{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T16:31:47Z","timestamp":1774369907401,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,30]],"date-time":"2021-12-30T00:00:00Z","timestamp":1640822400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the rise of deep learning, using deep learning to segment lesions and assist in diagnosis has become an effective means to promote clinical medical analysis. However, the partial volume effect of organ tissues leads to unclear and blurred edges of ROI in medical images, making it challenging to achieve high-accuracy segmentation of lesions or organs. In this paper, we assume that the distance map obtained by performing distance transformation on the ROI edge can be used as a weight map to make the network pay more attention to the learning of the ROI edge region. To this end, we design a novel framework to flexibly embed the distance map into the two-stage network to improve left atrium MRI segmentation performance. Furthermore, a series of distance map generation methods are proposed and studied to reasonably explore how to express the weight of assisting network learning. We conduct thorough experiments to verify the effectiveness of the proposed segmentation framework, and experimental results demonstrate that our hypothesis is feasible.<\/jats:p>","DOI":"10.3390\/s22010250","type":"journal-article","created":{"date-parts":[[2021,12,30]],"date-time":"2021-12-30T23:29:07Z","timestamp":1640906947000},"page":"250","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Two-Stage Segmentation Framework Based on Distance Transformation"],"prefix":"10.3390","volume":"22","author":[{"given":"Xiaoyang","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Computer Science, School of Informatics, Xiamen University, Xiamen 361005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhi","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Informatics, Xiamen University, Xiamen 361005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yudi","family":"Jiao","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Informatics, Xiamen University, Xiamen 361005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7093-1658","authenticated-orcid":false,"given":"Moon-Tong","family":"Chan","sequence":"additional","affiliation":[{"name":"School of Science and Technology, Hong Kong Metropolitan University, Homantin, Kowloon 999077, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaohui","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Informatics, Xiamen University, Xiamen 361005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liansheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Informatics, Xiamen University, Xiamen 361005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1016\/j.ultrasmedbio.2020.01.001","article-title":"Breast Cancer Classification in Automated Breast Ultrasound Using Multiview Convolutional Neural Network with Transfer Learning","volume":"46","author":"Wang","year":"2020","journal-title":"Ultrasound Med. 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