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

Processing circuitry that

• obtains, as learning data,
• medical image data,
• first-type labeling information meant for segmenting a predetermined structure into a plurality of categories, and
• second-type labeling information meant for segmenting a massive region, which covers the predetermined structure, into a plurality of blocks;
• and
• performs learning that, based on a loss function value, includes
• performing supervised learning of voxel in the medical image data according to a region to which the voxel belongs and by
• using the first-type labeling information about voxel of the predetermined structure and causing a segmentation model to perform direct supervised learning that represents learning for segmentation of the predetermined structure in target medical image data for segmentation into a plurality of categories, and
• using the second-type labeling information about voxel of the massive region and causing the segmentation model to perform indirect supervised learning that represents learning for segmentation of the massive region in the target medical image data for segmentation into a plurality of categories, and
• optimizing network parameter of the segmentation model.

A computer program product having a non-transitory computer-readable medium including programmed instructions, wherein the instructions, when executed by a computer, cause the computer to perform:

• obtaining, as learning data,
• medical image data,
• first-type labeling information meant for segmenting a predetermined structure into a plurality of categories, and
• second-type labeling information meant for segmenting a massive region, which covers the predetermined structure, into a plurality of blocks;
• and
• learning that, based on a loss function value, includes performing supervised learning of voxel in the medical image data according to a region to which the voxel belongs, wherein
• the supervised learning of the medical image data includes
• using the first-type labeling information about voxel of the predetermined structure and causing a segmentation model to perform direct supervised learning that represents learning for segmentation of the predetermined structure in target medical image data for segmentation into a plurality of categories,
• using the second-type labeling information about voxel of the massive region and causing the segmentation model to perform indirect supervised learning that represents learning for segmentation of the massive region in the target medical image data for segmentation into a plurality of categories, and
• optimizing network parameter of the segmentation model.