{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T13:16:31Z","timestamp":1775135791091,"version":"3.50.1"},"reference-count":64,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,3,29]],"date-time":"2021-03-29T00:00:00Z","timestamp":1616976000000},"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>Deep reinforcement learning (DRL) has been utilized in numerous computer vision tasks, such as object detection, autonomous driving, etc. However, relatively few DRL methods have been proposed in the area of image segmentation, particularly in left ventricle segmentation. Reinforcement learning-based methods in earlier works often rely on learning proper thresholds to perform segmentation, and the segmentation results are inaccurate due to the sensitivity of the threshold. To tackle this problem, a novel DRL agent is designed to imitate the human process to perform LV segmentation. For this purpose, we formulate the segmentation problem as a Markov decision process and innovatively optimize it through DRL. The proposed DRL agent consists of two neural networks, i.e., First-P-Net and Next-P-Net. The First-P-Net locates the initial edge point, and the Next-P-Net locates the remaining edge points successively and ultimately obtains a closed segmentation result. The experimental results show that the proposed model has outperformed the previous reinforcement learning methods and achieved comparable performances compared with deep learning baselines on two widely used LV endocardium segmentation datasets, namely Automated Cardiac Diagnosis Challenge (ACDC) 2017 dataset, and Sunnybrook 2009 dataset. Moreover, the proposed model achieves higher F-measure accuracy compared with deep learning methods when training with a very limited number of samples.<\/jats:p>","DOI":"10.3390\/s21072375","type":"journal-article","created":{"date-parts":[[2021,3,29]],"date-time":"2021-03-29T16:01:57Z","timestamp":1617033717000},"page":"2375","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Edge-Sensitive Left Ventricle Segmentation Using Deep Reinforcement Learning"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5288-3605","authenticated-orcid":false,"given":"Jingjing","family":"Xiong","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5185-1492","authenticated-orcid":false,"given":"Lai-Man","family":"Po","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9586-2812","authenticated-orcid":false,"given":"Kwok Wai","family":"Cheung","sequence":"additional","affiliation":[{"name":"School of Communication, The Hang Seng University of Hong Kong, Hang Shin Link, Siu Lek Yuen, Shatin, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5632-7780","authenticated-orcid":false,"given":"Pengfei","family":"Xian","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8561-2206","authenticated-orcid":false,"given":"Yuzhi","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2945-7181","authenticated-orcid":false,"given":"Yasar Abbas Ur","family":"Rehman","sequence":"additional","affiliation":[{"name":"TCL Corporate Research (HK) Co., Ltd., 22 Science Park East Avenue, Shatin, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3991-7388","authenticated-orcid":false,"given":"Yujia","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.1007\/s11517-015-1404-6","article-title":"Combining split-and-merge and multi-seed region growing algorithms for uterine fibroid segmentation in MRgFUS treatments","volume":"54","author":"Rundo","year":"2016","journal-title":"Med. 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