ディープラーニングに基づくCT画像からの複数の解剖学的構造の同時抽出

抄録

Our research group has been working on using deep learning（DL）to address a critical issue, automatic image segmentation, which is the fundamental part of medical image analysis based on computers. This review article describes the outline of our recent study as one application of the DL for multiple organ segmentations on CT images. We carry out the image segmentations as a multi-class, pixel-wise classification problem, and employ a fully convolutional network to solve this difficult classification task based on fully data-driven approach. Comparing to the previous works, our method uses an end-to-end DL approach to learn image features combined with a classifier together. As the result of image segmentations for 19 types of organs on 240 cases of 3D CT scans, our method demonstrated a comparable performance to other state-of-the-art works with much better efficiency, generality, and flexibility.