{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T11:33:32Z","timestamp":1791804812588,"version":"4.3.4"},"reference-count":19,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,10,4]],"date-time":"2021-10-04T00:00:00Z","timestamp":1633305600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Youth Teacher Education Research Funds of Fujian","award":["JAT201044"],"award-info":[{"award-number":["JAT201044"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2021,10,4]]},"abstract":"<jats:p>The task of segmenting cytoplasm in cytology images is one of the most challenging tasks in cervix cytological analysis due to the presence of fuzzy and highly overlapping cells. Deep learning-based diagnostic technology has proven to be effective in segmenting complex medical images. We present a two-stage framework based on Mask RCNN to automatically segment overlapping cells. In stage one, candidate cytoplasm bounding boxes are proposed. In stage two, pixel-to-pixel alignment is used to refine the boundary and category classification is also presented. The performance of the proposed method is evaluated on publicly available datasets from ISBI 2014 and 2015. The experimental results demonstrate that our method outperforms other state-of-the-art approaches with DSC 0.92 and FPRp 0.0008 at the DSC threshold of 0.8. Those results indicate that our Mask RCNN-based segmentation method could be effective in cytological analysis.<\/jats:p>","DOI":"10.1155\/2021\/3890988","type":"journal-article","created":{"date-parts":[[2021,10,4]],"date-time":"2021-10-04T21:06:11Z","timestamp":1633381571000},"page":"1-10","source":"Crossref","is-referenced-by-count":24,"title":["Segmentation of Overlapping Cervical Cells with Mask Region Convolutional Neural Network"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0486-6410","authenticated-orcid":true,"given":"Jiajia","family":"Chen","sequence":"first","affiliation":[{"name":"Zhongshan Hospital Xiamen University, Xiamen, Fujian 361004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8421-6404","authenticated-orcid":true,"given":"Baocan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Chengyi University College, Jimei University, Xiamen, Fujian 361021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21262"},{"issue":"12","key":"2","doi-asserted-by":"crossref","first-page":"4145","DOI":"10.1016\/j.patcog.2012.05.006","article-title":"Unsupervised segmentation and classification of cervical cell images","volume":"45","author":"A. 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