Multi-task learning for organ surface and landmark prediction for rigid and deformable registration in augmented reality pipelines

Abstract

Examples described herein provide a computer-implemented method that includes receiving, by an MTL model, a laparoscopic image of a first organ; and reproducing, by the MTL model, the laparoscopic image such that one or more of a silhouette and surface of a first organ is graphically differentiated from adjacent organs and biological structures; training the landmark head, of the MTL model that has a transformer encoder, the landmark head and a surface head, for predicting the ridges and silhouette of the first organ from the laparoscopic image of the first organ; training the surface head for segmenting the first organ from the adjacent organs and biological structures from the laparoscopic image of the first organ; applying the transformer encoder followed by multi-resolution feature interpolation and stacking, and per-head feature convolutions; and applying a homoscedastic weighting of per-task losses, to balance losses using the predictive uncertainty of each task.

Description

MULTI-TASK LEARNING FOR ORGAN SURFACE AND LANDMARKPREDICTION FOR RIGID AND DEFORMABLE REGISTRATION IN AUGMENTEDREALITY PIPELINESCROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims the benefit of U.S. Provisional Patent Application Serial No. 63 / 655,366, filed June 3, 2024, the entire content of which is incorporated herein by reference.BACKGROUND

[0002] The present disclosure relates in general to computing technology and relates more particularly to computing technology for multi-task learning for organ surface and landmark prediction for rigid and deformable registration in augmented reality pipelines.

[0003] Computer-assisted systems, particularly computer-assisted surgery systems (CASs), rely on video data digitally captured during a surgery. Such video data can be stored and / or streamed. In some cases, the video data can be used to augment a person’s physical sensing, perception, and reaction capabilities. For example, such systems can effectively provide the information corresponding to an expanded field of vision, both temporal and spatial, that enables a person to adjust current and future actions based on the part of an environment not included in his or her physical field of view. Alternatively, or in addition, the video data can be stored and / or transmitted for several purposes, such as archival, training, post-surgery analysis, and / or patient consultation.

[0004] Augmented reality (AR) systems that overlay patient-specific 3D soft-tissue organ models derived from preoperative imaging, onto the live endoscopic view of the organ, can enable surgeons to visualize internal structures, such as tumors and vessels, intraoperatively. Rigid and deformable registration are relatively important components of such AR systems. Surface landmarks have been shown to constrain the rigid alignment when only partial intraoperative views of the organ are available due to the limited endoscopic viewpoint.SUMMARY

[0005] According to an aspect, a computer-implemented method is provided. The method includes receiving in a machine learning system a laparoscopic image of a first organ; and reproducing by the machine learning system the laparoscopic image such that one or more of a silhouette and surface of a first organ is graphically differentiated from adjacent organs and biological structures.

[0006] According to another aspect, a system includes a data store including video data associated with a surgical procedure; and a machine learning training system configured to: receive a laparoscopic image of a first organ; and reproduce the laparoscopic image such that one or more of a silhouette and surface of a first organ is graphically differentiated from adjacent organs and biological structures

[0007] According to an aspect, a computer program product is provided, including a memory device having computer executable instructions stored thereon, which when executed by one or more processors cause the one or more processors to perform a plurality of operations including: receiving in a machine learning training system a laparoscopic image of a first organ; and reproducing by the machine learning training system the laparoscopic image such that one or more of a silhouette and surface of a first organ is graphically differentiated from adjacent organs and biological structures. .

[0008] The above features and advantages, and other features and advantages, of the disclosure are readily apparent from the following detailed description when taken in connection with the accompanying drawings.BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The specifics of the exclusive rights described herein are particularly pointed out and distinctly claimed in the claims at the conclusion of the specification. The foregoing and other features and advantages of the aspects of the disclosure are apparent from the following detailed description taken in conjunction with the accompanying drawings in which:

[0010] FIG. 1 depicts a computer-assisted surgery (CAS) system according to one or more aspects;

[0011] FIG. 2 depicts a surgical procedure system according to one or more aspects;

[0012] FIG. 3 depicts a system for analyzing video and data according to one or more aspects;

[0013] FIG. 4A depicts an MTL framework for landmark and surface prediction of biological organs and structures according to one or more aspects;

[0014] FIG. 4B depicts an example MTL model output frame, landmark prediction, and surface segmentation of the biological organs and structures according to one or more aspects;

[0015] FIG. 5 A is a graph depicting per surface / landmark loU distributions for a liver, according to one or more aspects;

[0016] FIG. 5B is a graph depicting per surface / landmark loU distributions for a gallbladder, according to one or more aspects;

[0017] FIG. 5C is a graph depicting per surface / landmark loU distributions for a falciform ligament, according to one or more aspects;

[0018] FIG. 5D is a graph depicting per surface / landmark loU distributions for an anterior ridge of the liver, according to one or more aspects;

[0019] FIG. 5E is a graph depicting per surface / landmark loU distributions for a silhouette of the liver, according to one or more aspects;

[0020] FIG. 6 depicts example surface and landmark ground-truth (GT) annotations and predictions related to the biological organs and structures overlay ed on frames from hepatectomy procedures, according to one or more aspects;

[0021] FIG. 7A depicts a flowchart of a method of processing a laparoscopic image of a first organ with a machine learning training system is generally shown, according to one or more aspects;

[0022] FIGS. 7B depicts a flowchart of a method of training an MTL framework for landmark and surface prediction of biological organs and structures, according to one or more aspects;

[0023] FIGS. 7C depicts a flowchart of additional aspects of the method of training the MTL framework for landmark and surface prediction of biological organs and structures, according to one or more aspects; and

[0024] FIG. 8 depicts a block diagram of a computer system according to one or more aspects.

[0025] The diagrams depicted herein are illustrative. There can be many variations to the diagrams and / or the operations described herein without departing from the spirit of the described aspects. For instance, the actions can be performed in a differing order, or actions can be added, deleted, or modified. Also, the term “coupled” and variations thereof describe having a communications path between two elements and do not imply a direct connection between the elements with no intervening elements / connections between them. All of these variations are considered a part of the specification.DETAILED DESCRIPTION

[0026] Exemplary aspects of the technical solutions described herein include systems and methods for multi-task learning for organ surface and landmark prediction for rigid and deformable registration in augmented reality pipelines.

[0027] As indicated, augmented reality (AR) systems that overlay patient-specific 3D soft-tissue organ models derived from preoperative imaging, onto the live endoscopic view of the organ, can enable surgeons to visualize internal structures, such as tumors and vessels, intraoperatively. Rigid and deformable registration are relatively important components of such AR systems. As further indicated, surface landmarks have been shown to constrain the rigid alignment when only partial intraoperative views of the organ are available due to the limited endoscopic viewpoint. The disclosed aspects present a multi-task learning (MTL) framework that jointly predicts surface landmarks and segments the visible organ surface, to enable landmark-guided rigid alignment and stereo reconstruction, for deformable registration, respectively. The disclosure evaluates themulti-task learning framework on surgical frames from liver procedures where the case for AR is established.

[0028] Technical solutions are described herein to address such technical challenges.

[0029] Turning now to FIG. 1, an example computer-assisted system (CAS) system 100 is generally shown in accordance with one or more aspects. The CAS system 100 includes at least a computing system 102, a video recording system 104, and a surgical instrumentation system 106. As illustrated in FIG. 1, an actor 112 can be medical personnel that uses the CAS system 100 to perform a surgical procedure on a patient 110. Medical personnel can be a surgeon, assistant, nurse, administrator, or any other actor that interacts with the CAS system 100 in a surgical environment. The surgical procedure can be any type of surgery, such as but not limited to cataract surgery, laparoscopic cholecystectomy, endoscopic endonasal transsphenoidal approach (eTSA) to resection of pituitary adenomas, or any other surgical procedure. In other examples, actor 112 can be a technician, an administrator, an engineer, or any other such personnel that interacts with the CAS system 100. For example, actor 112 can record data from the CAS system 100, configure / update one or more attributes of the CAS system 100, review past performance of the CAS system 100, repair the CAS system 100, and / or the like including combinations and / or multiples thereof.

[0030] A surgical procedure can include multiple phases, and each phase can include one or more surgical actions. A “surgical action” can include an incision, a compression, a stapling, a clipping, a suturing, a cauterization, a sealing, or any other such actions performed to complete a phase in the surgical procedure. A “phase” represents a surgical event that is composed of a series of steps (e.g., closure). A “step” refers to the completion of a named surgical objective (e.g., hemostasis). During each step, certain surgical instruments 108 (e.g., forceps) are used to achieve a specific objective by performing one or more surgical actions. In addition, a particular anatomical structure of the patient may be the target of the surgical action(s).

[0031] The video recording system 104 includes one or more cameras 105, such as operating room cameras, endoscopic cameras, and / or the like including combinations and / or multiples thereof. The cameras 105 capture video data of the surgical procedurebeing performed. The video recording system 104 includes one or more video capture devices that can include cameras 105 placed in the surgical room to capture events surrounding (i.e., outside) the patient being operated upon. The video recording system 104 further includes cameras 105 that are passed inside (e.g., endoscopic cameras) the patient 110 to capture endoscopic data. The endoscopic data provides video and images of the surgical procedure.

[0032] The computing system 102 includes one or more memory devices, one or more processors, a user interface device, among other components. All or a portion of the computing system 102 shown in FIG. 1 can be implemented for example, by all or a portion of computer system 800 of FIG. 8. Computing system 102 can execute one or more computer-executable instructions. The execution of the instructions facilitates the computing system 102 to perform one or more methods, including those described herein. The computing system 102 can communicate with other computing systems via a wired and / or a wireless network. In one or more examples, the computing system 102 includes one or more trained machine learning models that can detect and / or predict features of / from the surgical procedure that is being performed or has been performed earlier.Features can include structures, such as anatomical structures, surgical instruments 108 in the captured video of the surgical procedure. Features can further include events, such as phases and / or actions in the surgical procedure. Features that are detected can further include the actor 112 and / or patient 110. Based on the detection, the computing system 102, in one or more examples, can provide recommendations for subsequent actions to be taken by the actor 112. Alternatively, or in addition, the computing system 102 can provide one or more reports based on the detections. The detections by the machine learning models can be performed in an autonomous or semi-autonomous manner.

[0033] The machine learning models can include artificial neural networks, such as deep neural networks, convolutional neural networks, recurrent neural networks, vision transformers, encoders, decoders, or any other type of machine learning model. The machine learning models can be trained in a supervised, unsupervised, or hybrid manner. The machine learning models can be trained to perform detection and / or prediction using one or more types of data acquired by the CAS system 100. For example, the machine learning models can use the video data captured via the video recording system 104.Alternatively, or in addition, the machine learning models use the surgical instrumentation data from the surgical instrumentation system 106. In yet other examples, the machine learning models use a combination of video data and surgical instrumentation data.

[0034] Additionally, in some examples, the machine learning models can also use audio data captured during the surgical procedure. The audio data can include sounds emitted by the surgical instrumentation system 106 while activating one or more surgical instruments 108. Alternatively, or in addition, the audio data can include voice commands, snippets, or dialog from one or more actors 112. The audio data can further include sounds made by the surgical instruments 108 during their use.

[0035] In one or more examples, the machine learning models can detect surgical actions, surgical phases, anatomical structures, surgical instruments, and various other features from the data associated with a surgical procedure. The detection can be performed in realtime in some examples. Alternatively, or in addition, the computing system 102 analyzes the surgical data, i.e., the various types of data captured during the surgical procedure, in an offline manner (e.g., post-surgery). In one or more examples, the machine learning models detect surgical phases based on detecting some of the features, such as the anatomical structure, surgical instruments, and / or the like including combinations and / or multiples thereof.

[0036] A data collection system 150 can be employed to store the surgical data, including the video(s) captured during the surgical procedures. The data collection system 150 includes one or more storage devices 152. The data collection system 150 can be a local storage system, a cloud-based storage system, or a combination thereof. Further, the data collection system 150 can use any type of cloud-based storage architecture, for example, public cloud, private cloud, hybrid cloud, and / or the like including combinations and / or multiples thereof. In some examples, the data collection system can use a distributed storage, i.e., the storage devices 152 are located at different geographic locations. The storage devices 152 can include any type of electronic data storage media used for recording machine-readable data, such as semiconductor-based, magnetic-based, opticalbased storage media, and / or the like including combinations and / or multiples thereof. For example, the data storage media can include flash-based solid-state drives (SSDs),magnetic-based hard disk drives, magnetic tape, optical discs, and / or the like including combinations and / or multiples thereof.

[0037] In one or more examples, the data collection system 150 can be part of the video recording system 104, or vice-versa. In some examples, the data collection system 150, the video recording system 104, and the computing system 102, can communicate with each other via a communication network, which can be wired, wireless, or a combination thereof. The communication between the systems can include the transfer of data (e.g., video data, instrumentation data, and / or the like including combinations and / or multiples thereof), data manipulation commands (e.g., browse, copy, paste, move, delete, create, compress, and / or the like including combinations and / or multiples thereof), data manipulation results, and / or the like including combinations and / or multiples thereof. In one or more examples, the computing system 102 can manipulate the data already stored / being stored in the data collection system 150 based on outputs from the one or more machine learning models (e.g., phase detection, anatomical structure detection, surgical tool detection, and / or the like including combinations and / or multiples thereof). Alternatively, or in addition, the computing system 102 can manipulate the data already stored / being stored in the data collection system 150 based on information from the surgical instrumentation system 106.

[0038] In one or more examples, the video captured by the video recording system 104 is stored on the data collection system 150. In some examples, the computing system 102 curates parts of the video data being stored on the data collection system 150. In some examples, the computing system 102 filters the video captured by the video recording system 104 before it is stored on the data collection system 150. Alternatively, or in addition, the computing system 102 filters the video captured by the video recording system 104 after it is stored on the data collection system 150.

[0039] Turning now to FIG. 2, a surgical procedure system 200 is generally shown according to one or more aspects. The example of FIG. 2 depicts a surgical procedure support system 202 that can include or may be coupled to the CAS system 100 of FIG. 1. The surgical procedure support system 202 can acquire image or video data using one or more cameras 204. The surgical procedure support system 202 can also interface with oneor more sensors 206 and / or one or more effectors 208. The sensors 206 may be associated with surgical support equipment and / or patient monitoring. The effectors 208 can be robotic components or other equipment controllable through the surgical procedure support system 202. The surgical procedure support system 202 can also interact with one or more user interfaces 210, such as various input and / or output devices. The surgical procedure support system 202 can store, access, and / or update surgical data 214 associated with a training dataset and / or live data as a surgical procedure is being performed on patient 110 of FIG. 1. The surgical procedure support system 202 can store, access, and / or update surgical objectives 216 to assist in training and guidance for one or more surgical procedures. User configurations 218 can track and store user preferences.

[0040] Turning now to FIG. 3, a system 300 for analyzing video and data is generally shown according to one or more aspects. In accordance with aspects, the video and data is captured from video recording system 104 of FIG. 1. The analysis can result in predicting features that include surgical phases and structures (e.g., instruments, anatomical structures, and / or the like including combinations and / or multiples thereof) in the video data using machine learning. System 300 can be the computing system 102 of FIG. 1, or a part thereof in one or more examples. System 300 uses data streams in the surgical data to identify procedural states according to some aspects.

[0041] System 300 includes a data reception system 305 that collects surgical data, including the video data and surgical instrumentation data. The data reception system 305 can include one or more devices (e.g., one or more user devices and / or servers) located within and / or associated with a surgical operating room and / or control center. The data reception system 305 can receive surgical data in real-time, i.e., as the surgical procedure is being performed. Alternatively, or in addition, the data reception system 305 can receive or access surgical data in an offline manner, for example, by accessing data that is stored in the data collection system 150 of FIG. 1.

[0042] System 300 further includes a machine learning processing system 310 that processes the surgical data using one or more machine learning models to identify one or more features, such as surgical phase, instrument, anatomical structure, and / or the like including combinations and / or multiples thereof, in the surgical data. It will be appreciatedthat machine learning processing system 310 can include one or more devices (e.g., one or more servers), each of which can be configured to include part or all of one or more of the depicted components of the machine learning processing system 310. In some instances, a part or all of the machine learning processing system 310 is cloud-based and / or remote from an operating room and / or physical location corresponding to a part or all of data reception system 305. It will be appreciated that several components of the machine learning processing system 310 are depicted and described herein. However, the components are just one example structure of the machine learning processing system 310, and that in other examples, the machine learning processing system 310 can be structured using a different combination of the components. Such variations in the combination of the components are encompassed by the technical solutions described herein.

[0043] The machine learning processing system 310 includes a machine learning training system 325, which can be a separate device (e.g., server) that stores its output as one or more trained machine learning models 330. The machine learning models 330 are accessible by a machine learning execution system 340. The machine learning execution system 340 can be separate from the machine learning training system 325 in some examples. In other words, in some aspects, devices that “train” the models are separate from devices that “infer,” i.e., perform real-time processing of surgical data using the trained machine learning models 330.

[0044] Machine learning processing system 310, in some examples, further includes a data generator 315 to generate simulated surgical data, such as a set of synthetic images and / or synthetic video, in combination with real image and video data from the video recording system 104, to generate trained machine learning models 330. Data generator 315 can access (read / write) a data store 320 to record data, including multiple images and / or multiple videos. The images and / or videos can include images and / or videos collected during one or more procedures (e.g., one or more surgical procedures). For example, the images and / or video may have been collected by a user device worn by the actor 112 of FIG. 1 (e.g., surgeon, surgical nurse, anesthesiologist, and / or the like including combinations and / or multiples thereof) during the surgery, a non-wearable imaging device located within an operating room, an endoscopic camera inserted inside the patient 110 of FIG. 1, and / or the like including combinations and / or multiples thereof. The data store 320is separate from the data collection system 150 of FIG. 1 in some examples. In other examples, the data store 320 is part of the data collection system 150.

[0045] Each of the images and / or videos recorded in the data store 320 for performing training (e.g., generating the machine learning models 330) can be defined as a base image and can be associated with other data that characterizes an associated procedure and / or rendering specifications. For example, the other data can identify a type of procedure, a location of a procedure, one or more people involved in performing the procedure, surgical objectives, and / or an outcome of the procedure. Alternatively, or in addition, the other data can indicate a stage of the procedure with which the image or video corresponds, rendering specification with which the image or video corresponds and / or a type of imaging device that captured the image or video (e.g., and / or, if the device is a wearable device, a role of a particular person wearing the device, and / or the like including combinations and / or multiples thereof). Further, the other data can include imagesegmentation data that identifies and / or characterizes one or more objects (e.g., tools, anatomical objects, and / or the like including combinations and / or multiples thereof) that are depicted in the image or video. The characterization can indicate the position, orientation, or pose of the object in the image. For example, the characterization can indicate a set of pixels that correspond to the object and / or a state of the object resulting from a past or current user handling. Localization can be performed using a variety of techniques for identifying objects in one or more coordinate systems.

[0046] The machine learning training system 325 uses the recorded data in the data store 320, which can include the simulated surgical data (e.g., set of synthetic images and / or synthetic video) and / or actual surgical data to generate the trained machine learning models 330. The trained machine learning models 330 can be defined based on a type of model and a set of hyperparameters (e.g., defined based on input from a client device). The trained machine learning models 330 can be configured based on a set of parameters that can be dynamically defined based on (e.g., continuous or repeated) training (i.e., learning, parameter tuning). Machine learning training system 325 can use one or more optimization algorithms to define the set of parameters to minimize or maximize one or more loss functions. The set of (learned) parameters can be stored as part of the trained machine learning models 330 using a specific data structure for a particular trainedmachine learning model of the trained machine learning models 330. The data structure can also include one or more non-leamable variables (e.g., hyperparameters and / or model definitions).

[0047] Machine learning execution system 340 can access the data structure(s) of the trained machine learning models 330 and accordingly configure the trained machine learning models 330 for inference (e.g., prediction, classification, and / or the like including combinations and / or multiples thereof). The trained machine learning models 330 can include, for example, a fully convolutional network adaptation, an adversarial network model, an encoder, a decoder, or other types of machine learning models. The type of the trained machine learning models 330 can be indicated in the corresponding data structures. The trained machine learning models 330 can be configured in accordance with one or more hyperparameters and the set of learned parameters.

[0048] The trained machine learning models 330, during execution, receive, as input, surgical data to be processed and subsequently generate one or more inferences according to the training. For example, the video data captured by the video recording system 104 of FIG. 1 can include data streams (e.g., an array of intensity, depth, and / or RGB values) for a single image or for each of a set of frames (e.g., including multiple images or an image with sequencing data) representing a temporal window of fixed or variable length in a video. The video data that is captured by the video recording system 104 can be received by the data reception system 305, which can include one or more devices located within an operating room where the surgical procedure is being performed. Alternatively, the data reception system 305 can include devices that are located remotely, to which the captured video data is streamed live during the performance of the surgical procedure.Alternatively, or in addition, the data reception system 305 accesses the data in an offline manner from the data collection system 150 or from any other data source (e.g., local or remote storage device).

[0049] The data reception system 305 can process the video and / or data received. The processing can include decoding when a video stream is received in an encoded format such that data for a sequence of images can be extracted and processed. The data reception system 305 can also process other types of data included in the input surgical data. Forexample, the surgical data can include additional data streams, such as audio data, RFID data, textual data, measurements from one or more surgical instruments / sensors, and / or the like including combinations and / or multiples thereof, that can represent stimuli / procedural states from the operating room. The data reception system 305 synchronizes the different inputs from the different devices / sensors before inputting them in the machine learning processing system 310.

[0050] The trained machine learning models 330, once trained, can analyze the input surgical data, and in one or more aspects, predict and / or characterize features (e.g., structures) included in the video data included with the surgical data. The video data can include sequential images and / or encoded video data (e.g., using digital video file / stream formats and / or codecs, such as MP4, MOV, AVI, WEBM, AVCHD, OGG, and / or the like including combinations and / or multiples thereof). The prediction and / or characterization of the features can include segmenting the video data or predicting the localization of the structures with a probabilistic heatmap. In some instances, the one or more trained machine learning models 330 include or are associated with a preprocessing or augmentation (e.g., intensity normalization, resizing, cropping, and / or the like including combinations and / or multiples thereof) that is performed prior to segmenting the video data. An output of the one or more trained machine learning models 330 can include image-segmentation or probabilistic heatmap data that indicates which (if any) of a defined set of structures are predicted within the video data, a location and / or position and / or pose of the structure(s) within the video data, and / or state of the structure(s). The location can be a set of coordinates in an image / frame in the video data. For example, the coordinates can provide a bounding box. The coordinates can provide boundaries that surround the structure(s) being predicted. The trained machine learning models 330, in one or more examples, are trained to perform higher-level predictions and tracking, such as predicting a phase of a surgical procedure and tracking one or more surgical instruments used in the surgical procedure.

[0051] While some techniques for predicting a surgical phase (“phase”) in the surgical procedure are described herein, it should be understood that any other technique for phase prediction can be used without affecting the aspects of the technical solutions described herein. In some examples, the machine learning processing system 310 includes a detector350 that uses the trained machine learning models 330 to identify various items or states within the surgical procedure (“procedure”). The detector 350 can use a particular procedural tracking data structure 355 from a list of procedural tracking data structures. The detector 350 can select the procedural tracking data structure 355 based on the type of surgical procedure that is being performed. In one or more examples, the type of surgical procedure can be predetermined or input by actor 112. For instance, the procedural tracking data structure 355 can identify a set of potential phases that can correspond to a part of the specific type of procedure as “phase predictions”, where the detector 350 is a phase detector.

[0052] In some examples, the procedural tracking data structure 355 can be a graph that includes a set of nodes and a set of edges, with each node corresponding to a potential phase. The edges can provide directional connections between nodes that indicate (via the direction) an expected order during which the phases will be encountered throughout an iteration of the procedure. The procedural tracking data structure 355 may include one or more branching nodes that feed to multiple next nodes and / or can include one or more points of divergence and / or convergence between the nodes. In some instances, a phase indicates a procedural action (e.g., surgical action) that is being performed or has been performed and / or indicates a combination of actions that have been performed. In some instances, a phase relates to a biological state of a patient undergoing a surgical procedure. For example, the biological state can indicate a complication (e.g., blood clots, clogged arteries / veins, and / or the like including combinations and / or multiples thereof), precondition (e.g., lesions, polyps, and / or the like including combinations and / or multiples thereof). In some examples, the trained machine learning models 330 are trained to detect an “abnormal condition,” such as hemorrhaging, arrhythmias, blood vessel abnormality, and / or the like including combinations and / or multiples thereof.

[0053] Each node within the procedural tracking data structure 355 can identify one or more characteristics of the phase corresponding to that node. The characteristics can include visual characteristics. In some instances, the node identifies one or more tools that are typically in use or available for use (e.g., on a tool tray) during the phase. The node also identifies one or more roles of people who are typically performing a surgical task, a typical type of movement (e.g., of a hand or tool), and / or the like including combinationsand / or multiples thereof. Thus, detector 350 can use the segmented data generated by machine learning execution system 340 that indicates the presence and / or characteristics of particular objects within a field of view to identify an estimated node to which the real image data corresponds. Identification of the node (i. e. , phase) can further be based upon previously detected phases for a given procedural iteration and / or other detected input (e.g., verbal audio data that includes person-to-person requests or comments, explicit identifications of a current or past phase, information requests, and / or the like including combinations and / or multiples thereof).

[0054] The detector 350 can output predictions, such as a phase prediction associated with a portion of the video data that is analyzed by the machine learning processing system 310. The phase prediction is associated with the portion of the video data by identifying a start time and an end time of the portion of the video that is analyzed by the machine learning execution system 340. The phase prediction that is output can include segments of the video where each segment corresponds to and includes an identity of a surgical phase as detected by the detector 350 based on the output of the machine learning execution system 340. Further, the phase prediction, in one or more examples, can include additional data dimensions, such as, but not limited to, identities of the structures (e.g., instrument, anatomy, and / or the like including combinations and / or multiples thereof) that are identified by the machine learning execution system 340 in the portion of the video that is analyzed. The phase prediction can also include a confidence score of the prediction. Other examples can include various other types of information in the phase prediction that is output. Further, other types of outputs of the detector 350 can include state information or other information used to generate audio output, visual output, and / or commands. For instance, the output can trigger an alert, an augmented visualization, identify a predicted current condition, identify a predicted future condition, command control of equipment, and / or result in other such data / commands being transmitted to a support system component, e.g., through surgical procedure support system 202 of FIG. 2.

[0055] It should be noted that although some of the drawings depict endoscopic videos being analyzed, the technical solutions described herein can be applied to analyze video and image data captured by cameras that are not endoscopic (i.e. , cameras external to the patient’s body) when performing open surgeries (i.e., not laparoscopic surgeries). Forexample, the video and image data can be captured by cameras that are mounted on one or more personnel in the operating room (e.g., surgeon). Alternatively, or in addition, the cameras can be mounted on surgical instruments, walls, or other locations in the operating room. Alternatively, or in addition, the video can be images captured by other imaging modalities, such as ultrasound.

[0056] Turning now to FIG. 4A, according to one or more aspects, the figure shows an MTL framework for landmark and surface prediction of biological organs and structures.

[0057] According to the aspects, a multi-task learning (MTL) model 4A10 (which may generally be considered a machine learning training system) was trained with a plurality (two) of prediction heads. A landmark head 4A20 and a surface head 4A30. The landmark head 4A20 was utilized for predicting the anterior ridge 4A21 and silhouette 4A22 of the liver from a laparoscopic image 4A40 of the liver 4A1. The surface head 4A30 was utilized for segmenting the liver, from the image 4A40, compared with the gallbladder 4A2, and falciform ligament 4A3, e.g., by overlaying each with a separate and distinct surface shading or color.

[0058] The model utilizes a Swin Base transformer encoder 4A50 followed by multiresolution feature interpolation and stacking, and per-head feature convolutions. It is to be appreciated that multi -resolution features are a typical output of a Swin transformer, and that interpolation and stacking are typical extensions to enable segmentation. It is to be appreciated that other transformer encoders may be utilized within the scope of the aspects as described herein. In addition, for training, a homoscedastic weighting of per-task losses (e.g., a typical loss weighting strategy) was utilized, which balances losses using the predictive uncertainty of each task.

[0059] In experiments, MTL model performance was compared with single-task models. Landmark predictions were compared as a segmentation task and a distance map regression task. Intersection over Union (loU) and image-level detection Fl score (>25% overlap for true positive) were recorded in evaluations.

[0060] FIG. 4B depicts an example MTL model output frame (first) image 4C1, a landmark prediction (second) image 4C2, and a surface segmentation (third) image 4C3 ofthe biological organs and structures, according to one or more aspects. In the landmark prediction 4C2, a colored edge line 4C21 is provided around the liver 4A1. In the surface segmentation a colored surface 4C22 is overlay ed on the liver 4A1.

[0061] As an example, 5000 frames from hepatectomy videos were labelled by annotators with clinical oversight, and split by video into train, validation, and test (70: 15:15). An additional 3300 frames from cholecystectomy and gastrectomy were used to augment the training set. In experiments, greater stability was observed from training the landmark task as segmentation rather than distance map regression.

[0062] On validation and test set frames (18 videos), a 1-2% improvement in loU and detection Fl was observed for landmark and surface segmentation, for MTL compared with single-task training. Mean loU and detection Fl for each structure are shown in Table 1, below.

[0063] Table 1: Per surface / landmark loU and detection Fl.

[0064] For the anterior ridge and silhouette landmarks, loUs of 32% and 51%, and detection FIs of 56% and 85%, were observed. For the liver, gallbladder, and falciform ligament surface segmentations, loUs of 93%, 86%, and 73%, and detection FIs of 99%, 86%, and 88%, were recorded.

[0065] FIG. 5A is a graph depicting per surface / landmark loU distributions 5A1 for the liver, according to one or more aspects. The figure shows loU in the horizontal axis 5Ax and a number of frames in the vertical axis 5Ay. The loU increases with increasing frames.

[0066] FIG. 5B is a graph depicting per surface / landmark loU distributions 5B1 for the gallbladder, according to one or more aspects. The figure shows loU in the horizontal axis 5Bx and a number of frames in the vertical axis 5By.

[0067] FIG. 5C is a graph depicting per surface / landmark loU distributions 5C1 for the falciform ligament, according to one or more aspects. The figure shows loU in the horizontal axis 5Cx and a number of frames in the vertical axis 5Cy.

[0068] FIG. 5D is a graph depicting per surface / landmark loU distributions 5D1 for the anterior ridge of the liver, according to one or more aspects. The figure shows loU in the horizontal axis 5Dx and a number of frames in the vertical axis 5Dy.

[0069] FIG. 5E is a graph depicting per surface / landmark loU distributions 5E1 for the silhouette of the liver, according to one or more aspects. The figure shows loU in the horizontal axis 5Ex and a number of frames in the vertical axis 5Ey.

[0070] For each of FIGS. 5 A to 5E, a greater right skew reflects a stronger performance on the anatomy class. A greater left skew reflects a weaker performance.

[0071] FIG. 6 depicts example surface and landmark ground-truth (GT) annotations and predictions related to the biological organs and structures overlayed on frames from hepatectomy procedures according to one or more aspects. The figure shows three rows 6R1-6R3 of images representing three different procedures on livers.

[0072] Each row has five images. The images include a first image 6F1 showing a frame image, a second image 6F2 showing a surface GT image and a third image 6F3 showing a surface prediction image. The images also include a fourth image 6F4 showing a landmark GT image and a fifth image 6F5 showing a landmark predication image.

[0073] Each row has an accompanying row of loU values for each respective image, which is shown below the respective image, such that each row includes a five loU values.The loU values include a first loU value 6V1 for the frame image 6F1, a second loU value 6V2 for the surface GT image 6F2 and a third loU value 6V3 for the surface prediction image 6F3. The loU values include a fourth loU value 6V4 for the landmark GT image 6F4 and a fifth loU value 6V5 for the landmark prediction image 6F5.

[0074] As shown in FIG. 6, in the first row 6R1, the loU values, from first to fifth images 6F1 to 6F5, are 0.86, 0.41, 0.86, 0.26, and 0.34. In the second row 6R2, the loU values, from first to fifth images 6F1 to 6F5, are 0.94, 0.76, 0.81, 0.52, and 0.80. In the third row 6R3, the loU values, from first to fifth images 6F1 to 6F5, are 0.88, 0.63, 0.92, 0.55, and 0.42.

[0075] In viewing the values of table 1, the graph values of FIGS. 5A-5D and the loU values shown in FIG. 6, it can be seen that low values of loU for landmarks can correspond to sufficient characterization.

[0076] Turning now to FIG. 7A, a method 700 of processing a laparoscopic image of a first organ 4A1 with an MTL model (which may also be generally considered a machine learning training system, as indicated above) 4A10 is generally shown in accordance with one or more aspects. Aspects of the method 700 are shown in FIG. 4A and discussed above. All or a portion of method 700 can be implemented, for example, by all or a portion of CAS system 100 of FIG. 1 and / or computer system 800 of FIG. 8.

[0077] At block 702, the method includes receiving by the MTL model 4A10 the laparoscopic image 4A40 of a first organ 4A1. As shown in block 704 the method includes reproducing by the MTL model 4A10 the laparoscopic image such that one or more of a silhouette 4A22 and surface 4A21 of the first organ 4A1 is graphically differentiated from adjacent organs 4A2 and biological structures 4 A3.

[0078] Turning now to FIG. 7B, a method 700A of training the MTL model 4A10 is generally shown in accordance with one or more aspects. Aspects of the method 700A are shown in FIG. 4A and discussed above. As shown in block 706 the method includes training a landmark head 4A20, of the MTL model 4A10, for predicting the ridges 4A21 and silhouette 4A22 of a first organ (e.g., a liver) from a laparoscopic image 4A40 of the first organ. As indicated, the MLT model 4A10 has a transformer encoder 4A50, thelandmark head 4A20 and a surface head 4A30. As shown in block 708 the method includes training the surface head 4A30 for segmenting the first organ 4A1 from adjacent organs 4A2 and biological structures 4A3 (e.g., gallbladder 4A2 and falciform ligament 4A3) from the laparoscopic image 4A40 of the first organ 4A1.

[0079] Block 704A provides additional details of processing the laparoscopic image 4A40 disclosed in block 704, above. As shown in block 704A, the method includes processing the laparoscopic image 4A40 with the MTL model 4A10, through the transformer encoder 4A50 and one or both of the landmark head 4A20 and the surface head 4A30. This processing reproduces the laparoscopic image 4A40 such that one or more of the silhouette 4A22 and the surface 4A21 of the first organ 4A1 is graphically differentiated from adjacent organs 4A2 and biological structures 4A3.

[0080] Turning to FIG. 7C, additional aspects of the method of training the MTL model 4A10 are shown. As shown in block 710 the method includes applying the transformer encoder 4A50 (e.g., a Swin Base transformer encoder) followed by multi-resolution feature interpolation and stacking, and per-head feature convolutions. As shown in block 712 the method includes applying a homoscedastic weighting of per-task losses, to balance losses using the predictive uncertainty of each task.

[0081] The processing shown in FIGS. 7A and 7B are not intended to indicate that the operations are to be executed in any particular order or that all of the operations shown in FIGS. 7A and 7B are to be included in every case. Additionally, the processing shown in FIGS. 7 A and 7B can include any suitable number of additional operations.

[0082] According to an aspect of the disclosure, a computer-implemented method includes receiving in a machine learning system a laparoscopic image of a first organ; and reproducing by the machine learning system the laparoscopic image such that one or more of a silhouette and surface of a first organ is graphically differentiated from adjacent organs and biological structures.

[0083] According to another aspect of the disclosure, directed to the computer- implemented method, the method further includes training a landmark head, of an MTLmodel that has a transformer encoder, the landmark head and a surface head, for predicting the ridges and silhouette of the first organ from the laparoscopic image of the first organ.

[0084] According to another aspect of the disclosure, directed to the computer- implemented method, the method further includes training the surface head for segmenting the first organ from the adjacent organs and biological structures from the laparoscopic image of the first organ.

[0085] According to another aspect of the disclosure, directed to the computer- implemented method, the method further includes processing the laparoscopic image with the MTL model, through the transformer encoder and one or both of the landmark head and the surface head to reproduce the laparoscopic image such that one or more of the silhouette and surface of the first organ is graphically differentiated from adjacent organs and biological structures.

[0086] According to another aspect of the disclosure, directed to the computer- implemented method, the method further includes applying the transformer encoder followed by multi-resolution feature interpolation and stacking, and per-head feature convolutions.

[0087] According to another aspect of the disclosure, directed to the computer- implemented method, the method further includes applying a homoscedastic weighting of per-task losses, to balance losses using the predictive uncertainty of each task.

[0088] According to another aspect of the disclosure, directed to the computer- implemented method, the transformer encoder is a Swin Base transformer encoder.

[0089] According to an aspect of the disclosure, a system includes a data store including video data associated with a surgical procedure; and a machine learning training system configured to receive a laparoscopic image of a first organ; and reproduce the laparoscopic image such that one or more of a silhouette and surface of a first organ is graphically differentiated from adjacent organs and biological structures.

[0090] According to another aspect of the disclosure, directed to the system, the machine learning training system includes an MTL model with a transformer encoder, a landmarkhead and a surface head; and the landmark head is trained for predicting the ridges and silhouette of a first organ from a laparoscopic image of the first organ.

[0091] According to another aspect of the disclosure, directed to the system, the surface head is trained for segmenting the first organ from adjacent organs and biological structures from the laparoscopic image of the first organ.

[0092] According to another aspect of the disclosure, directed to the system, the MTL model is configured to process the laparoscopic image with the MTL model, through the transformer encoder and one or both of the landmark head and the surface head, to reproduce the laparoscopic image such that one or more of the silhouette and surface of the first organ is graphically differentiated from adjacent organs and biological structures.

[0093] According to another aspect of the disclosure, directed to the system, the system is configured to apply the transformer encoder, followed by multi-resolution feature interpolation and stacking, and per-head feature convolutions.

[0094] According to another aspect of the disclosure, directed to the system, the system is configured to apply a homoscedastic weighting of per-task losses, to balance losses using the predictive uncertainty of each task.

[0095] According to another aspect of the disclosure, directed to the system, the transformer encoder is a Swin Base transformer encoder.

[0096] According to an aspect of the disclosure, a computer program product including a memory device having computer executable instructions stored thereon, which when executed by one or more processors cause the one or more processors to perform a plurality of operations including: receiving in a machine learning training system a laparoscopic image of a first organ; and reproducing by the machine learning training system the laparoscopic image such that one or more of a silhouette and surface of a first organ is graphically differentiated from adjacent organs and biological structures.

[0097] According to another aspect of the disclosure, directed to the computer program product, when executed by the one or more processors cause the one or more processors to perform one or more further operations including: training a landmark head, of an MTLmodel that has a transformer encoder, the landmark head and a surface head, for predicting the ridges and silhouette of the first organ from the laparoscopic image of the first organ; and training the surface head for segmenting the first organ from the adjacent organs and biological structures from the laparoscopic image of the first organ.

[0098] According to another aspect of the disclosure, directed to the computer program product, when executed by the one or more processors cause the one or more processors to perform one or more further operations including: processing the laparoscopic image with the MTL model, through the transformer encoder and one or both of the landmark head and the surface head, to reproduce the laparoscopic image such that one or more of the silhouette and surface of the first organ is graphically differentiated from adjacent organs and biological structures.

[0099] According to another aspect of the disclosure, directed to the computer program product, when executed by the one or more processors cause the one or more processors to perform one or more further operations including: applying the transformer encoder followed by multi-resolution feature interpolation and stacking, and per-head feature convolutions.

[0100] According to another aspect of the disclosure, directed to the computer program product, when executed by the one or more processors cause the one or more processors to perform one or more further operations including: applying a homoscedastic weighting of per-task losses, to balance losses using the predictive uncertainty of each task.

[0101] According to another aspect of the disclosure, directed to the computer program product, the transformer encoder is a Swin Base transformer encoder.

[0102] Turning now to FIG. 8, a computer system 800 is generally shown in accordance with an aspect. The computer system 800 can be an electronic computer framework comprising and / or employing any number and combination of computing devices and networks utilizing various communication technologies, as described herein. The computer system 800 can be easily scalable, extensible, and modular, with the ability to change to different services or reconfigure some features independently of others. The computer system 800 may be, for example, a server, desktop computer, laptop computer,tablet computer, or smartphone. In some examples, computer system 800 may be a cloud computing node. Computer system 800 may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. Computer system 800 may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media, including memory storage devices.

[0103] As shown in FIG. 8, the computer system 800 has one or more central processing units (CPU(s)) 801a, 801b, 801c, etc. (collectively or generically referred to as processor(s) 801). The processors 801 can be a single-core processor, multi-core processor, computing cluster, or any number of other configurations. The processors 801 can be any type of circuitry capable of executing instructions. The processors 801, also referred to as processing circuits, are coupled via a system bus 802 to a system memory 803 and various other components. The system memory 803 can include one or more memory devices, such as read-only memory (ROM) 804 and a random-access memory (RAM) 805. The ROM 804 is coupled to the system bus 802 and may include a basic input / output system (BIOS), which controls certain basic functions of the computer system 800. The RAM is read-write memory coupled to the system bus 802 for use by the processors 801. The system memory 803 provides temporary memory space for operations of said instructions during operation. The system memory 803 can include random access memory (RAM), read-only memory, flash memory, or any other suitable memory systems.

[0104] The computer system 800 comprises an input / output (I / O) adapter 806 and a communications adapter 807 coupled to the system bus 802. The I / O adapter 806 may be a small computer system interface (SCSI) adapter that communicates with a hard disk 808 and / or any other similar component. The I / O adapter 806 and the hard disk 808 are collectively referred to herein as a mass storage 810.

[0105] Software 811 for execution on the computer system 800 may be stored in the mass storage 810. The mass storage 810 is an example of a tangible storage medium readable by the processors 801, where the software 811 is stored as instructions for execution by the processors 801 to cause the computer system 800 to operate, such as is described hereinbelow with respect to the various Figures. Examples of computer program product and the execution of such instruction is discussed herein in more detail. The communications adapter 807 interconnects the system bus 802 with a network 812, which may be an outside network, enabling the computer system 800 to communicate with other such systems. In one aspect, a portion of the system memory 803 and the mass storage 810 collectively store an operating system, which may be any appropriate operating system to coordinate the functions of the various components shown in FIG. 8.

[0106] Additional input / output devices are shown as connected to the system bus 802 via a display adapter 815 and an interface adapter 816. In one aspect, the adapters 806, 807, 815, and 816 may be connected to one or more I / O buses that are connected to the system bus 802 via an intermediate bus bridge (not shown). A display 819 (e.g., a screen or a display monitor) is connected to the system bus 802 by a display adapter 815, which may include a graphics controller to improve the performance of graphics-intensive applications and a video controller. A keyboard, a mouse, a touchscreen, one or more buttons, a speaker, etc., can be interconnected to the system bus 802 via the interface adapter 816, which may include, for example, a Super I / O chip integrating multiple device adapters into a single integrated circuit. Suitable I / O buses for connecting peripheral devices such as hard disk controllers, network adapters, and graphics adapters typically include common protocols, such as the Peripheral Component Interconnect (PCI). Thus, as configured in FIG. 8, the computer system 800 includes processing capability in the form of the processors 801, and storage capability including the system memory 803 and the mass storage 810, input means such as the buttons, touchscreen, and output capability including the speaker 823 and the display 819.

[0107] In some aspects, the communications adapter 807 can transmit data using any suitable interface or protocol, such as the internet small computer system interface, among others. The network 812 may be a cellular network, a radio network, a wide area network (WAN), a local area network (LAN), or the Internet, among others. An external computingdevice may connect to the computer system 800 through the network 812. In some examples, an external computing device may be an external web server or a cloud computing node.

[0108] It is to be understood that the block diagram of FIG. 8 is not intended to indicate that the computer system 800 is to include all of the components shown in FIG. 8. Rather, the computer system 800 can include any appropriate fewer or additional components not illustrated in FIG. 8 (e.g., additional memory components, embedded controllers, modules, additional network interfaces, etc.). Further, the aspects described herein with respect to computer system 800 may be implemented with any appropriate logic, wherein the logic, as referred to herein, can include any suitable hardware (e.g., a processor, an embedded controller, or an application-specific integrated circuit, among others), software (e.g., an application, among others), firmware, or any suitable combination of hardware, software, and firmware, in various aspects. Various aspects can be combined to include two or more of the aspects described herein.

[0109] Aspects disclosed herein may be a system, a method, and / or a computer program product at any possible technical detail level of integration. The computer program product may include a computer-readable storage medium (or media) having computer- readable program instructions thereon for causing a processor to carry out various aspects.

[0110] The computer-readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer-readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non- exhaustive list of more specific examples of the computer-readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device, such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of theforegoing. A computer-readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0111] Computer-readable program instructions described herein can be downloaded to respective computing / processing devices from a computer-readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network, and / or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium within the respective computing / processing device.

[0112] Computer-readable program instructions for carrying out operations of the present disclosure may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source-code or object code written in any combination of one or more programming languages, including an object-oriented programming language, such as Smalltalk, C++, high-level languages such as Python, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user’s computer, partly on the user’s computer, as a stand-alone software package, partly on the user’s computer and partly on a remote computer, or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user’s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some aspects, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmablelogic arrays (PLA) may execute the computer-readable program instruction by utilizing state information of the computer-readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0113] Aspects are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to aspects of the disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0114] These computer-readable program instructions may be provided to a processor of a computer system, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, a programmable data processing apparatus, and / or other devices to function in a particular manner, such that the computer-readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function / act specified in the flowchart and / or block diagram block or blocks.

[0115] The computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer-implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0116] The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various aspects. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions,which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustration, and combinations of blocks in the block diagrams and / or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

[0117] The descriptions of the various aspects have been presented for purposes of illustration but are not intended to be exhaustive or limited to the aspects disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described aspects. The terminology used herein was chosen to best explain the principles of the aspects, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the aspects described herein.

[0118] Various aspects are described herein with reference to the related drawings. Alternative aspects can be devised without departing from the scope of this disclosure. Various connections and positional relationships (e.g., over, below, adjacent, etc.) are set forth between elements in the following description and in the drawings. These connections and / or positional relationships, unless specified otherwise, can be direct or indirect, and the present disclosure is not intended to be limiting in this respect. Accordingly, a coupling of entities can refer to either a direct or an indirect coupling, and a positional relationship between entities can be a direct or indirect positional relationship. Moreover, the various tasks and process steps described herein can be incorporated into a more comprehensive procedure or process having additional steps or functionality not described in detail herein.

[0119] The following definitions and abbreviations are to be used for the interpretation of the claims and the specification. As used herein, the terms “comprises,” “comprising,”“includes,” “including,” “has,” “having,” “contains,” or “containing,” or any other variation thereof are intended to cover anon-exclusive inclusion. For example, a composition, a mixture, process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but can include other elements not expressly listed or inherent to such composition, mixture, process, method, article, or apparatus.

[0120] Additionally, the term “exemplary” is used herein to mean “serving as an example, instance or illustration.” Any aspect or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs. The terms “at least one” and “one or more” may be understood to include any integer number greater than or equal to one, i.e., one, two, three, four, etc. The terms “a plurality” may be understood to include any integer number greater than or equal to two, i.e., two, three, four, five, etc. The term “connection” may include both an indirect “connection” and a direct “connection.”

[0121] The terms “about,” “substantially,” “approximately,” and variations thereof are intended to include the degree of error associated with measurement of the particular quantity based upon the equipment available at the time of filing the application. For example, “about” can include a range of ± 8% or 5%, or 2% of a given value.

[0122] For the sake of brevity, conventional techniques related to making and using aspects may or may not be described in detail herein. In particular, various aspects of computing systems and specific computer programs to implement the various technical features described herein are well known. Accordingly, in the interest of brevity, many conventional implementation details are only mentioned briefly herein or are omitted entirely without providing the well-known system and / or process details.

[0123] It should be understood that various aspects disclosed herein may be combined in different combinations than the combinations specifically presented in the description and accompanying drawings. It should also be understood that, depending on the example, certain acts or events of any of the processes or methods described herein may be performed in a different sequence, may be added, merged, or left out altogether (e.g., all described acts or events may not be necessary to carry out the techniques). In addition,while certain aspects of this disclosure are described as being performed by a single module or unit for purposes of clarity, it should be understood that the techniques of this disclosure may be performed by a combination of units or modules associated with, for example, a medical device.

[0124] In one or more examples, the described techniques may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. Computer-readable media may include non-transitory computer-readable media, which corresponds to a tangible medium, such as data storage media (e.g., RAM, ROM, EEPROM, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer).

[0125] Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), graphics processing units (GPUs), microprocessors, application-specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Accordingly, the term “processor” as used herein may refer to any of the foregoing structure or any other physical structure suitable for implementation of the described techniques. Also, the techniques could be fully implemented in one or more circuits or logic elements.

[0126] The following examples are illustrative of the techniques described herein.

[0127] Example 1. A computer-implemented method comprising: receiving in a machine learning system a laparoscopic image of a first organ; and reproducing by the machine learning system the laparoscopic image such that one or more of a silhouette and surface of a first organ is graphically differentiated from adjacent organs and biological structures.

[0128] Example 2. The computer-implemented method of Example 1, further comprising: training a landmark head, of an MTL model that has a transformer encoder, the landmark head and a surface head, for predicting the ridges and silhouette of the first organ from the laparoscopic image of the first organ.

[0129] Example 3. The computer-implemented method of Example 2, further comprising: training the surface head for segmenting the first organ from the adjacent organs and biological structures from the laparoscopic image of the first organ.

[0130] Example 4. The computer-implemented method of Example 3, further comprising: processing the laparoscopic image with the MTL model, through the transformer encoder and one or both of the landmark head and the surface head to reproduce the laparoscopic image such that one or more of the silhouette and surface of the first organ is graphically differentiated from adjacent organs and biological structures.

[0131] Example 5. The computer-implemented method of Example 4, further comprising: applying the transformer encoder followed by multi-resolution feature interpolation and stacking, and per-head feature convolutions.

[0132] Example 6. The computer-implemented method of Example 4, further comprising: applying a homoscedastic weighting of per-task losses, to balance losses using the predictive uncertainty of each task.

[0133] Example 7. The computer-implemented method of Example 4, wherein the transformer encoder is a Swin Base transformer encoder.

[0134] Example s. A system comprising: a data store comprising video data associated with a surgical procedure; and a machine learning training system configured to: receive a laparoscopic image of a first organ; and reproduce the laparoscopic image such that one or more of a silhouette and surface of a first organ is graphically differentiated from adjacent organs and biological structures.

[0135] Example 9. The system of Example 8, wherein: the machine learning training system includes an MTL model with a transformer encoder, a landmark head and a surface head; and the landmark head is trained for predicting the ridges and silhouette of a first organ from a laparoscopic image of the first organ.

[0136] Example 10. The system of Example 9, wherein: the surface head is trained for segmenting the first organ from adjacent organs and biological structures from the laparoscopic image of the first organ.

[0137] Example 11. The system of Example 10, wherein: the MTL model is configured to process the laparoscopic image with the MTL model, through the transformer encoder and one or both of the landmark head and the surface head, to reproduce the laparoscopic image such that one or more of the silhouette and surface of the first organ is graphically differentiated from adjacent organs and biological structures.

[0138] Example 12. The system of Example 10, wherein: the system is configured to apply the transformer encoder, followed by multi-resolution feature interpolation and stacking, and per-head feature convolutions.

[0139] Example 13. The system of Example 10, wherein: the system is configured to apply a homoscedastic weighting of per-task losses, to balance losses using the predictive uncertainty of each task.

[0140] Example 14. The system of Example 10, wherein: the transformer encoder is a Swin Base transformer encoder.

[0141] Example 15. A computer program product comprising a memory device having computer executable instructions stored thereon, which when executed by one or more processors cause the one or more processors to perform a plurality of operations comprising: receiving in a machine learning training system a laparoscopic image of a first organ; and reproducing by the machine learning training system the laparoscopic image such that one or more of a silhouette and surface of a first organ is graphically differentiated from adjacent organs and biological structures.

[0142] Example 16. The computer program product of Example 15, wherein, when executed by the one or more processors cause the one or more processors to perform one or more further operations comprising: training a landmark head, of an MTL model that has a transformer encoder, the landmark head and a surface head, for predicting the ridges and silhouette of the first organ from the laparoscopic image of the first organ; and training the surface head for segmenting the first organ from the adjacent organs and biological structures from the laparoscopic image of the first organ.

[0143] Example 17. The computer program product of Example 16, wherein, when executed by the one or more processors cause the one or more processors to perform oneor more further operations comprising: processing the laparoscopic image with the MTL model, through the transformer encoder and one or both of the landmark head and the surface head, to reproduce the laparoscopic image such that one or more of the silhouette and surface of the first organ is graphically differentiated from adjacent organs and biological structures.

[0144] Example 18. The computer program product of Example 17, wherein, when executed by the one or more processors cause the one or more processors to perform one or more further operations comprising: applying the transformer encoder followed by multi-resolution feature interpolation and stacking, and per-head feature convolutions.

[0145] Example 19. The computer program product of Example 17, wherein, when executed by the one or more processors cause the one or more processors to perform one or more further operations comprising: applying a homoscedastic weighting of per-task losses, to balance losses using the predictive uncertainty of each task.

[0146] Example 20. The computer program product of Example 17, wherein the transformer encoder is a Swin Base transformer encoder.

Claims

CLAIMSWhat is claimed is:

1. A computer-implemented method comprising: receiving in a machine learning system 325 a laparoscopic image 4A3 of a first organ 4A1; and reproducing by the machine learning system 325 the laparoscopic image 4A3 such that one or more of a silhouette 4A22 and surface 4A21 of a first organ 4A1 is graphically differentiated from adjacent organs 4A2 and biological structures 4A3.

2. The computer-implemented method of claim 1, further comprising: training a landmark head 4A20, of an MTL model 4A10 that has a transformer encoder 4A50, the landmark head 4A20 and a surface head 4A30, for predicting the ridges and silhouette 4A22 of the first organ 4A1 from the laparoscopic image 4A3 of the first organ 4A1.

3. The computer-implemented method of claim 1 or 2, further comprising: training the surface head 4A30 for segmenting the first organ 4A1 from the adjacent organs 4A2 and biological structures 4 A3 from the laparoscopic image 4A3 of the first organ 4A1.

4. The computer-implemented method of any of claims 1-3, further comprising: processing the laparoscopic image 4A3 with the MTL model 4A10, through the transformer encoder 4A50 and one or both of the landmark head 4A20 and the surface head 4A30 to reproduce the laparoscopic image 4A3 such that one or more of the silhouette 4A22 and surface 4A21 of the first organ 4A1 is graphically differentiated from adjacent organs 4A2 and biological structures 4A3.

5. The computer-implemented method of any of claims 1-4, further comprising: applying the transformer encoder 4A50 followed by multi-resolution feature interpolation and stacking, and per-head feature convolutions.

6. The computer-implemented method of any of claims 1-5, further comprising: applying a homoscedastic weighting of per-task losses, to balance losses using the predictive uncertainty of each task.

7. The computer-implemented method of any of claim 1-6, wherein the transformer encoder 4A50 is a Swin Base transformer encoder 4A50.

8. A system comprising: a data store comprising video data associated with a surgical procedure; and a machine learning training system 325 configured to: receive a laparoscopic image 4A3 of a first organ 4A1; and reproduce the laparoscopic image 4A3 such that one or more of a silhouette 4A22 and surface 4A21 of a first organ 4A1 is graphically differentiated from adjacent organs 4A2 and biological structures 4 A3.

9. The system of claim 8, wherein: the machine learning training system 325 includes an MTL model 4A10 with a transformer encoder 4A50, a landmark head 4A20 and a surface head 4A30; and the landmark head 4A20 is trained for predicting the ridges and silhouette 4A22 of a first organ 4A1 from a laparoscopic image 4A3 of the first organ 4A1.

10. The system of claim 8 or 9, wherein: the surface head 4A30 is trained for segmenting the first organ 4A1 from adjacent organs 4A2 and biological structures 4A3 from the laparoscopic image 4A3 of the first organ 4A1.

11. The system of any of claims 8-10, wherein: the MTL model 4A10 is configured to process the laparoscopic image 4A3 with the MTL model 4A10, through the transformer encoder 4A50 and one or both of the landmark head 4A20 and the surface head 4A30, to reproduce the laparoscopic image 4A3 such that one or more of the silhouette 4A22 and surface 4A21 of the first organ 4A1 is graphically differentiated from adjacent organs 4A2 and biological structures 4 A3.

12. The system of any of claims 8-11, wherein: the system is configured to apply the transformer encoder 4A50, followed by multi-resolution feature interpolation and stacking, and per-head feature convolutions.

13. The system of any of claims 8-12, wherein: the system is configured to apply a homoscedastic weighting of per-task losses, to balance losses using the predictive uncertainty of each task.

14. The system of any of claims 8-13, wherein: the transformer encoder 4A50 is a Swin Base transformer encoder 4A50.

15. A computer program product comprising a memory device having computer executable instructions stored thereon, which when executed by one or more processors cause the one or more processors to perform the method of any of claims 1-7.

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