TY - GEN
T1 - Two-Stage Multi-Organ Automatic Segmentation with Low GPU Memory Occupancy
AU - Lv, Yi
AU - Wang, Junchen
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Abdominal multi organ segmentation is of great significance in medical diagnosis and research. As the abdominal CT usually has a high resolution and a high image size, automatic segmentation of the abdominal organs demands a high configuration of hardware. In this paper, we proposed a low GPU memory occupied two stage fully supervised automatic segmentation framework for abdomina113 organs: liver, spleen, pancreas, right kidney, left kidney, stomach, gallbladder, esophagus, aorta, inferior vena cava, right adrenal gland, left adrenal gland, and duodenum, and designed a lightweight 3D CNN refer to as Tiny-CED Net. The proposed Tiny-CED Net can accurately complete the automatic segmentation of the whole abdominal CT with the GPU memory occupation <2GB. The results show that the average DSC of our method reached 0.83. The average time consumption and max GPU memory occupied are less than 25s and 2GB.
AB - Abdominal multi organ segmentation is of great significance in medical diagnosis and research. As the abdominal CT usually has a high resolution and a high image size, automatic segmentation of the abdominal organs demands a high configuration of hardware. In this paper, we proposed a low GPU memory occupied two stage fully supervised automatic segmentation framework for abdomina113 organs: liver, spleen, pancreas, right kidney, left kidney, stomach, gallbladder, esophagus, aorta, inferior vena cava, right adrenal gland, left adrenal gland, and duodenum, and designed a lightweight 3D CNN refer to as Tiny-CED Net. The proposed Tiny-CED Net can accurately complete the automatic segmentation of the whole abdominal CT with the GPU memory occupation <2GB. The results show that the average DSC of our method reached 0.83. The average time consumption and max GPU memory occupied are less than 25s and 2GB.
UR - https://www.scopus.com/pages/publications/85140749950
U2 - 10.1109/WRCSARA57040.2022.9903976
DO - 10.1109/WRCSARA57040.2022.9903976
M3 - 会议稿件
AN - SCOPUS:85140749950
T3 - Proceedings of the 4th WRC Symposium on Advanced Robotics and Automation 2022, WRC SARA 2022
SP - 95
EP - 100
BT - Proceedings of the 4th WRC Symposium on Advanced Robotics and Automation 2022, WRC SARA 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th WRC Symposium on Advanced Robotics and Automation, WRC SARA 2022
Y2 - 20 September 2022
ER -