{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T05:34:06Z","timestamp":1781242446178,"version":"3.54.1"},"reference-count":37,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T00:00:00Z","timestamp":1643241600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007060","name":"Riga Technical University","doi-asserted-by":"publisher","award":["ERAF 1.1.1.1\/19\/A\/147"],"award-info":[{"award-number":["ERAF 1.1.1.1\/19\/A\/147"]}],"id":[{"id":"10.13039\/501100007060","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>U-Net is the most cited and widely-used deep learning model for biomedical image segmentation. In this paper, we propose a new enhanced version of a ubiquitous U-Net architecture, which improves upon the original one in terms of generalization capabilities, while addressing several immanent shortcomings, such as constrained resolution and non-resilient receptive fields of the main pathway. Our novel multi-path architecture introduces a notion of an individual receptive field pathway, which is merged with other pathways at the bottom-most layer by concatenation and subsequent application of Layer Normalization and Spatial Dropout, which can improve generalization performance for small datasets. In general, our experiments show that the proposed multi-path architecture outperforms other state-of-the-art approaches that embark on similar ideas of pyramid structures, skip-connections, and encoder\u2013decoder pathways. A significant improvement of the Dice similarity coefficient is attained at our proprietary colony-forming unit dataset, where a score of 0.809 was achieved for the foreground class.<\/jats:p>","DOI":"10.3390\/s22030990","type":"journal-article","created":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T22:01:57Z","timestamp":1643320917000},"page":"990","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Multi-Path U-Net Architecture for Cell and Colony-Forming Unit Image Segmentation"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4771-2411","authenticated-orcid":false,"given":"Vilen","family":"Jumutc","sequence":"first","affiliation":[{"name":"Institute of Smart Computer Technologies, Riga Technical University, LV-1658 Riga, Latvia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4252-9220","authenticated-orcid":false,"given":"Dmitrijs","family":"B\u013ciz\u0146uks","sequence":"additional","affiliation":[{"name":"Institute of Smart Computer Technologies, Riga Technical University, LV-1658 Riga, Latvia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4178-895X","authenticated-orcid":false,"given":"Alexey","family":"Lihachev","sequence":"additional","affiliation":[{"name":"Institute of Atomic Physics and Spectroscopy, University of Latvia, LV-1586 Riga, Latvia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,27]]},"reference":[{"key":"ref_1","first-page":"234","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"Volume 9351","author":"Ronneberger","year":"2015","journal-title":"Medical Image Computing and Computer-Assisted Intervention (MICCAI)"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/978-3-319-46976-8_19","article-title":"The Importance of Skip Connections in Biomedical Image Segmentation","volume":"Volume 10008","author":"Drozdzal","year":"2016","journal-title":"Deep Learning and Data Labeling for Medical Applications"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1007\/s42452-019-0694-y","article-title":"A U-net based approach to epidermal tissue segmentation in whole slide histopathological images","volume":"1","author":"Oskal","year":"2019","journal-title":"SN Appl. 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