MANS-Net: Multiple Attention-Based Nuclei Segmentation in Multi Organ Digital Cancer Histopathology Images
The segmentation of nuclei is critical in histopathology investigations. The segmentation of images of nuclei is difficult in variable clinical conditions. Some deep learning methods were recently proposed; however, these approaches rarely provide solutions to clinical challenges. Since most of the...
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| Format: | Article |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10758637/ |
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| author | Ibtihaj Ahmad Zain Ul Islam Saleem Riaz Fuzhong Xue |
| author_facet | Ibtihaj Ahmad Zain Ul Islam Saleem Riaz Fuzhong Xue |
| author_sort | Ibtihaj Ahmad |
| collection | DOAJ |
| description | The segmentation of nuclei is critical in histopathology investigations. The segmentation of images of nuclei is difficult in variable clinical conditions. Some deep learning methods were recently proposed; however, these approaches rarely provide solutions to clinical challenges. Since most of the information in histopathology images is in the color channels, and the remaining is in the spatial patterns, distributions, etc., these problems can be handled by considering all the associated features simultaneously. This work suggests a novel multiple attention-based model, MANS-Net, that utilizes channel, spatial, and transformer-based attention modules to address the problems mentioned above. MANS-Net efficiently learns color-based, spatial, rough, and granular features, improving nuclei segmentation. We report that MANS-Net significantly outperforms state-of-the-art segmentation algorithms. We achieve F1 score of 0.8457 for PanNuke dataset and F1 score of 0.8137 for Kumar dataset. We show that the suggested model is more lightweight than the state-of-the-art approaches. The suggested model will impact future works that depend on semantic segmentation, such as nuclei instance segmentation and nuclei categorization. |
| format | Article |
| id | doaj-art-df58bb6e04a14e28af587beb51253f7b |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-df58bb6e04a14e28af587beb51253f7b2024-11-26T00:01:32ZengIEEEIEEE Access2169-35362024-01-011217353017353910.1109/ACCESS.2024.350276610758637MANS-Net: Multiple Attention-Based Nuclei Segmentation in Multi Organ Digital Cancer Histopathology ImagesIbtihaj Ahmad0https://orcid.org/0000-0002-6628-6967Zain Ul Islam1https://orcid.org/0000-0001-9499-3290Saleem Riaz2https://orcid.org/0000-0001-7818-2578Fuzhong Xue3Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, ChinaDivision of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarSchool of Automation, Northwestern Polytechnical University, Xi’an, Shaanxi, ChinaDepartment of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, ChinaThe segmentation of nuclei is critical in histopathology investigations. The segmentation of images of nuclei is difficult in variable clinical conditions. Some deep learning methods were recently proposed; however, these approaches rarely provide solutions to clinical challenges. Since most of the information in histopathology images is in the color channels, and the remaining is in the spatial patterns, distributions, etc., these problems can be handled by considering all the associated features simultaneously. This work suggests a novel multiple attention-based model, MANS-Net, that utilizes channel, spatial, and transformer-based attention modules to address the problems mentioned above. MANS-Net efficiently learns color-based, spatial, rough, and granular features, improving nuclei segmentation. We report that MANS-Net significantly outperforms state-of-the-art segmentation algorithms. We achieve F1 score of 0.8457 for PanNuke dataset and F1 score of 0.8137 for Kumar dataset. We show that the suggested model is more lightweight than the state-of-the-art approaches. The suggested model will impact future works that depend on semantic segmentation, such as nuclei instance segmentation and nuclei categorization.https://ieeexplore.ieee.org/document/10758637/Nuclei segmentationhistology segmentationattentiontransformercancer segmentationhistopathology segmentation |
| spellingShingle | Ibtihaj Ahmad Zain Ul Islam Saleem Riaz Fuzhong Xue MANS-Net: Multiple Attention-Based Nuclei Segmentation in Multi Organ Digital Cancer Histopathology Images IEEE Access Nuclei segmentation histology segmentation attention transformer cancer segmentation histopathology segmentation |
| title | MANS-Net: Multiple Attention-Based Nuclei Segmentation in Multi Organ Digital Cancer Histopathology Images |
| title_full | MANS-Net: Multiple Attention-Based Nuclei Segmentation in Multi Organ Digital Cancer Histopathology Images |
| title_fullStr | MANS-Net: Multiple Attention-Based Nuclei Segmentation in Multi Organ Digital Cancer Histopathology Images |
| title_full_unstemmed | MANS-Net: Multiple Attention-Based Nuclei Segmentation in Multi Organ Digital Cancer Histopathology Images |
| title_short | MANS-Net: Multiple Attention-Based Nuclei Segmentation in Multi Organ Digital Cancer Histopathology Images |
| title_sort | mans net multiple attention based nuclei segmentation in multi organ digital cancer histopathology images |
| topic | Nuclei segmentation histology segmentation attention transformer cancer segmentation histopathology segmentation |
| url | https://ieeexplore.ieee.org/document/10758637/ |
| work_keys_str_mv | AT ibtihajahmad mansnetmultipleattentionbasednucleisegmentationinmultiorgandigitalcancerhistopathologyimages AT zainulislam mansnetmultipleattentionbasednucleisegmentationinmultiorgandigitalcancerhistopathologyimages AT saleemriaz mansnetmultipleattentionbasednucleisegmentationinmultiorgandigitalcancerhistopathologyimages AT fuzhongxue mansnetmultipleattentionbasednucleisegmentationinmultiorgandigitalcancerhistopathologyimages |