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Adaptive Multiphase Liver Tumor Segmentation With Multiscale Supervision

文献类型：期刊论文

作者Kuang, Haopeng1; Yang, Xue2; Li, Hongjun3; Wei, Jingwei4; Zhang, Lihua1
刊名IEEE SIGNAL PROCESSING LETTERS
出版日期2024
卷号31页码:426-430
关键词Tumors Feature extraction Liver Image segmentation Computed tomography Annotations Hospitals Liver tumor segmentation multi-phase computed tomography multi-scale supervision
ISSN号1070-9908
DOI10.1109/LSP.2024.3356414
通讯作者Wei, Jingwei(weijingwei2014@ia.ac.cn) ; Zhang, Lihua(lihuazhang@fudan.edu.cn)
英文摘要The segmentation of liver tumors using multi-phase computed tomography (CT) images has garnered considerable attention in medical signal processing. However, existing multi-phase liver tumor segmentation methods primarily concentrate on feature integration across various phases, neglecting a comprehensive exploration of synergistic relationships among these phases and constraints on features across different scales. This limitation has led to performance bottlenecks in existing approaches. This article proposes a robust multi-phase liver tumor segmentation framework designed to address the aforementioned challenges. Specifically, we introduce a novel multi-phase and channel-stacked dual attention module, seamlessly integrated within a multi-scale architecture. This module adaptively captures essential semantic information among different phases, enhancing the segmentation network's feature extraction capabilities. A scale-weighted loss function for multi-scale supervision is also designed to mitigate false positives in the segmentation results. To facilitate a systematic evaluation of our model's performance on multi-phase data, we curate a new dataset comprising samples from four distinct phases. Our proposed framework is rigorously assessed through comprehensive quantitative and qualitative experiments, highlighting its compelling performance.
资助项目National Natural Science Foundation of China
WOS研究方向Engineering
语种英语
WOS记录号WOS:001166718500004
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/57777]  
专题自动化研究所_中国科学院分子影像重点实验室
通讯作者Wei, Jingwei; Zhang, Lihua
作者单位1.Fudan Univ, Acad Engn & Technol, Shanghai 200433, Peoples R China
2.Zhengzhou Univ, Affiliated Hosp 1, Zhengzhou 450052, Henan, Peoples R China
3.Capital Med Univ, Beijing Youan Hosp, Beijing 100069, Peoples R China
4.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Kuang, Haopeng,Yang, Xue,Li, Hongjun,et al. Adaptive Multiphase Liver Tumor Segmentation With Multiscale Supervision[J]. IEEE SIGNAL PROCESSING LETTERS,2024,31:426-430.
APA Kuang, Haopeng,Yang, Xue,Li, Hongjun,Wei, Jingwei,&Zhang, Lihua.(2024).Adaptive Multiphase Liver Tumor Segmentation With Multiscale Supervision.IEEE SIGNAL PROCESSING LETTERS,31,426-430.
MLA Kuang, Haopeng,et al."Adaptive Multiphase Liver Tumor Segmentation With Multiscale Supervision".IEEE SIGNAL PROCESSING LETTERS 31(2024):426-430.

入库方式： OAI收割

来源：自动化研究所

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