{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T01:00:25Z","timestamp":1785632425024,"version":"3.56.0"},"reference-count":30,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2022,2,4]],"date-time":"2022-02-04T00:00:00Z","timestamp":1643932800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"fujian health science research talent training project","award":["2019-ZQN-17"],"award-info":[{"award-number":["2019-ZQN-17"]}]},{"name":"fujian science and technology innovation joint fund","award":["2018Y9112"],"award-info":[{"award-number":["2018Y9112"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Biomedical research is inseparable from the analysis of various histopathological images, and hematoxylin-eosin (HE)-stained images are one of the most basic and widely used types. However, at present, machine learning based approaches of the analysis of this kind of images are highly relied on manual labeling of images for training. Fully automated processing of HE-stained images remains a challenging task due to the high degree of color intensity, size and shape uncertainty of the stained cells. For this problem, we propose a fully automatic pixel-wise semantic segmentation method based on pseudo-labels, which concerns to significantly reduce the manual cell sketching and labeling work before machine learning, and guarantees the accuracy of segmentation. First, we collect reliable training samples in a unsupervised manner based on K-means clustering results; second, we use full mixup strategy to enhance the training images and to obtain the U-Net model for the nuclei segmentation from the background. The experimental results based on the meningioma pathology image dataset show that the proposed method has good performance and the pathological features obtained statistically based on the segmentation results can be used to assist in the clinical grading of meningiomas. Compared with other machine learning strategies, it can provide a reliable reference for clinical research more effectively.<\/jats:p>","DOI":"10.1371\/journal.pone.0263006","type":"journal-article","created":{"date-parts":[[2022,2,4]],"date-time":"2022-02-04T18:25:17Z","timestamp":1643999117000},"page":"e0263006","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":10,"title":["Segmentation of HE-stained meningioma pathological images based on pseudo-labels"],"prefix":"10.1371","volume":"17","author":[{"given":"Chongshu","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanping","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunjing","family":"Xue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7124-0210","authenticated-orcid":true,"given":"Peng","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2022,2,4]]},"reference":[{"issue":"2","key":"pone.0263006.ref001","first-page":"115","article-title":"Cancer statistics in China, 2015","volume":"66","author":"W Chen","year":"2016","journal-title":"CA: A Cancer Journal for Clinicians"},{"issue":"20","key":"pone.0263006.ref002","doi-asserted-by":"crossref","first-page":"3921","DOI":"10.3760\/cma.j.issn.0366-6999.20123368","article-title":"Clinicopathological classification and individualized treatment of breast cancer","volume":"126","author":"H Hu","year":"2013","journal-title":"Chinese Medical Journal"},{"issue":"1","key":"pone.0263006.ref003","doi-asserted-by":"crossref","first-page":"32","DOI":"10.4103\/0366-6999.221274","article-title":"Diagnosis and Differential Diagnosis of Desmoplastic Fibroblastoma by Clinical, Radiological, and Histopathological Analyses","volume":"131","author":"L Gong","year":"2018","journal-title":"Chinese Medical Journal"},{"key":"pone.0263006.ref004","first-page":"155","article-title":"Digital image analysis in breast cancer: an example of an automated methodology and the effects of image compression","volume":"179","author":"C Lopez","year":"2012","journal-title":"Studies in health technology and informatics"},{"key":"pone.0263006.ref005","first-page":"296","article-title":"A robust automatic nuclei segmentation technique for quantitative histopathological image analysis","volume":"34","author":"C Lu","year":"2012","journal-title":"Anal Quant Cytopatho"},{"key":"pone.0263006.ref006","doi-asserted-by":"crossref","first-page":"2044","DOI":"10.1109\/TMI.2018.2815013","article-title":"Multi-Pass Fast Watershed for Accurate Segmentation of Overlapping Cervical Cells","volume":"37","author":"A Tareef","year":"2018","journal-title":"IEEE Trans Med Imag"},{"key":"pone.0263006.ref007","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.media.2015.10.005","article-title":"High-throughput histopathological image analysis via robust cell segmentation and hashing","volume":"26","author":"X Zhang","year":"2015","journal-title":"Med Image Anal"},{"key":"pone.0263006.ref008","doi-asserted-by":"crossref","unstructured":"Taheri S, Fevens G T, Bui D T. 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