Coarse to Fine Automatic Segmentation of Abdominal Multiple Organs Based on Semi-Supervised Network

Yi Lv, Yu Ning, Junchen Wang

Keywords: Medical Image Segmentation, Deep Learning, Neural Network

Abstract: Abdominal multi-organ segmentation is fast becoming a key instrument in preoperative diagnosis. Using the results of abdominal CT image segmentation for three-dimensional reconstruction is an intuitive and accurate method for surgical planning. In this paper, we propose a stable three-stage fast automatic segmentation method for abdominal 13 organs: liver, spleen, pancreas, right kidney, left kidney, stomach, gallbladder, esophagus, aorta, inferior vena cava, right adrenal gland, left adrenal gland, and duodenum. Our method includes preprocessing the CT data, segmenting the multi-organ and post-processing the segmentation outputs. The results on the first-fold validation set show that the average DSC performance on the official validation leaderboard is about 0.77. The average time and GPU memory consumption for each case is 81.42s and 1953MB.