PMID- 38520785
OWN - NLM
STAT- MEDLINE
DCOM- 20240402
LR  - 20240402
IS  - 1872-7565 (Electronic)
IS  - 0169-2607 (Linking)
VI  - 248
DP  - 2024 May
TI  - SC-Net: Symmetrical conical network for colorectal pathology image segmentation.
PG  - 108119
LID - S0169-2607(24)00115-9 [pii]
LID - 10.1016/j.cmpb.2024.108119 [doi]
AB  - BACKGROUND AND OBJECTIVE: Image segmentation of histopathology of colorectal 
      cancer is a core task of computer aided medical image diagnosis system. Existing 
      convolutional neural networks generally extract multi-scale information in linear 
      flow structures by inserting multi-branch modules, which is difficult to extract 
      heterogeneous semantic information under multi-level and different receptive 
      field and tough to establish context dependency among different receptive field 
      features. METHODS: To address these issues, we propose a symmetric spiral 
      progressive feature fusion encoder-decoder network called the Symmetric Conical 
      Network (SC-Net). First, we design a Multi-scale Feature Extraction Block (MFEB) 
      matching with the Symmetric Conical Network to obtain multi-branch heterogeneous 
      semantic information under different receptive fields, so as to enrich the 
      diversity of extracted feature information. The encoder is composed of MFEB 
      through spiral and multi-branch arrangement to enhance context dependence between 
      different information flow. Secondly, the information loss of contour, color and 
      others in high-level semantic information through causally stacking MFEB, the 
      Feature Mapping Layer (FML) is designed to map low-level features to high-level 
      semantic features along the down-sampling branch and solve the problem of 
      insufficient global feature extraction in deep levels. RESULTS: The SC-Net was 
      evaluated on our self-constructed colorectal cancer dataset, a publicly available 
      breast cancer dataset and a polyp dataset. The results revealed that the mDice of 
      segmentation reached 0.8611, 0.7259 and 0.7144. We compare our model with the 
      state-of-art semantic segmentation UNet++, PSPNet, Attention U-Net, R2U-Net and 
      other advanced segmentation networks. The experimental results demonstrate that 
      we achieve the most advanced performance. CONCLUSIONS: The results indicate that 
      the proposed SC-Net excels in segmenting H&E stained pathology images, 
      effectively preserving morphological features and spatial information even in 
      scenarios with weak texture, poor contrast, and variations in appearance.
CI  - Copyright (c) 2024 Elsevier B.V. All rights reserved.
FAU - Zhang, Gang
AU  - Zhang G
AD  - Faculty of Mechanical and Electrical Engineering, Kunming University of Science 
      and Technology, Kunming 650500, China. Electronic address: zhanggang97zg@163.com.
FAU - He, Zifen
AU  - He Z
AD  - Faculty of Mechanical and Electrical Engineering, Kunming University of Science 
      and Technology, Kunming 650500, China. Electronic address: zyhhzf1998@163.com.
FAU - Zhang, Yinhui
AU  - Zhang Y
AD  - Faculty of Mechanical and Electrical Engineering, Kunming University of Science 
      and Technology, Kunming 650500, China. Electronic address: 
      zhangyinhui@kust.edu.cn.
FAU - Li, Zhenhui
AU  - Li Z
AD  - Yunnan Cancer Hospital, Department of Radiology, The Third Affiliated Hospital of 
      Kunming Medical University, Yunnan Cancer Center, Kunming 650118, China. 
      Electronic address: lizhenhui@kmmu.edu.cn.
FAU - Wu, Lin
AU  - Wu L
AD  - Yunnan Cancer Hospital, Department of Radiology, The Third Affiliated Hospital of 
      Kunming Medical University, Yunnan Cancer Center, Kunming 650118, China. 
      Electronic address: 740847705@qq.com.
LA  - eng
PT  - Journal Article
DEP - 20240313
PL  - Ireland
TA  - Comput Methods Programs Biomed
JT  - Computer methods and programs in biomedicine
JID - 8506513
SB  - IM
MH  - Humans
MH  - Diagnosis, Computer-Assisted
MH  - Neural Networks, Computer
MH  - *Polyps
MH  - Semantics
MH  - *Colorectal Neoplasms/diagnostic imaging
MH  - Image Processing, Computer-Assisted
OTO - NOTNLM
OT  - Colorectal cancer
OT  - Multi-branch
OT  - Pathology image segmentation
OT  - Symmetric conical network
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- 2024/03/24 00:42
MHDA- 2024/04/02 06:46
CRDT- 2024/03/23 19:06
PHST- 2023/12/04 00:00 [received]
PHST- 2024/02/25 00:00 [revised]
PHST- 2024/03/04 00:00 [accepted]
PHST- 2024/04/02 06:46 [medline]
PHST- 2024/03/24 00:42 [pubmed]
PHST- 2024/03/23 19:06 [entrez]
AID - S0169-2607(24)00115-9 [pii]
AID - 10.1016/j.cmpb.2024.108119 [doi]
PST - ppublish
SO  - Comput Methods Programs Biomed. 2024 May;248:108119. doi: 
      10.1016/j.cmpb.2024.108119. Epub 2024 Mar 13.