Korea University Research and Business Foundation

Samsung Life Public Welfare Foundation

Sihyeon Kim

Jeong Hun Kim

Sang Won Seo

Jun Kyung Seong

The present invention relates to an apparatus for predicting a region-specific cerebral cortical contraction rate on the basis of a CT image. The present invention may comprise: a deep learning step of a deep learning network learning, by selecting and using CT images of a plurality of patients and segmentation information thereof, a correlation between the CT images and the segmentation information; a feature extraction step of extracting, on the basis of each piece of the segmentation information, semantic feature information corresponding to the CT images; a machine learning step of a machine learning model learning, after a plurality of region-specific cerebral cortical contraction rates corresponding to each piece of the semantic feature information are additionally acquired, a correlation between the semantic feature information and the region-specific cerebral cortical contraction rates; a segmentation step of, when an image to be analyzed is input, acquiring segmentation information corresponding to the image to be analyzed, through the deep learning network; and a prediction step of predicting and reporting, after semantic feature information corresponding to the image to be analyzed is extracted on the basis of the segmentation information, a region-specific cerebral cortical contraction rate corresponding to the semantic feature information through the machine learning model.

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Daejeon, South Korea

Sihyeon Kim