Jointly trained machine learning models for automatic contouring in radiotherapy applications
AssigneeElekta Limited
InventorsPhilip P. Novosad, Silvain Beriault
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Abstract
Joint training techniques to train multiple models across clinical datasets for automatic contouring. Rather than using separate deep neural networks that are trained independently for each different dataset (e.g., a different image contrast or anatomy), joint training can be used to train multiple models simultaneously across clinical datasets for automatic contouring. By taking advantage of commonalities between two or more datasets, the techniques effectively take advantage of data that would otherwise be considered irrelevant to the task—allowing the user to train more performant models while requiring less training data per dataset.
Claims23
A computer-implemented method for generating a radiotherapy treatment plan for a subject including training processing circuitry to automatically contour an image depicting an anatomy of the subject, the method comprising:
- jointly training a first machine learning model and a second machine learning model by using images from a first training dataset and a second training dataset independent from the first training dataset, wherein the first machine learning model and the second machine learning model are configured to share at least one learned model parameter, and wherein, when trained, the machine learning models are configured to automatically contour images for radiotherapy treatment.
A computer-implemented method for generating a radiotherapy treatment plan for a subject including automatically contouring an image depicting an anatomy of the subject, the method comprising:
- obtaining the image of the subject;
- selecting, based on an image parameter, a previously trained first machine learning model, wherein the previously trained first machine learning model was jointly trained with a second machine learning model by using images from a first training dataset and a second training dataset independent from the first training dataset;
- applying the first previously trained machine learning model to the image of the subject to generate a machine learning model output, wherein applying includes applying a shared learned parameter learned during joint training with the second machine learning model, and a learned parameter specific to the first machine learning model and not shared with the second machine learning model;
- contouring, without user intervention and based on the first machine learning model output, one or more anatomical structures of the image;
- and
- processing the one or more contoured anatomical structures to generate the radiotherapy treatment plan.
A radiotherapy system for generating a radiotherapy treatment plan for a subject including automatically contouring an image depicting an anatomy of the subject, the radiotherapy system comprising:
- a radiation therapy device configured to deliver a dose of radiation to an anatomical region of interest;
- and
- a processor configured to:
- obtain the image of the subject;
- select, based on an image parameter, a previously trained first machine learning model, wherein the previously trained first machine learning model was jointly trained with a second machine learning model by using images from a first training dataset and a second training dataset independent from the first training dataset;
- apply the first previously trained machine learning model to the image of the subject to generate a machine learning model output, wherein applying includes applying a shared learned parameter learned during joint training with the second machine learning model, and a learned parameter specific to the first machine learning model and not shared with the second machine learning model;
- contour, without user intervention and based on the first machine learning model output, one or more anatomical structures of the image;
- and
- process the one or more contoured anatomical structures to generate the radiotherapy treatment plan.
Dependencies are inferred from the claim text.