Abstract: Systems and methods for anatomical structure segmentation in medical images using multiple anatomical structures, instructions and segmentation models.

Abstract: Systems and methods for anatomical structure segmentation in medical images using multiple anatomical structures, instructions and segmentation models.

Abstract: A treatment planning apparatus includes: a modeler configured to obtain a model definition, wherein the model definition comprises a first quality metric of a first clinical goal; and a treatment planner having: a model trainer configured to obtain a set of existing treatment plans following desired clinical practice, and to perform model training to obtain a trained model based on the existing treatment plans and the first quality metric of the first clinical goal; an objective generator configured to generate a cost function based on the trained model; and an optimizer configured to determine a treatment plan based on the cost function.

Abstract: Systems and methods for cloud-based scalable segmentation model training solutions including a computing interface by which a client/user/customer can upload and store training data in a storage device of a cloud-based network, provide access to the training data stored in the storage device, initiate a request for training a segmentation model, monitor the training of the segmentation model, and download the trained segmentation model, and a computing system operatively coupled with a client device through the computing interface and configured to pre-process the training data using a first set of computing resources of the cloud-based network, store the processed training data in a storage device of the cloud-based network, deploy, upon a training request from the client device, a training application on a second set of computing resources of the cloud-based network to train the segmentation model based on the processed training data, provide access to the client device to monitor the training, and provide

Abstract: Example methods and systems for training deep learning engines for radiotherapy treatment planning are provided. One example method may comprise: obtaining a set of training data that includes unlabeled training data and labeled training data; and configuring a deep learning engine to include (a) a primary network and (b) a deep supervision network that branches off from the primary network. The method may further comprise: training the deep learning engine to perform the radiotherapy treatment planning task by processing training data instance to generate (a) primary output data and (b) deep supervision output data; and updating weight data associated with at least some of the multiple processing layers based on the primary output data and/or the deep supervision output data. The deep supervision network may be pruned prior to applying the primary network to perform the radiotherapy treatment planning task for a patient.

Abstract: Example methods and systems for deep transfer learning for radiotherapy treatment planning are provided. One example method may comprise: obtaining (310) a base deep learning engine that is pre-trained to perform a base radiotherapy treatment planning task; and based on the base deep learning engine, generating a target deep learning engine to perform a target radiotherapy treatment planning task. The target deep learning engine may be generated by configuring (330) a variable base layer among multiple base layers of the base deep learning engine, and generating (340) one of multiple target layers of the target deep learning engine by modifying the variable base layer. Alternatively or additionally, the target deep learning engine may be generated by configuring (350) an invariable base layer among the multiple base layers, and generating (360) one of multiple target layers of the target deep learning engine based on feature data generated using the invariable base layer.

Abstract: Systems and methods for cloud-based scalable segmentation model training solutions including a computing interface by which a client/user/customer can upload and store training data in a storage device of a cloud-based network, provide access to the training data stored in the storage device, initiate a request for training a segmentation model, monitor the training of the segmentation model, and download the trained segmentation model, and a computing system operatively coupled with a client device through the computing interface and configured to pre-process the training data using a first set of computing resources of the cloud-based network, store the processed training data in a storage device of the cloud-based network, deploy, upon a training request from the client device, a training application on a second set of computing resources of the cloud-based network to train the segmentation model based on the processed training data, provide access to the client device to monitor the training, and provide

Abstract: Example methods and systems for training deep learning engines for radiotherapy treatment planning are provided. One example method may comprise: obtaining a set of training data that includes unlabeled training data and labeled training data; and configuring a deep learning engine to include (a) a primary network and (b) a deep supervision network that branches off from the primary network. The method may further comprise: training the deep learning engine to perform the radiotherapy treatment planning task by processing training data instance to generate (a) primary output data and (b) deep supervision output data; and updating weight data associated with at least some of the multiple processing layers based on the primary output data and/or the deep supervision output data. The deep supervision network may be pruned prior to applying the primary network to perform the radiotherapy treatment planning task for a patient.

Abstract: Systems and methods for anatomical structure segmentation in medical images using multiple anatomical structures, instructions and segmentation models.

Abstract: An apparatus includes: one or more input communicatively coupled to one or more medium storing current treatment plan data and a current image of a patient, the one or more input configured to obtain the current treatment plan data and the current image of the patient, wherein the current treatment plan data is for processing by a treatment machine; and a re-planning decision processor configured to determine a re-plan information based at least in part on the current treatment plan data, the current image, and a re-plan triggering model, the re-plan triggering model based on previous treatment plan data and previous image(s), wherein the re-plan information indicates a recommendation regarding treatment re-planning; wherein the re-planning decision processor is configured to output the re-plan information for reducing a burden, or for obviating a need, for a user of the apparatus to manually decide whether the treatment re-planning is desirable or not.

Abstract: A treatment planning apparatus includes: a modeler configured to obtain a model definition, wherein the model definition comprises a first quality metric of a first clinical goal; and a treatment planner having: a model trainer configured to obtain a set of existing treatment plans following desired clinical practice, and to perform model training to obtain a trained model based on the existing treatment plans and the first quality metric of the first clinical goal; an objective generator configured to generate a cost function based on the trained model; and an optimizer configured to determine a treatment plan based on the cost function.

Abstract: An apparatus includes: one or more input communicatively coupled to one or more medium storing current treatment plan data and a current image of a patient, the one or more input configured to obtain the current treatment plan data and the current image of the patient, wherein the current treatment plan data is for processing by a treatment machine; and a re-planning decision processor configured to determine a re-plan information based at least in part on the current treatment plan data, the current image, and a re-plan triggering model, the re-plan triggering model based on previous treatment plan data and previous image(s), wherein the re-plan information indicates a recommendation regarding treatment re-planning; wherein the re-planning decision processor is configured to output the re-plan information for reducing a burden, or for obviating a need, for a user of the apparatus to manually decide whether the treatment re-planning is desirable or not.