Boundary aware microscopic hyperspectral pathology image segmentation network guided by information entropy weight
IntroductionAccurate segmentation of lesion tissues in medical microscopic hyperspectral pathological images is crucial for enhancing early tumor diagnosis and improving patient prognosis. However, the complex structure and indistinct boundaries of lesion tissues present significant challenges in ac...
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1549544/full |
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| author | Xueying Cao Hongmin Gao Ting Qin Min Zhu Ping Zhang Ping Zhang Peipei Xu Peipei Xu |
| author_facet | Xueying Cao Hongmin Gao Ting Qin Min Zhu Ping Zhang Ping Zhang Peipei Xu Peipei Xu |
| author_sort | Xueying Cao |
| collection | DOAJ |
| description | IntroductionAccurate segmentation of lesion tissues in medical microscopic hyperspectral pathological images is crucial for enhancing early tumor diagnosis and improving patient prognosis. However, the complex structure and indistinct boundaries of lesion tissues present significant challenges in achieving precise segmentation.MethodsTo address these challenges, we propose a novel method named BE-Net. It employs multi-scale strategy and edge operators to capture fine edge details, while incorporating information entropy to construct attention mechanisms that further strengthen the representation of relevant features. Specifically, we first propose a Laplacian of Gaussian operator convolution boundary feature extraction block, which encodes feature gradient information through the improved edge detection operators and emphasizes relevant boundary channel weights based on channel information entropy weighting. We further designed a grouped multi-scale edge feature extraction module to optimize the fusion process between the encoder and decoder, with the goal of optimize boundary details and emphasizing relevant channel representations. Finally, we propose a multi-scale spatial boundary feature extraction block to guide the model in emphasizing the most important spatial locations and boundary regions.ResultWe evaluate BE-Net on medical microscopic hyperspectral pathological image datasets of gastric intraepithelial neoplasia and gastric mucosal intestinal metaplasia. Experimental results demonstrate that BE-Net outperforms other state-of-the-art segmentation methods in terms of accuracy and boundary preservation.DiscussionThis advance has significant implications for the field of MHSIs segmentation. Our code is freely available at https://github.com/sharycao/BE-NET. |
| format | Article |
| id | doaj-art-0f958adc457c4982a8460c41e1cd4ef6 |
| institution | DOAJ |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Oncology |
| spelling | doaj-art-0f958adc457c4982a8460c41e1cd4ef62025-08-20T02:49:43ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-03-011510.3389/fonc.2025.15495441549544Boundary aware microscopic hyperspectral pathology image segmentation network guided by information entropy weightXueying Cao0Hongmin Gao1Ting Qin2Min Zhu3Ping Zhang4Ping Zhang5Peipei Xu6Peipei Xu7College of Computer Science and Software Engineering, Hohai University, Nanjing, ChinaCollege of Computer Science and Software Engineering, Hohai University, Nanjing, ChinaDepartment of Hematology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, ChinaCollege of Computer Science and Software Engineering, Hohai University, Nanjing, ChinaSchool of Information Management & Engineering, Shanghai University of Finance and Economics, Shanghai, ChinaCollege of International Exchange, Nanjing Normal University of Special Education, Nanjing, ChinaDepartment of Hematology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, ChinaDepartment of Hematology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, ChinaIntroductionAccurate segmentation of lesion tissues in medical microscopic hyperspectral pathological images is crucial for enhancing early tumor diagnosis and improving patient prognosis. However, the complex structure and indistinct boundaries of lesion tissues present significant challenges in achieving precise segmentation.MethodsTo address these challenges, we propose a novel method named BE-Net. It employs multi-scale strategy and edge operators to capture fine edge details, while incorporating information entropy to construct attention mechanisms that further strengthen the representation of relevant features. Specifically, we first propose a Laplacian of Gaussian operator convolution boundary feature extraction block, which encodes feature gradient information through the improved edge detection operators and emphasizes relevant boundary channel weights based on channel information entropy weighting. We further designed a grouped multi-scale edge feature extraction module to optimize the fusion process between the encoder and decoder, with the goal of optimize boundary details and emphasizing relevant channel representations. Finally, we propose a multi-scale spatial boundary feature extraction block to guide the model in emphasizing the most important spatial locations and boundary regions.ResultWe evaluate BE-Net on medical microscopic hyperspectral pathological image datasets of gastric intraepithelial neoplasia and gastric mucosal intestinal metaplasia. Experimental results demonstrate that BE-Net outperforms other state-of-the-art segmentation methods in terms of accuracy and boundary preservation.DiscussionThis advance has significant implications for the field of MHSIs segmentation. Our code is freely available at https://github.com/sharycao/BE-NET.https://www.frontiersin.org/articles/10.3389/fonc.2025.1549544/fullmicroscopic hyperspectral imageboundary-awareinformation entropyattention mechanismmulti-scale |
| spellingShingle | Xueying Cao Hongmin Gao Ting Qin Min Zhu Ping Zhang Ping Zhang Peipei Xu Peipei Xu Boundary aware microscopic hyperspectral pathology image segmentation network guided by information entropy weight Frontiers in Oncology microscopic hyperspectral image boundary-aware information entropy attention mechanism multi-scale |
| title | Boundary aware microscopic hyperspectral pathology image segmentation network guided by information entropy weight |
| title_full | Boundary aware microscopic hyperspectral pathology image segmentation network guided by information entropy weight |
| title_fullStr | Boundary aware microscopic hyperspectral pathology image segmentation network guided by information entropy weight |
| title_full_unstemmed | Boundary aware microscopic hyperspectral pathology image segmentation network guided by information entropy weight |
| title_short | Boundary aware microscopic hyperspectral pathology image segmentation network guided by information entropy weight |
| title_sort | boundary aware microscopic hyperspectral pathology image segmentation network guided by information entropy weight |
| topic | microscopic hyperspectral image boundary-aware information entropy attention mechanism multi-scale |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1549544/full |
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