Segmentation model learning method, processing circuitry, computer program product, and medical information processing device

Abstract

A segmentation model learning method according to an embodiment includes learning that, based on a loss function value, includes performing supervised learning of the voxels in medical image data according to the region to which the voxels belong. The learning of the medical image data includes: using first-type labeling information, which is meant for segmenting a predetermined structure into a plurality of categories, about the voxels of a predetermined structure and causing a segmentation model to perform direct supervised learning that represents learning for segmentation of the predetermined structure into a plurality of categories; using second-type labeling information, which is meant for segmenting a massive region covering the predetermined structure into a plurality of blocks, about the voxels of a massive region and causing the segmentation model to perform indirect supervised learning that represents learning for segmentation of the massive region into a plurality of categories; and optimizing the network parameters of the segmentation model.