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In this study, we propose a convolutional neural network with foveal blur enriching datasets with multiple local nuclei regions of interest derived from original pathology images. We further propose a human-knowledge boosted deep learning system by inclusion to the convolutional neural network new loss function terms capturing shape prior knowledge and imposing smoothness constraints on the predicted probability maps.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Our proposed system outperforms all state-of-the-art deep learning and non-deep learning methods by Jaccard coefficient, Dice coefficient, Accuracy and Panoptic Quality in three independent datasets. The high segmentation accuracy and execution speed suggest its promising potential for automating histopathology nuclei segmentation in biomedical research and clinical settings.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>The codes, the documentation and example data are available on an open source at: https:\/\/github.com\/HongyiDuanmu26\/FovealBoosted.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab418","type":"journal-article","created":{"date-parts":[[2021,6,2]],"date-time":"2021-06-02T20:46:40Z","timestamp":1622666800000},"page":"3905-3913","source":"Crossref","is-referenced-by-count":6,"title":["Foveal blur-boosted segmentation of nuclei in histopathology images with shape prior knowledge and probability map constraints"],"prefix":"10.1093","volume":"37","author":[{"given":"Hongyi","family":"Duanmu","sequence":"first","affiliation":[{"name":"Department of Computer Science, Stony Brook University , Stony Brook, NY 11794, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9369-9361","authenticated-orcid":false,"given":"Fusheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Stony Brook University , Stony Brook, NY 11794, USA"},{"name":"Department of Biomedical Informatics, Stony Brook University , Stony Brook, NY 11794, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6289-3914","authenticated-orcid":false,"given":"George","family":"Teodoro","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Federal University of Minas Gerais , Belo Horizonte 31270-901, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8757-8339","authenticated-orcid":false,"given":"Jun","family":"Kong","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics and Computer Science, Georgia State University , Atlanta, GA 30303, USA"},{"name":"Department of Computer Science and Winship Cancer Institute, Emory University , Atlanta, GA 30322, USA"}]}],"member":"286","published-online":{"date-parts":[[2021,6,3]]},"reference":[{"key":"2024041100100130300_btab418-B1","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1007\/978-3-030-32245-8_11","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Bertels","year":"2019"},{"key":"2024041100100130300_btab418-B2","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1109\/TCSVT.2004.842603","article-title":"Foveated shot detection for video segmentation","volume":"15","author":"Boccignone","year":"2005","journal-title":"IEEE Trans. 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