Systems and methods for automated tumor segmentation in radiology imaging using data mined line annotations
OwnerNathaniel Cuthbert Swinburne, Vivek Yadavinventors
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Abstract
Systems and methods for segmenting pathological features are disclosed. The system can receive a plurality of training images and corresponding data mined or separately introduced line annotations. The system can train a component thereof to generate bounding boxes upon an image of interest based on the training images and the bounding boxes converted from the mined line annotations. The system can detect and refine the segmentations of a pathological feature on the image of interest. The system can reconcile various images, or portions thereof, between the refinement or other processes performed by the system.
Claims20
A system for automated segmentation of pathological lesions, the system comprising:
- one or more processors coupled to a non-transitory memory, the one or more processors configured to:
- receive a plurality of first images, a first portion of the plurality of first images comprising line annotations corresponding to one or more pathological lesions;
- convert a first plurality of line annotations to a first plurality of corresponding bounding boxes;
- train a lesion detection model to generate a second plurality of bounding boxes, based on the first plurality of bounding boxes;
- generate, based on the lesion detection model, a third bounding box for one or more unannotated lesions of a second image;
- define a volumetric segmentation corresponding to the third bounding box;
- refine the volumetric segmentation based on volumetric segmentation training data to define a refined volumetric segmentation;
- reconcile the refined volumetric segmentation with another volumetric description corresponding to at least one of the one or more unannotated lesions;
- generate a presentment image, based on the reconciliation;
- and
- cause a display of the presentment image via a graphical user interface.
A method for segmenting pathological lesions, the method comprising:
- receiving, by one or more processors coupled to a non-transitory memory, a plurality of first images, a first portion of the plurality of first images comprising line annotations corresponding to one or more first pathological lesions;
- defining, by the one or more processors, a plurality of first bounding boxes converted from the line annotations of the plurality of first images;
- defining, by the one or more processors, a second bounding box for one or more second pathological lesions of a second image using a detection model trained from the plurality of first bounding boxes;
- defining, by the one or more processors, a volumetric segmentation corresponding to each bounding box of the second image;
- refining, by the one or more processors, the volumetric segmentation based on volumetric segmentation training data to define a refined volumetric segmentation;
- reconciling, by the one or more processors, the refined volumetric segmentation with another volumetric segmentation corresponding to at least one of the one or more second pathological lesions;
- generating, by the one or more processors, a presentment image, based on the reconciliation;
- and
- causing, by the one or more processors, a display of the presentment image via a graphical user interface.
A non-transitory computer-readable medium having instructions that, upon execution by a computing device, cause the computing device to perform operations comprising:
- receiving an image comprising a pathological lesion;
- ingesting, by a model trained to detect lesions based on a marking, the image;
- generating, based on the model trained to detect lesions, a marking for one or more unannotated lesions of the image;
- defining a volumetric segmentation corresponding to the marking;
- refining the volumetric segmentation based on volumetric segmentation training data to define a refined volumetric segmentation;
- reconciling the refined volumetric segmentation with another volumetric description corresponding to the one or more unannotated lesions;
- generating a presentment image, based on the reconciliation;
- and
- causing a display of the presentment image via a graphical user interface.
Dependencies are inferred from the claim text.