文献类型：期刊文献

英文题名：Multi-Scale Context-Guided Deep Network for Automated Lesion Segmentation With Endoscopy Images of Gastrointestinal Tract

作者：Wang, Shuai[1];Cong, Yang[1];Zhu, Hancan[2];Chen, Xianyi[3];Qu, Liangqiong[1];Fan, Huijie[1];Zhang, Qiang[4];Liu, Mingxia[5]

机构：[1]Chinese Acad Sci, State Key Lab Robot, Shenyang Inst Automat, Shenyang 110016, Peoples R China;[2]Shaoxing Univ, Sch Math Phys & Informat, Shaoxing 312000, Peoples R China;[3]Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Peoples R China;[4]Dalian Univ Technol, Sch Comp Sci & Technol, Dalian 116024, Peoples R China;[5]Taishan Univ, Dept Informat Sci & Technol, Tai An 271000, Shandong, Peoples R China

年份：2021

卷号：25

期号：2

起止页码：514

外文期刊名：IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS

收录：SCI-EXPANDED(收录号：WOS:000616310200021)、、EI(收录号：20210709918704)、Scopus(收录号：2-s2.0-85100823832)、WOS

基金：This work was supported in part by the National Natural Science Foundation of China under Grants 61703301 and 61602307, in part by the Natural Science Foundation of Zhejiang Province under Grant LY19F020013, in part by the Taishan Scholar Program of Shandong Province in China, in part by the Shandong Provincial Natural Science Foundation under Grant ZR2019YQ27, and in part by the Scientific Research Foundation of Taishan University under Grant Y-01-2018019.

语种：英文

外文关键词：Multi-scale Context; fully convolutional network; lesion segmentation; endoscopy image; gastrointestinal tract

外文摘要：Accurate lesion segmentation based on endoscopy images is a fundamental task for the automated diagnosis of gastrointestinal tract (GI Tract) diseases. Previous studies usually use hand-crafted features for representing endoscopy images, while feature definition and lesion segmentation are treated as two standalone tasks. Due to the possible heterogeneity between features and segmentation models, these methods often result in suboptimal performance. Several fully convolutional networks have been recently developed to jointly perform feature learning and model training for GI Tract disease diagnosis. However, they generally ignore local spatial details of endoscopy images, as down-sampling operations (e.g., pooling and convolutional striding) may result in irreversible loss of image spatial information. To this end, we propose a multi-scale context-guided deep network (MCNet) for end-to-end lesion segmentation of endoscopy images in GI Tract, where both global and local contexts are captured as guidance for model training. Specifically, one global subnetwork is designed to extract the global structure and high-level semantic context of each input image. Then we further design two cascaded local subnetworks based on output feature maps of the global subnetwork, aiming to capture both local appearance information and relatively high-level semantic information in a multi-scale manner. Those feature maps learned by three subnetworks are further fused for the subsequent task of lesion segmentation. We have evaluated the proposed MCNet on 1,310 endoscopy images from the public EndoVis-Ab and CVC-ClinicDB datasets for abnormal segmentation and polyp segmentation, respectively. Experimental results demonstrate that MCNet achieves 74% and 85% mean intersection over union (mloU) on two datasets, respectively, outperforming several state-of-the-art approaches in automated lesion segmentation with endoscopy images of GI Tract.