Abstract: A control circuit accesses historical information regarding previously optimized radiation treatment plans for different patients and processes that information to determine the relative importance of different clinical goals. The circuit then facilitates development of a particular plan for a particular patient as a function of the relative importance of the clinical goals. By one approach the control circuit can be configured as a radiation treatment plan recommendation resource that accesses a database of radiation treatment plan formulation content items including at least one of a radiation treatment plan template, an auto-planning algorithm, and an auto-segmentation algorithm.

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 radiotherapy treatment planning based on continuous deep learning are provided. One example method may comprise: obtaining a deep learning engine that is trained to perform a radiotherapy treatment planning task based on first training data associated with a first planning rule. The method may also comprise: based on input data associated with a particular patient, performing the radiotherapy treatment planning task using the deep learning engine to generate output data associated with the particular patient; and obtaining modified output data that includes one or more modifications to the output data generated by the deep learning engine. The method may further comprise: based on the modified output data, generating second training data associated with a second planning rule; and generating a modified deep learning engine by re-training the deep learning engine using a combination of the first training data and the second training data.

Abstract: One or more medical images of a patient are processed by a first neural network model to determine a region-of-interest (ROI) or a cut-off plane. Information from the first neural network model is used to crop the medical images, which serves as input to a second neural network model. The second neural network model processes the cropped medical images to determine contours of anatomical structures in the medical images of the patient. Each of the first and second neural network models are deep neural network models. By use of cropped images in the training and inference phases of the second neural network model, contours are produced with sharp edges or flat surfaces.

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: One or more medical images of a patient are processed by a first neural network model to determine a region-of-interest (ROI) or a cut-off plane. Information from the first neural network model is used to crop the medical images, which serves as input to a second neural network model. The second neural network model processes the cropped medical images to determine contours of anatomical structures in the medical images of the patient. Each of the first and second neural network models are deep neural network models. By use of cropped images in the training and inference phases of the second neural network model, contours are produced with sharp edges or flat surfaces.

Abstract: A control circuit accesses historical information regarding previously optimized radiation treatment plans for different patients and processes that information to determine the relative importance of different clinical goals. The circuit then facilitates development of a particular plan for a particular patient as a function of the relative importance of the clinical goals. By one approach the control circuit can be configured as a radiation treatment plan recommendation resource that accesses a database of radiation treatment plan formulation content items including at least one of a radiation treatment plan template, an auto-planning algorithm, and an auto-segmentation algorithm.

Abstract: A control circuit accesses patient information including anatomical image information of the patient, segmentation information corresponding to the anatomical image information, and a dose map for the radiation treatment plan. The control circuit then generates at least one organ-specific three-dimensional risk map as a function of the patient information and presents that risk map to a user via a display.

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: One or more medical images of a patient are processed by a first neural network model to determine a region-of-interest (ROI) or a cut-off plane. Information from the first neural network model is used to crop the medical images, which serves as input to a second neural network model. The second neural network model processes the cropped medical images to determine contours of anatomical structures in the medical images of the patient. Each of the first and second neural network models are deep neural network models. By use of cropped images in the training and inference phases of the second neural network model, contours are produced with sharp edges or flat surfaces.

Abstract: Example methods and systems for radiotherapy treatment planning based on continuous deep learning are provided. One example method may comprise: obtaining a deep learning engine that is trained to perform a radiotherapy treatment planning task based on first training data associated with a first planning rule. The method may also comprise: based on input data associated with a particular patient, performing the radiotherapy treatment planning task using the deep learning engine to generate output data associated with the particular patient; and obtaining modified output data that includes one or more modifications to the output data generated by the deep learning engine. The method may further comprise: based on the modified output data, generating second training data associated with a second planning rule; and generating a modified deep learning engine by re-training the deep learning engine using a combination of the first training data and the second training data.