that perform specific functions or tasks with respect to imaging data generated by CT scanner 3802. In at least one embodiment, applications may be applied to deployment pipeline 3710A as containers that may leverage services 3620 and / or hardware 3622 of system 3700. In addition, deployment pipeline 3710A may include additional processing tasks or applications that may be implemented to prepare data for use by applications (e.g., DICOM adapter 3702B and DICOM reader 3806 may be used in deployment pipeline 3710A to prepare data for use by CT reconstruction 3808, organ segmentation 3810, etc.). In at least one embodiment, deployment pipeline 3710A may be customized or selected for consistent deployment, one time use, or for another frequency or interval. In at least one embodiment, a user may desire to have CT reconstruction 3808 and organ segmentation 3810 for several subjects over a specific interval, and thus may deploy pipeline 3710A for that period of time. In at least one embodiment, a user may select, for each request from system 3700, applications that a user wants to perform processing on that data for that request. In at least one embodiment, deployment pipeline 3710A may be adjusted at any interval and, because of adaptability and scalability of a container structure within system 3700, this may be a seamless process.

[0627] In at least one embodiment, deployment pipeline 3710A of FIG. 38 may include CT scanner 3802 generating imaging data of a patient or subject. In at least one embodiment, imaging data from CT scanner 3802 may be stored on a PACS server(s) 3804 associated with a facility housing CT scanner 3802. In at least one embodiment, PACS server(s) 3804 may include software and / or hardware components that may directly interface with imaging modalities (e.g., CT scanner 3802) at a facility. In at least one embodiment, DICOM adapter 3702B may enable sending and receipt of DICOM objects using DICOM protocols. In at least one embodiment, DICOM adapter 3702B may aid in preparation or configuration of DICOM data from PACS server(s) 3804 for use by deployment pipeline 3710A. In at least one embodiment, once DICOM data is processed through DICOM adapter 3702B, pipeline manager 3712 may route data through to deployment pipeline 3710A. In at least one embodiment, DICOM reader 3806 may extract image files and any associated metadata from DICOM data (e.g., raw sinogram data, as illustrated in visualization 3816A). In at least one embodiment, working files that are extracted may be stored in a cache for faster processing by other applications in deployment pipeline 3710A. In at least one embodiment, once DICOM reader 3806 has finished extracting and / or storing data, a signal of completion may be communicated to pipeline manager 3712. In at least one embodiment, pipeline manager 3712 may then initiate or call upon one or more other applications or containers in deployment pipeline 3710A.

[0628] In at least one embodiment, CT reconstruction 3808 application and / or container may be executed once data (e.g., raw sinogram data) is available for processing by CT reconstruction 3808 application. In at least one embodiment, CT reconstruction 3808 may read raw sinogram data from a cache, reconstruct an image file out of raw sinogram data (e.g., as illustrated in visualization 3816B), and store resulting image file in a cache. In at least one embodiment, at completion of reconstruction, pipeline manager 3712 may be signaled that reconstruction task is complete. In at least one embodiment, once reconstruction is complete, and a reconstructed image file may be stored in a cache (or other storage device), organ segmentation 3810 application and / or container may be triggered by pipeline manager 3712. In at least one embodiment, organ segmentation 3810 application and / or container may read an image file from a cache, normalize or convert an image file to format suitable for inference (e.g., convert an image file to an input resolution of a machine learning model), and run inference against a normalized image. In at least one embodiment, to run inference on a normalized image, organ segmentation 3810 application and / or container may rely on services 3620, and pipeline manager 3712 and / or application orchestration system 3728 may facilitate use of services 3620 by organ segmentation 3810 application and / or container. In at least one embodiment, for example, organ segmentation 3810 application and / or container may leverage AI services 3718 to perform inference on a normalized image, and AI services 3718 may leverage hardware 3622 (e.g., AI system 3724) to execute AI services 3718. In at least one embodiment, a result of an inference may be a mask file (e.g., as illustrated in visualization 3816C) that may be stored in a cache (or other storage device).

[0629] In at least one embodiment, once applications that process DICOM data and / or data extracted from DICOM data have completed processing, a signal may be generated for pipeline manager 3712. In at least one embodiment, pipeline manager 3712 may then execute DICOM writer 3812 to read results from a cache (or other storage device), package results into a DICOM format (e.g., as DICOM output 3814) for use by users at a facility who generated a request. In at least one embodiment, DICOM output 3814 may then be transmitted to DICOM adapter 3702B to prepare DICOM output 3814 for storage on PACS server(s) 3804 (e.g., for viewing by a DICOM viewer at a facility). In at least one embodiment, in response to a request for reconstruction and segmentation, visualizations 3816B and 3816C may be generated and available to a user for diagnoses, research, and / or for other purposes.

[0630] Although illustrated as consecutive application in deployment pipeline 3710A, CT reconstruction 3808 and organ segmentation 3810 applications may be processed in parallel in at least one embodiment. In at least one embodiment, where applications do not have dependencies on one another, and data is available for each application (e.g., after DICOM reader 3806 extracts data), applications may be executed at a same time, substantially at a same time, or with some overlap. In at least one embodiment, where two or more applications require similar services 3620, a scheduler of system 3700 may be used to load balance and distribute compute or processing resources between and among various applications. In at least one embodiment, in some embodiments, parallel computing platform 3730 may be used to perform parallel processing for applications to decrease run-time of deployment pipeline 3710A to provide realtime results.

[0631] In at least one embodiment, and with reference to FIGS. 39A-39B, deployment system 3606 may be implemented as one or more virtual instruments to perform different functionalities - such as image processing, segmentation, enhancement, AI, visualization, and inferencing -with imaging devices (e.g., CT scanners, X-ray machines, MRI machines, etc.), sequencing devices, genomics devices, and / or other device types. In at least one embodiment, system 3700 may allow for creation and provision of virtual instruments that may include a software-defined deployment pipeline 3710 that may receive raw / unprocessed input data generated by a device(s) and output processed / reconstructed data. In at least one embodiment, deployment pipelines 3710 (e.g., 3710A and 371 OB) that represent virtual instruments may implement intelligence into a pipeline, such as by leveraging machine learning models, to provide containerized inference support to a system. In at least one embodiment, virtual instruments may execute any number of containers each including instantiations of applications. In at least one embodiment, such as where real-time processing is desired, deployment pipelines 3710 representing virtual instruments may be static (e.g., containers and / or applications may be set), while in other examples, container and / or applications for virtual instruments may be selected (e.g., on a perrequest basis) from a pool of applications or resources (e.g., within a container registry).

[0632] In at least one embodiment, system 3700 may be instantiated or executed as one or more virtual instruments on-premise at a facility in, for example, a computing system deployed next to or otherwise in communication with a radiology machine, an imaging device, and / or another device type at a facility. In at least one embodiment, however, an on-premise installation may be instantiated or executed within a computing system of a device itself (e.g., a computing system integral to an imaging device), in a local datacenter (e.g., a datacenter on-premise), and / or in a cloud-environment (e.g., in cloud 3726). In at least one embodiment, deployment system 3606, operating as a virtual instrument, may be instantiated by a supercomputer or other HPC system in some examples. In at least one embodiment, on-premise installation may allow for high-bandwidth uses (via, for example, higher throughput local communication interfaces, such as RF over Ethernet) for real-time processing. In at least one embodiment, real-time or near real-time processing may be particularly useful where a virtual instrument supports an ultrasound device or other imaging modality where immediate visualizations are expected or required for accurate diagnoses and analyses. In at least one embodiment, a cloud-computing architecture may be capable of dynamic bursting to a cloud computing service provider, or other compute cluster, when local demand exceeds on-premise capacity or capability. In at least one embodiment, a cloud architecture, when implemented, may be tuned for training neural networks or other machine learning models, as described herein with respect to training system 3604. In at least one embodiment, with training pipelines in place, machine learning models may be continuously learn and improve as they process additional data from devices they support. In at least one embodiment, virtual instruments may be continually improved using additional data, new data, existing machine learning models, and / or new or updated machine learning models.

[0633] In at least one embodiment, a computing system may include some or all of hardware 3622 described herein, and hardware 3622 may be distributed in any of a number of ways including within a device, as part of a computing device coupled to and located proximate a device, in a local datacenter at a facility, and / or in cloud 3726. In at least one embodiment, because deployment system 3606 and associated applications or containers are created in software (e.g., as discrete containerized instantiations of applications), behavior, operation, and configuration of virtual instruments, as well as outputs generated by virtual instruments, may be modified or customized as desired, without having to change or alter raw output of a device that a virtual instrument supports.

[0634] FIG. 39A includes an example data flow diagram of a virtual instrument supporting an ultrasound device, in accordance with at least one embodiment. In at least one embodiment, deployment pipeline 3710B may leverage one or more of services 3620 of system 3700. In at least one embodiment, deployment pipeline 3710B and services 3620 may leverage hardware 3622 of a system either locally or in cloud 3726. In at least one embodiment, although not illustrated, process 3900 may be facilitated by pipeline manager 3712, application orchestration system 3728, and / or parallel computing platform 3730.

[0635] In at least one embodiment, process 3900 may include receipt of imaging data from an ultrasound device 3902. In at least one embodiment, imaging data may be stored on PACS server(s) in a DICOM format (or other format, such as RIS, CIS, REST compliant, RPC, raw, etc.), and may be received by system 3700 for processing through deployment pipeline 3710 selected or customized as a virtual instrument (e.g., a virtual ultrasound) for ultrasound device 3902. In at least one embodiment, imaging data may be received directly from an imaging device (e.g., ultrasound device 3902) and processed by a virtual instrument. In at least one embodiment, a transducer or other signal converter communicatively coupled between an imaging device and a virtual instrument may convert signal data generated by an imaging device to image data that may be processed by a virtual instrument. In at least one embodiment, raw data and / or image data may be applied to DICOM reader 3806 to extract data for use by applications or containers of deployment pipeline 3710B. In at least one embodiment, DICOM reader 3806 may leverage data augmentation library 3914 (e.g., NVIDIA’s DALI) as a service 3620 (e.g., as one of compute service(s) 3716) for extracting, resizing, rescaling, and / or otherwise preparing data for use by applications or containers.

[0636] In at least one embodiment, once data is prepared, a reconstruction 3906 application and / or container may be executed to reconstruct data from ultrasound device 3902 into an image file. In at least one embodiment, after reconstruction 3906, or at a same time as reconstruction 3906, a detection 3908 application and / or container may be executed for anomaly detection, object detection, feature detection, and / or other detection tasks related to data. In at least one embodiment, an image file generated during reconstruction 3906 may be used during detection 3908 to identify anomalies, objects, features, etc. In at least one embodiment, detection 3908 application may leverage an inference engine 3916 (e.g., as one of AI service(s) 3718) to perform inference on data to generate detections. In at least one embodiment, one or more machine learning models (e.g., from training system 3604) may be executed or called by detection 3908 application.

[0637] In at least one embodiment, once reconstruction 3906 and / or detection 3908 is / are complete, data output from these application and / or containers may be used to generate visualizations 3910, such as visualization 3912 (e.g., a grayscale output) displayed on a workstation or display terminal. In at least one embodiment, visualization may allow a technician or other user to visualize results of deployment pipeline 3710B with respect to ultrasound device 3902. In at least one embodiment, visualization 3910 may be executed by leveraging a render component 3918 of system 3700 (e.g., one of visualization service(s) 3720). In at least one embodiment, render component 3918 may execute a 2D, OpenGL, or ray-tracing service to generate visualization 3912.

[0638] FIG. 39B includes an example data flow diagram of a virtual instrument supporting a CT scanner, in accordance with at least one embodiment. In at least one embodiment, deployment pipeline 3710C may leverage one or more of services 3620 of system 3700. In at least one embodiment, deployment pipeline 3710C and services 3620 may leverage hardware 3622 of a system either locally or in cloud 3726. In at least one embodiment, although not illustrated, process 3980 may be facilitated by pipeline manager 3712, application orchestration system 3728, and / or parallel computing platform 3730.

[0639] In at least one embodiment, process 3920 may include CT scanner 3922 generating raw data that may be received by DICOM reader 3806 (e.g., directly, via a PACS server 3804, after processing, etc.). In at least one embodiment, a Virtual CT (instantiated by deployment pipeline 3710C) may include a first, real-time pipeline for monitoring a patient (e.g., patient movement detection AI 3926) and / or for adjusting or optimizing exposure of CT scanner 3922 (e.g., using exposure control AI 3924). In at least one embodiment, one or more of applications (e.g., 3924 and 3926) may leverage a service 3620, such as AI service(s) 3718. In at least one embodiment, outputs of exposure control AI 3924 application (or container) and / or patient movement detection AI 3926 application (or container) may be used as feedback to CT scanner 3922 and / or a technician for adjusting exposure (or other settings of CT scanner 3922) and / or informing a patient to move less.

[0640] In at least one embodiment, deployment pipeline 3710C may include a non-real-time pipeline for analyzing data generated by CT scanner 3922. In at least one embodiment, a second pipeline may include CT reconstruction 3808 application and / or container, a coarse detection AI 3928 application and / or container, a fine detection AI 3932 application and / or container (e.g., where certain results are detected by coarse detection AI 3928), a visualization 3930 application and / or container, and a DICOM writer 3812 (and / or other data type writer, such as RIS, CIS, REST compliant, RPC, raw, etc.) application and / or container. In at least one embodiment, raw data generated by CT scanner 3922 may be passed through pipelines of deployment pipeline 3710C (instantiated as a virtual CT instrument) to generate results. In at least one embodiment, results from DICOM writer 3812 may be transmitted for display and / or may be stored on PACS server(s) 3804 for later retrieval, analysis, or display by a technician, practitioner, or other user.

[0641] In at least one embodiment, a single semiconductor platform may refer to a sole unitary semiconductor-based integrated circuit or chip. In at least one embodiment, multi-chip modules may be used with increased connectivity which simulate on-chip operation, and make substantial improvements over utilizing a conventional central processing unit (“CPU”) and bus implementation. In at least one embodiment, various modules may also be situated separately or in various combinations of semiconductor platforms per desires of user.

[0642] In at least one embodiment, referring back to FIG. 13, computer programs in form of machine-readable executable code or computer control logic algorithms are stored in main memory 1304 and / or secondary storage. Computer programs, if executed by one or more processors, enable system 1300 to perform various functions in accordance with at least one embodiment. In at least one embodiment, memory 1304, storage, and / or any other storage are possible examples of computer-readable media. In at least one embodiment, secondary storage may refer to any suitable storage device or system such as a hard disk drive and / or a removable storage drive, representing a floppy disk drive, a magnetic tape drive, a compact disk drive, digital versatile disk (“DVD”) drive, recording device, universal serial bus (“USB”) flash memory, etc. In at least one embodiment, architecture and / or functionality of various previous figures are implemented in context of CPU 1302, parallel processing system 1312, an integrated circuit capable of at least a portion of capabilities of both CPU 1302, parallel processing system 1312, a chipset (e.g., a group of integrated circuits designed to work and sold as a unit for performing related functions, etc.), and / or any suitable combination of integrated circuit(s).

[0643] In at least one embodiment, architecture and / or functionality of various previous figures are implemented in context of a general computer system, a circuit board system, a game console system dedicated for entertainment purposes, an application-specific system, and more. In at least one embodiment, computer system 1300 may take form of a desktop computer, a laptop computer, a tablet computer, servers, supercomputers, a smart-phone (e.g., a wireless, hand-held device), personal digital assistant (“PDA”), a digital camera, a vehicle, a head mounted display, a hand-held electronic device, a mobile phone device, a television, workstation, game consoles, embedded system, and / or any other type of logic.

[0644] In at least one embodiment, parallel processing system 1312 includes, without limitation, a plurality of parallel processing units (“PPUs”) 1314 and associated memories 1316. In at least one embodiment, PPUs 1314 are connected to a host processor or other peripheral devices via an interconnect 1318 and a switch 1320 or multiplexer. In at least one embodiment, parallel processing system 712 distributes computational tasks across PPUs 1314 which can be parallelizable — for example, as part of distribution of computational tasks across multiple graphics processing unit (“GPU”) thread blocks. In at least one embodiment, memory is shared and accessible (e.g., for read and / or write access) across some or all of PPUs 1314, although such shared memory may incur performance penalties relative to use of local memory and registers resident to a PPU 1314. In at least one embodiment, operation of PPUs 1314 is synchronized through use of a command such as__syncthreadsQ, wherein all threads in a block (e.g., executed across multiple PPUs 1314) to reach a certain point of execution of code before proceeding.

[0645] Other variations are within spirit of present disclosure. Thus, while disclosed techniques are susceptible to various modifications and alternative constructions, certain illustrated embodiments thereof are shown in drawings and have been described above in detail. It should be understood, however, that there is no intention to limit disclosure to specific form or forms disclosed, but on contrary, intention is to cover all modifications, alternative constructions, and equivalents falling within spirit and scope of disclosure, as defined in appended claims. [0€H6] Use of terms “a” and “an” and “the” and similar referents in context of describing disclosed embodiments (especially in context of following claims) are to be construed to cover both singular and plural, unless otherwise indicated herein or clearly contradicted by context, and not as a definition of a term. Terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (meaning “including, but not limited to,”) unless otherwise noted. “Connected,” when unmodified and referring to physical connections, is to be construed as partly or wholly contained within, attached to. or joined together, even if there is something intervening. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within range, unless otherwise indicated herein and each separate value is incorporated into specification as if it were individually recited herein. In at least one embodiment, use of term “set” (e.g., “a set of items”) or “subset” unless otherwise noted or contradicted by context, is to be construed as a nonempty collection comprising one or more members. Further, unless otherwise noted or contradicted by context, term “subset” of a corresponding set does not necessarily denote a proper subset of corresponding set, but subset and corresponding set may be equal.

[0647] Conjunctive language, such as phrases of form “at least one of A, B, and C,” or “at least one of A, B and C,” unless specifically stated otherwise or otherwise clearly contradicted by context, is otherwise understood with context as used in general to present that an item, term, etc., may be either A or B or C, or any nonempty subset of set of A and B and C. For instance, in illustrative example of a set having three members, conjunctive phrases “at least one of A, B, and C” and “at least one of A, B and C” refer to any of following sets: {A}, {B}, {C}, {A, B}, {A, C}, {B, C}, {A, B, C}. Thus, such conjunctive language is not generally intended to imply that certain embodiments require at least one of A, at least one of B and at least one of C each to be present. In addition, unless otherwise noted or contradicted by context, term “plurality” indicates a state of being plural (e.g., “a plurality of items” indicates multiple items). In at least one embodiment, number of items in a plurality is at least two, but can be more when so indicated either explicitly or by context. Further, unless stated otherwise or otherwise clear from context, phrase “based on” means “based at least in part on” and not “based solely on.”

[0648] Operations of processes described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. In at least one embodiment, a process such as those processes described herein (or variations and / or combinations thereof) is performed under control of one or more computer systems configured with executable instructions and is implemented as code (e.g., executable instructions, one or more computer programs or one or more applications) executing collectively on one or more processors, by hardware or combinations thereof. In at least one embodiment, code is stored on a computer-readable storage medium, for example, in form of a computer program comprising a plurality of instructions executable by one or more processors. In at least one embodiment, a computer-readable storage medium is a non-transitory computer-readable storage medium that excludes transitory signals (e.g., a propagating transient electric or electromagnetic transmission) but includes non-transitory data storage circuitry (e.g., buffers, cache, and queues) within transceivers of transitory signals. In at least one embodiment, code (e.g., executable code or source code) is stored on a set of one or more non-transitory computer-readable storage media having stored thereon executable instructions (or other memory to store executable instructions) that, when executed (i.e., as a result of being executed) by one or more processors of a computer system, cause computer system to perform operations described herein. In at least one embodiment, set of non-transitory computer-readable storage media comprises multiple non-transitory computer-readable storage media and one or more of individual non-transitory storage media of multiple non-transitory computer-readable storage media lack all of code while multiple non-transitory computer-readable storage media collectively store all of code. In at least one embodiment, executable instructions are executed such that different instructions are executed by different processors — for example, a non-transitory computer-readable storage medium store instructions and a main central processing unit (“CPU”) executes some of instructions while a graphics processing unit (“GPU”) executes other instructions. In at least one embodiment, different components of a computer system have separate processors and different processors execute different subsets of instructions.

[0649] Accordingly, in at least one embodiment, computer systems are configured to implement one or more services that singly or collectively perform operations of processes described herein and such computer systems are configured with applicable hardware and / or software that enable performance of operations. Further, a computer system that implements at least one embodiment of present disclosure is a single device and, in another embodiment, is a distributed computer system comprising multiple devices that operate differently such that distributed computer system performs operations described herein and such that a single device does not perform all operations.

[0650] Use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate embodiments of disclosure and does not pose a limitation on scope of disclosure unless otherwise claimed. No language in specification should be construed as indicating any non-claimed element as essential to practice of disclosure. [0651 ] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.

[0652] In description and claims, terms “coupled” and “connected,” along with their derivatives, may be used. It should be understood that these terms may be not intended as synonyms for each other. Rather, in particular examples, “connected” or “coupled” may be used to indicate that two or more elements are in direct or indirect physical or electrical contact with each other. “Coupled” may also mean that two or more elements are not in direct contact with each other, but yet still co-operate or interact with each other. [0653 ] Unless specifically stated otherwise, it may be appreciated that throughout specification terms such as “processing,” “computing,” “calculating,” “determining,” or like, refer to action and / or processes of a computer or computing system, or similar electronic computing device, that manipulate and / or transform data represented as physical, such as electronic, quantities within computing system’s registers and / or memories into other data similarly represented as physical quantities within computing system’s memories, registers or other such information storage, transmission or display devices.

[0654] In a similar manner, term “processor” may refer to any device or portion of a device that processes electronic data from registers and / or memory and transform that electronic data into other electronic data that may be stored in registers and / or memory. As non-limiting examples, “processor” may be a CPU or a GPU. A “computing platform” may comprise one or more processors. As used herein, “software” processes may include, for example, software and / or hardware entities that perform work over time, such as tasks, threads, and intelligent agents. Also, each process may refer to multiple processes, for carrying out instructions in sequence or in parallel, continuously or intermittently. In at least one embodiment, terms “system” and “method” are used herein interchangeably insofar as system may embody one or more methods and methods may be considered a system.

[0655] In present document, references may be made to obtaining, acquiring, receiving, or inputting analog or digital data into a subsystem, computer system, or computer-implemented machine. In at least one embodiment, process of obtaining, acquiring, receiving, or inputting analog and digital data can be accomplished in a variety of ways such as by receiving data as a parameter of a function call or a call to an application programming interface. In at least one embodiment, processes of obtaining, acquiring, receiving, or inputting analog or digital data can be accomplished by transferring data via a serial or parallel interface. In at least one embodiment, processes of obtaining, acquiring, receiving, or inputting analog or digital data can be accomplished by transferring data via a computer network from providing entity to acquiring entity. In at least one embodiment, references may also be made to providing, outputting, transmitting, sending, or presenting analog or digital data. In various examples, processes of providing, outputting, transmitting, sending, or presenting analog or digital data can be accomplished by transferring data as an input or output parameter of a function call, a parameter of an application programming interface or interprocess communication mechanism.

[0656] Although descriptions herein set forth example implementations of described techniques, other architectures may be used to implement described functionality, and are intended to be within scope of this disclosure. Furthermore, although specific distributions of responsibilities may be defined above for purposes of description, various functions and responsibilities might be distributed and divided in different ways, depending on circumstances.

[0657] Furthermore, although subject matter has been described in language specific to structural features and / or methodological acts, it is to be understood that subject matter claimed in appended claims is not necessarily limited to specific features or acts described. Rather, specific features and acts are disclosed as exemplary forms of implementing below claims.

[0658] Aspects of the invention are also set out in the following set of numbered clauses, in which is described: 1. A processor comprising: one or more circuits to interpolate one or more intermediate images from two or more images based, at least in part, on one or more inconsistent flow vectors corresponding to the two or more images. 2. The processor of clause 1, wherein the one or more circuits is configured to determine the one or more inconsistent flow vectors based on comparison of forward optical flow data from a first image to a second image in the two or more images and backward optical flow data from the second image to the first image. 3. The processor of clause 1 or clause 2, wherein the one or more circuits are further to determine candidate pixels associated with coordinates of the one or more inconsistent flow vectors. 4. The processor of any preceding clause, wherein the one or more circuits are further configured to generate a gradient map for a first image in the two or more images. 5. The processor of any preceding clause, wherein the one or more circuits are further configured to identify a plurality of superpixels starting from coordinates of candidate pixels associated with the inconsistent flow vectors using a gradient map and intensity information from a first image of the two or more images. 6. The processor of any preceding clause, wherein the one or more circuits are further configured to use a first criterion to group edge coordinates, determined from an edge detection operation, with neighbor edge coordinates and to use a second criterion to group non-edge coordinates with neighboring non-edge coordinates. 7. The processor of any preceding clause, wherein the one or more circuits are further configured to identify a plurality of superpixels based on downsampled versions of the two or more images. 8. The processor of any preceding clause, wherein the one or more circuits are further configured to traverse neighboring pixels of a candidate pixel using at least one of a first buffer or a second buffer and an array of neighbor coordinates, wherein the first buffer includes a first value wherein bits corresponding to the first value indicate current level coordinates using the array of neighbor coordinates, and wherein the second buffer includes a second value wherein bits corresponding to the second value indicate next level coordinates using the array of neighbor coordinates. 9. The processor of any preceding clause, wherein the one or more circuits are further configured to interpolate one or more intermediate images from two or more images using a plurality of superpixels for the two or more images. 10. A system, comprising a memory; and one or more circuits, the one or more circuits configured to: generate one or more superpixels for one or more images based, at least in part, on one or more inconsistent flow vectors or one or more invalid pixels corresponding to the one or more images. 11. The system of clause 10, wherein the one or more circuits is configured to determine the one or more inconsistent flow vectors based on comparison of forward optical flow data from a first image to a second image in the one or more images and backward optical flow data from the second image to the first images. 12. The system of clause 10 or clause 11, wherein the one or more circuits are further to determine candidate pixels associated with coordinates of the one or more inconsistent flow vectors. 13. The system of any of clauses 10-12, wherein the one or more circuits are further configured to generate a gradient map for a first image in the one or more images. 14. The system of any of clauses 10-13, wherein the one or more circuits are further configured to identify the one or more superpixels starting from coordinates of candidate pixels associated with the inconsistent flow vectors using a gradient map and intensity information from a first image of the one or more images. 15. The system of any of clauses 10-14, wherein the one or more circuits arc further configured to perform flows infilling or image infilling on the one or more images using the one or more superpixels. 16. The system of any of clauses 10-15, wherein the one or more circuits are further configured to identify the one or more superpixels based on downsampled versions of the one or more images. 17. The system of any of clauses 10-16, wherein the one or more circuits are further configured to traverse neighboring pixels of a candidate pixel using at least one of a first buffer or a second buffer and an array of neighbor coordinates, wherein the first buffer includes a first value wherein bits corresponding to the first value indicate current level coordinates using the array of neighbor coordinates, and wherein the second buffer includes a second value wherein bits corresponding to the second value indicate next level coordinates using the array of neighbor coordinates. 18.    The system of any of clauses 10-17, wherein the one or more circuits are further configured to interpolate one or more intermediate images from one or more images using the one or more superpixels for the one or more images. 19. A method comprising: interpolating one or more intermediate images from two or more images based, at least in part, on one or more inconsistent flow vectors corresponding to the two or more images. 20. The method of clause 19, further comprising: determining the one or more inconsistent flow vectors based on comparison of forward optical flow data from a first image to a second image in the two or more images and backward optical flow data from the second image to the first image. 21. The method of clause 19 or clause 20, further comprising determining candidate pixels associated with coordinates of the one or more inconsistent flow vectors. 22. The method of any of clauses 19-21, further comprising generating a gradient map for a first image in the two or more images. 23. The method of any of clauses 19-22, further comprising identifying a plurality of superpixels starting from coordinates of candidate pixels associated with the inconsistent flow vectors using a gradient map and intensity information from a first image of the two or more images. 24. The method of any of clauses 19-23, further comprising using a first criterion to group edge coordinates, determined from an edge detection operation, with neighbor edge coordinates and using a second criterion to group non-edge coordinates with neighboring non-edge coordinates. 25. The method of any of clauses 19-24, further comprising identifying a plurality of superpixels based on downsampled versions of the two or more images. 26. The method of any of clauses 19-25, further comprising traversing neighboring pixels of a candidate pixel using at least one of a first buffer or a second buffer and an array of neighbor coordinates, wherein the first buffer includes a first value wherein bits corresponding to the first value indicate current level coordinates using the array of neighbor coordinates, and wherein the second buffer includes a second value wherein bits corresponding to the second value indicate next level coordinates using the array of neighbor coordinates. 27. The method of any of clauses 19-26, further comprising: traversing neighboring pixels of a candidate pixel using a second buffer and the array of neighbor coordinates, wherein the second buffer includes a second value wherein bits corresponding to the second value indicate next level coordinates using the array of neighbor coordinates. 28. The method of any of clauses 19-27, further comprising: interpolating one or more intermediate images from two or more images using a plurality of superpixels for the two or more images. 29. A processor comprising one or more circuits to train one or more neural networks to perform a machine vision task using a training dataset comprising one or more corrected optical flow vectors between two or more images, the one or more optical flow vectors having been corrected using superpixels determined based on inconsistent flow vectors between the two or more images. 30. The processor of clause 29, wherein the one or more circuits are further to determine the one or more inconsistent flow vectors based on comparison of forward optical flow data from a first image to a second image in the two or more images and backward optical flow data from the second image to the first image. 31. The processor of clause 29 or clause 30, wherein the one or more circuits are further to determine candidate pixels associated with coordinates of the one or more inconsistent flow vectors. 32. The processor of any of clauses 29-31, wherein the one or more circuits are further configured to identify the superpixels starting from coordinates of candidate pixels associated with the inconsistent flow vectors using a gradient map and intensity information from a first image of the two or more images. 33. The processor of any of clauses 29-32, wherein the one or more circuits are further configured to use a first criterion to group edge coordinates, determined from an edge detection operation, with neighbor edge coordinates and to use a second criterion to group non-edge coordinates with neighboring non-edge coordinates. 34. The processor of any of clauses 29-33, wherein the one or more circuits are further configured to traverse neighboring pixels of a candidate pixel using at least one of a first buffer or a second buffer and an array of neighbor coordinates, wherein the first buffer includes a first value wherein bits corresponding to the first value indicate current level coordinates using the array of neighbor coordinates, and wherein the second buffer includes a second value wherein bits corresponding to the second value indicate next level coordinates using the array of neighbor coordinates. 35. A processor comprising one or more circuits to: use one or more trained neural networks to process an input comprising one or more corrected optical flow vectors between two or more images to perform a machine vision task, the one or more optical flow vectors having been corrected using superpixels determined based on inconsistent flow vectors between the two or more images. 36. The processor of clause 35, wherein the one or more circuits are further to determine the one or more inconsistent flow vectors based on comparison of forward optical flow data from a first image to a second image in the two or more images and backward optical flow data from the second image to the first image. 37. The processor of clause 35 or clause 36, wherein the one or more circuits are further configured to identify the superpixels starting from coordinates of candidate pixels associated with the inconsistent flow vectors using a gradient map and intensity information from a first image of the two or more images. 38. The processor of any of clauses 35-37, wherein the one or more circuits are further configured to traverse neighboring pixels of a candidate pixel using at least one of a first buffer or a second buffer and an array of neighbor coordinates, wherein the first buffer includes a first value wherein bits corresponding to the first value indicate current level coordinates using the array of neighbor coordinates, and wherein the second buffer includes a second value wherein bits corresponding to the second value indicate next level coordinates using the array of neighbor coordinates. 10659] It will be understood that the present invention has been described above purely by way of example, and modifications of detail can be made within the scope of the invention.

[0660] Reference numerals appearing in the claims are by way of illustration only and shall have no limiting effect on the scope of the claims.

Claims

1. A processor comprising: one or more circuits to cause one or more pixels between two or more video frames to be generated based, at least in part, on motion predicted using one or more pixels within a first radius of the one or more pixels if one or more forward motion vectors corresponding to the one or more pixels is inconsistent with one or more backward motion vectors corresponding to the one or more pixels.

2. The processor of claim 1, wherein the one or more circuits are further to interpolate one or more intermediate frames based, at least in part, on the motion of the one or more pixels between two or more video frames.

3. The processor of claim 1 or claim 2, wherein the one or more circuits are further to determine candidate pixels associated with the first radius.

4. The processor of any preceding claim, wherein the one or more circuits are further to generate a gradient map for the one or more pixels between two or more video frames.

5. The processor of any preceding claim, wherein the one or more circuits are further configured to identify a plurality of superpixels starting from coordinates of candidate pixels associated with the first radius using a gradient map and intensity information from the one or more pixels.

6. The processor of any preceding claim, wherein the one or more circuits are further to use a first criterion to group edge coordinates, determined from an edge detection operation, with neighbor edge coordinates and to use a second criterion to group non-edge coordinates with neighboring non-edge coordinates.

7. The processor of any preceding claim, wherein the one or more circuits are further configured to identify a plurality of superpixels based on downsampled versions of the two or more video frames.

8. The processor of any preceding claim, wherein the one or more circuits are further to traverse neighboring pixels of a candidate pixel using at least one of a first buffer or a second buffer and an array of neighbor coordinates, wherein the first buffer includes a first value wherein bits corresponding to the first value indicate current level coordinates using the array of neighbor coordinates, and wherein the second buffer includes a second value wherein bits corresponding to the second value indicate next level coordinates using the array of neighbor coordinates.

9. The processor of any preceding claim, wherein the one or more circuits are further to cause motion of one or more pixels between two or more video frames to be predicted using a plurality of superpixels for the two or more video frames.

10. A system, comprisinga memory; andone or more circuits, the one or more circuits configured to:cause one or more pixels between two or more video frames to be generated based, at least in part, on motion predicted using one or more pixels within a first radius of the one or more pixels if one or more forward motion vectors corresponding to the one or more pixels is inconsistent with one or more backward motion vectors corresponding to the one or more pixels.

11. The system of claim 10, wherein the one or more circuits is configured to determine one or more inconsistent flow vectors based on a comparison of forward optical flow data from a first image to a second image in the one or more images and backward optical flow data from the second image to the first images.

12. The system of claim 10 or claim 11, wherein the one or more circuits are further to determine candidate pixels associated with coordinates of the one or more inconsistent flow vectors.

13. The system of any of claims 10-12, wherein the one or more circuits are further configured to generate a gradient map for a first image in the one or more images.

14. The system of any of claims 10-13, wherein the one or more circuits are further configured to identify one or more superpixels starting from coordinates of candidate pixels associated with the inconsistent flow vectors using a gradient map and intensity information from a first image of the one or more images.

15. The system of any of claims 10-14, wherein the one or more circuits are further configured to perform flows infilling or image infilling on the one or more images using one or more superpixels.

16. The system of any of claims 10-15, wherein the one or more circuits are further configured to identify one or more superpixels based on downsampled versions of the one or more images.

17. The system of any of claims 10-16, wherein the one or more circuits are further configured to traverse neighboring pixels of a candidate pixel using at least one of a first buffer or a second buffer and an array of neighbor coordinates, wherein the first buffer includes a first value wherein bits corresponding to the first value indicate current level coordinates using the array of neighbor coordinates, and wherein the second buffer includes a second value wherein bits corresponding to the second value indicate next level coordinates using the array of neighbor coordinates.

18. The system of any of claims 10-17, wherein the one or more circuits are further configured to interpolate one or more intermediate images from one or more images using one or more superpixels for the one or more images.

19. A method comprising:causing one or more pixels between two or more video frames to be generated based, at least in part, on motion predicted using one or more pixels within a first radius of the one or more pixels if one or more forward motion vectors corresponding to the one or more pixels is inconsistent with one or more backward motion vectors corresponding to the one or more pixels.

20. The method of claim 19, further comprising:interpolating one or more intermediate frames based, at least in part, on the motion of the one or more pixels between two or more video frames.

21. The method of claim 19 or claim 20, further comprising determining candidate pixels associated with coordinates of at least one of the one or more pixels between two or more video frames.

22. The method of any of claims 19-21, further comprising generating a gradient map for the one or more pixels between two or more video frames.

23. The method of any of claims 19-22, further comprising identifying a plurality of superpixels starting from coordinates of candidate pixels associated with the first radius using a gradient map and intensity information from a first image of the two or more images.

24. The method of any of claims 19-23, further comprising using a first criterion to group edge coordinates, determined from an edge detection operation, with neighbor edge coordinates and using a second criterion to group non-edge coordinates with neighboring non-edge coordinates.

25. The method of any of claims 19-24, further comprising identifying a plurality of superpixels based on downsampled versions of the two or more video frames.

26. The method of any of claims 19-25, further comprising traversing neighboring pixels of a candidate pixel using at least one of a first buffer or a second buffer and an array of neighbor coordinates, wherein the first buffer includes a first value wherein bits corresponding to the first value indicate current level coordinates using the array of neighbor coordinates, and wherein the second buffer includes a second value wherein bits corresponding to the second value indicate next level coordinates using the array of neighbor coordinates.

27. The method of any of claims 19-26, further comprising: traversing neighboring pixels of a candidate pixel using a second buffer and the array of neighbor coordinates, wherein the second buffer includes a second value wherein bits corresponding to the second value indicate next level coordinates using the array of neighbor coordinates.

28. The method of any of claims 19-27, further comprising: interpolating one or more intermediate images from two or more video frames using a plurality of superpixels for the two or more video frames.

29. A processor comprising one or more circuits totrain one or more neural networks to cause one or more pixels between two or more video frames to be generated based, at least in part, on motion predicted using one or more pixels within a first radius of the one or more pixels if one or more forward motion vectors corresponding to the one or more pixels is inconsistent with one or more backward motion vectors corresponding to the one or more pixels.

30. The processor of claim 29, wherein the one or more circuits are further to determine one or more inconsistent flow vectors based on comparison of forward optical flow data from a first image to a second image in the two or more images and backward optical flow data from the second image to the first image.

31. The processor of claim 29 or claim 30, wherein the one or more circuits are further to determine candidate pixels associated with coordinates of one or more inconsistent flow vectors.

32. The processor of any of claims 29-31, wherein the one or more circuits are further configured to identify superpixels starting from coordinates of candidate pixels associated with the inconsistent flow vectors using a gradient map and intensity information from a first image of the two or more images.

33. The processor of any of claims 29-32, wherein the one or more circuits are further configured to use a first criterion to group edge coordinates, determined from an edge detection operation, with neighbor edge coordinates and to use a second criterion to group non-edge coordinates with neighboring non-edge coordinates.

34. The processor of any of claims 29-33, wherein the one or more circuits are further configured to traverse neighboring pixels of a candidate pixel using at least one of a first buffer or a second buffer and an array of neighbor coordinates, wherein the first buffer includes a first value wherein bits corresponding to the first value indicate current level coordinates using the array of neighbor coordinates, and wherein the second buffer includes a second value wherein bits corresponding to the second value indicate next level coordinates using the array of neighbor coordinates.

35. A processor comprising one or more circuits to:use one or more trained neural networks to cause one or more pixels between two or more video frames to be generated based, at least in part, on motion predicted using one or more pixels within a first radius of the one or more pixels if one or more forward motion vectors corresponding to the one or more pixels is inconsistent with one or more backward motion vectors corresponding to the one or more pixels.

36. The processor of claim 35, wherein the one or more circuits are further to determine one or more inconsistent flow vectors based on comparison of forward optical flow data from a firstimage to a second image in the two or more images and backward optical flow data from the second image to the first image.

37. The processor of claim 35 or claim 36, wherein the one or more circuits are further configured to identify superpixels starting from coordinates of candidate pixels associated with inconsistent flow vectors using a gradient map and intensity information from a first image of the two or more images.

38. The processor of any of claims 35-37, wherein the one or more circuits are further configured to traverse neighboring pixels of a candidate pixel using at least one of a first buffer or a second buffer and an array of neighbor coordinates, wherein the first buffer includes a first value wherein bits corresponding to the first value indicate current level coordinates using the array of neighbor coordinates, and wherein the second buffer includes a second value wherein bits corresponding to the second value indicate next level coordinates using the array of neighbor coordinates.

Images