Weakly Supervised Nuclei Segmentation with Point-Guided Attention and Self-Supervised Pseudo-Labeling
Due to the labor-intensive manual annotations for nuclei segmentation, point-supervised segmentation based on nuclei coordinate supervision has gained recognition in recent years. Despite great progress, two challenges hinder the performance of weakly supervised nuclei segmentation methods: (1) The...
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MDPI AG
2025-01-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/12/1/85 |
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| author | Yapeng Mo Lijiang Chen Lingfeng Zhang Qi Zhao |
| author_facet | Yapeng Mo Lijiang Chen Lingfeng Zhang Qi Zhao |
| author_sort | Yapeng Mo |
| collection | DOAJ |
| description | Due to the labor-intensive manual annotations for nuclei segmentation, point-supervised segmentation based on nuclei coordinate supervision has gained recognition in recent years. Despite great progress, two challenges hinder the performance of weakly supervised nuclei segmentation methods: (1) The stable and effective segmentation of adjacent cell nuclei remains an unresolved challenge. (2) Existing approaches rely solely on initial pseudo-labels generated from point annotations for training, and inaccurate labels may lead the model to assimilate a considerable amount of noise information, thereby diminishing performance. To address these issues, we propose a method based on center-point prediction and pseudo-label updating for precise nuclei segmentation. First, we devise a Gaussian kernel mechanism that employs multi-scale Gaussian masks for multi-branch center-point prediction. The generated center points are utilized by the segmentation module to facilitate the effective separation of adjacent nuclei. Next, we introduce a point-guided attention mechanism that concentrates the segmentation module’s attention around authentic point labels, reducing the noise impact caused by pseudo-labels. Finally, a label updating mechanism based on the exponential moving average (EMA) and k-means clustering is introduced to enhance the quality of pseudo-labels. The experimental results on three public datasets demonstrate that our approach has achieved state-of-the-art performance across multiple metrics. This method can significantly reduce annotation costs and reliance on clinical experts, facilitating large-scale dataset training and promoting the adoption of automated analysis in clinical applications. |
| format | Article |
| id | doaj-art-d15d8c93419f45e99f8e1b7d71405948 |
| institution | Kabale University |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-d15d8c93419f45e99f8e1b7d714059482025-01-24T13:23:13ZengMDPI AGBioengineering2306-53542025-01-011218510.3390/bioengineering12010085Weakly Supervised Nuclei Segmentation with Point-Guided Attention and Self-Supervised Pseudo-LabelingYapeng Mo0Lijiang Chen1Lingfeng Zhang2Qi Zhao3Institute of Electronic Information Engineering, Beihang University, 37 Xueyuan Road, Haidian District, Beijing 100191, ChinaInstitute of Electronic Information Engineering, Beihang University, 37 Xueyuan Road, Haidian District, Beijing 100191, ChinaInstitute of Electronic Information Engineering, Beihang University, 37 Xueyuan Road, Haidian District, Beijing 100191, ChinaInstitute of Electronic Information Engineering, Beihang University, 37 Xueyuan Road, Haidian District, Beijing 100191, ChinaDue to the labor-intensive manual annotations for nuclei segmentation, point-supervised segmentation based on nuclei coordinate supervision has gained recognition in recent years. Despite great progress, two challenges hinder the performance of weakly supervised nuclei segmentation methods: (1) The stable and effective segmentation of adjacent cell nuclei remains an unresolved challenge. (2) Existing approaches rely solely on initial pseudo-labels generated from point annotations for training, and inaccurate labels may lead the model to assimilate a considerable amount of noise information, thereby diminishing performance. To address these issues, we propose a method based on center-point prediction and pseudo-label updating for precise nuclei segmentation. First, we devise a Gaussian kernel mechanism that employs multi-scale Gaussian masks for multi-branch center-point prediction. The generated center points are utilized by the segmentation module to facilitate the effective separation of adjacent nuclei. Next, we introduce a point-guided attention mechanism that concentrates the segmentation module’s attention around authentic point labels, reducing the noise impact caused by pseudo-labels. Finally, a label updating mechanism based on the exponential moving average (EMA) and k-means clustering is introduced to enhance the quality of pseudo-labels. The experimental results on three public datasets demonstrate that our approach has achieved state-of-the-art performance across multiple metrics. This method can significantly reduce annotation costs and reliance on clinical experts, facilitating large-scale dataset training and promoting the adoption of automated analysis in clinical applications.https://www.mdpi.com/2306-5354/12/1/85weakly supervised learningnuclei instance segmentationmulti-scale Gaussian kernelpoint-guided attentionpseudo-label updating |
| spellingShingle | Yapeng Mo Lijiang Chen Lingfeng Zhang Qi Zhao Weakly Supervised Nuclei Segmentation with Point-Guided Attention and Self-Supervised Pseudo-Labeling Bioengineering weakly supervised learning nuclei instance segmentation multi-scale Gaussian kernel point-guided attention pseudo-label updating |
| title | Weakly Supervised Nuclei Segmentation with Point-Guided Attention and Self-Supervised Pseudo-Labeling |
| title_full | Weakly Supervised Nuclei Segmentation with Point-Guided Attention and Self-Supervised Pseudo-Labeling |
| title_fullStr | Weakly Supervised Nuclei Segmentation with Point-Guided Attention and Self-Supervised Pseudo-Labeling |
| title_full_unstemmed | Weakly Supervised Nuclei Segmentation with Point-Guided Attention and Self-Supervised Pseudo-Labeling |
| title_short | Weakly Supervised Nuclei Segmentation with Point-Guided Attention and Self-Supervised Pseudo-Labeling |
| title_sort | weakly supervised nuclei segmentation with point guided attention and self supervised pseudo labeling |
| topic | weakly supervised learning nuclei instance segmentation multi-scale Gaussian kernel point-guided attention pseudo-label updating |
| url | https://www.mdpi.com/2306-5354/12/1/85 |
| work_keys_str_mv | AT yapengmo weaklysupervisednucleisegmentationwithpointguidedattentionandselfsupervisedpseudolabeling AT lijiangchen weaklysupervisednucleisegmentationwithpointguidedattentionandselfsupervisedpseudolabeling AT lingfengzhang weaklysupervisednucleisegmentationwithpointguidedattentionandselfsupervisedpseudolabeling AT qizhao weaklysupervisednucleisegmentationwithpointguidedattentionandselfsupervisedpseudolabeling |