PMID- 37178624
OWN - NLM
STAT- MEDLINE
DCOM- 20230529
LR  - 20230529
IS  - 1724-191X (Electronic)
IS  - 1120-1797 (Linking)
VI  - 110
DP  - 2023 Jun
TI  - Robust and efficient abdominal CT segmentation using shape constrained 
      multi-scale attention network.
PG  - 102595
LID - S1120-1797(23)00072-8 [pii]
LID - 10.1016/j.ejmp.2023.102595 [doi]
AB  - PURPOSE: Although many deep learning-based abdominal multi-organ segmentation 
      networks have been proposed, the various intensity distributions and organ shapes 
      of the CT images from multi-center, multi-phase with various diseases introduce 
      new challenges for robust abdominal CT segmentation. To achieve robust and 
      efficient abdominal multi-organ segmentation, a new two-stage method is presented 
      in this study. METHODS: A binary segmentation network is used for coarse 
      localization, followed by a multi-scale attention network for the fine 
      segmentation of liver, kidney, spleen, and pancreas. To constrain the organ 
      shapes produced by the fine segmentation network, an additional network is 
      pre-trained to learn the shape features of the organs with serious diseases and 
      then employed to constrain the training of the fine segmentation network. 
      RESULTS: The performance of the presented segmentation method was extensively 
      evaluated on the multi-center data set from the Fast and Low GPU Memory Abdominal 
      oRgan sEgmentation (FLARE) challenge, which was held in conjunction with 
      International Conference on Medical Image Computing and Computer Assisted 
      Intervention (MICCAI) 2021. Dice Similarity Coefficient (DSC) and Normalized 
      Surface Dice (NSD) were calculated to quantitatively evaluate the segmentation 
      accuracy and efficiency. An average DSC and NSD of 83.7% and 64.4% were achieved, 
      and our method finally won the second place among more than 90 participating 
      teams. CONCLUSIONS: The evaluation results on the public challenge demonstrate 
      that our method shows promising performance in robustness and efficiency, which 
      may promote the clinical application of the automatic abdominal multi-organ 
      segmentation.
CI  - Copyright (c) 2023. Published by Elsevier Ltd.
FAU - Tong, Nuo
AU  - Tong N
AD  - AI-based Big Medical Imaging Data Frontier Research Center, Academy of Advanced 
      Interdisciplinary Research, Xidian University, Xi'an, Shaanxi 710071, China.
FAU - Xu, Yinan
AU  - Xu Y
AD  - Key Lab of Intelligent Perception and Image Understanding of Ministry of 
      Education, Xidian University, Xi'an, Shaanxi, 710071, China.
FAU - Zhang, Jinsong
AU  - Zhang J
AD  - Xijing Hospital of Air Force Military Medical University, Xian, Shaanxi 710032, 
      China.
FAU - Gou, Shuiping
AU  - Gou S
AD  - AI-based Big Medical Imaging Data Frontier Research Center, Academy of Advanced 
      Interdisciplinary Research, Xidian University, Xi'an, Shaanxi 710071, China; Key 
      Lab of Intelligent Perception and Image Understanding of Ministry of Education, 
      Xidian University, Xi'an, Shaanxi, 710071, China. Electronic address: 
      shpgou@mail.xidian.edu.cn.
FAU - Li, Mengbin
AU  - Li M
AD  - Xijing Hospital of Air Force Military Medical University, Xian, Shaanxi 710032, 
      China. Electronic address: limbin@fmmu.edu.cn.
LA  - eng
PT  - Journal Article
DEP - 20230511
PL  - Italy
TA  - Phys Med
JT  - Physica medica : PM : an international journal devoted to the applications of 
      physics to medicine and biology : official journal of the Italian Association of 
      Biomedical Physics (AIFB)
JID - 9302888
SB  - IM
MH  - *Neural Networks, Computer
MH  - *Algorithms
MH  - Tomography, X-Ray Computed/methods
MH  - Abdomen/diagnostic imaging
MH  - Spleen/diagnostic imaging
MH  - Image Processing, Computer-Assisted/methods
OTO - NOTNLM
OT  - Abdominal CT
OT  - Channel-wise attention
OT  - Multi-scale feature
OT  - Robust multi-organ segmentation
OT  - Shape constraints
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/05/14 01:07
MHDA- 2023/05/29 06:41
CRDT- 2023/05/13 18:07
PHST- 2022/09/22 00:00 [received]
PHST- 2023/03/02 00:00 [revised]
PHST- 2023/04/17 00:00 [accepted]
PHST- 2023/05/29 06:41 [medline]
PHST- 2023/05/14 01:07 [pubmed]
PHST- 2023/05/13 18:07 [entrez]
AID - S1120-1797(23)00072-8 [pii]
AID - 10.1016/j.ejmp.2023.102595 [doi]
PST - ppublish
SO  - Phys Med. 2023 Jun;110:102595. doi: 10.1016/j.ejmp.2023.102595. Epub 2023 May 11.