TY - GEN
T1 - Coarse to Fine Automatic Segmentation of Abdominal Multiple Organs
AU - Lv, Yi
AU - Ning, Yu
AU - Wang, Junchen
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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 test set show that the average DSC performance is about 0.766. The average time and GPU memory consumption for each case is 81.42 s and 1953 MB.
AB - 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 test set show that the average DSC performance is about 0.766. The average time and GPU memory consumption for each case is 81.42 s and 1953 MB.
KW - Deep learning
KW - Medical image segmentation
KW - Neural network
UR - https://www.scopus.com/pages/publications/85149643850
U2 - 10.1007/978-3-031-23911-3_20
DO - 10.1007/978-3-031-23911-3_20
M3 - 会议稿件
AN - SCOPUS:85149643850
SN - 9783031239106
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 223
EP - 232
BT - Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation - MICCAI 2022 Challenge, FLARE 2022, Held in Conjunction with MICCAI 2022, Proceedings
A2 - Ma, Jun
A2 - Wang, Bo
PB - Springer Science and Business Media Deutschland GmbH
T2 - International challenge on Fast and Lowresource Semi-supervised Abdominal Organ Segmentation in CT Scans, FLARE 2022 held in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022
Y2 - 22 September 2022 through 22 September 2022
ER -