Patent ID: 11967072
Assignee: GENENTECH, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A method for segmenting objects within medical images, comprising:
obtaining medical images of a subject, the medical images include a first image having a first characteristic and a second image having a second characteristic, wherein the medical images are generated using one or more medical imaging modalities;
locating and classifying, using a localization model, objects within the first image into a plurality of object classes, where the classifying assigns sets of pixels or voxels of the first, image into one or more of the plurality of object classes;
determining, using the localization model, a bounding box or segmentation mask for an object of interest within the first image based on sets of pixels or voxels assigned with an object class of the plurality of object classes;
transferring the bounding box or the segmentation mask onto the second image to define a portion of the second image comprising the object of interest;
inputting the portion of the second image into a three-dimensional neural network model constructed for volumetric segmentation using a weighted loss function, wherein the weighted loss function includes variables configured to compensate for an imbalance between background and foreground of the portion of the second image, and wherein the three-dimensional neural network model comprises a plurality of model parameters identified using a set of training data comprising:
a plurality of medical images with annotations associated with segmentation boundaries around objects of interest; and
a plurality of additional medical images with annotations associated with segmentation boundaries around objects of interest, wherein the plurality of additional medical images is artificially generated by matching image histograms from the plurality of medical images to image histograms from a plurality of reference maps,

wherein the plurality of model parameters is identified using the set of training data based on minimizing the weighted loss function;
generating, using the three-dimensional neural network model, an estimated segmentation boundary around the object of interest; and
outputting, using the three-dimensional neural network, the portion of the second image with the estimated segmentation boundary around the object of interest.