{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T05:04:09Z","timestamp":1783487049498,"version":"3.55.0"},"reference-count":31,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T00:00:00Z","timestamp":1638230400000},"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>Accurate segmentation of fetal heart in echocardiography images is essential for detecting the structural abnormalities such as congenital heart defects (CHDs). Due to the wide variations attributed to different factors, such as maternal obesity, abdominal scars, amniotic fluid volume, and great vessel connections, this process is still a challenging problem. CHDs detection with expertise in general are substandard; the accuracy of measurements remains highly dependent on humans\u2019 training, skills, and experience. To make such a process automatic, this study proposes deep learning-based computer-aided fetal heart echocardiography examinations with an instance segmentation approach, which inherently segments the four standard heart views and detects the defect simultaneously. We conducted several experiments with 1149 fetal heart images for predicting 24 objects, including four shapes of fetal heart standard views, 17 objects of heart-chambers in each view, and three cases of congenital heart defect. The result showed that the proposed model performed satisfactory performance for standard views segmentation, with a 79.97% intersection over union and 89.70% Dice coefficient similarity. It also performed well in the CHDs detection, with mean average precision around 98.30% for intra-patient variation and 82.42% for inter-patient variation. We believe that automatic segmentation and detection techniques could make an important contribution toward improving congenital heart disease diagnosis rates.<\/jats:p>","DOI":"10.3390\/s21238007","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"8007","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":67,"title":["Deep Learning-Based Computer-Aided Fetal Echocardiography: Application to Heart Standard View Segmentation for Congenital Heart Defects Detection"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8024-2952","authenticated-orcid":false,"given":"Siti","family":"Nurmaini","sequence":"first","affiliation":[{"name":"Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Naufal","family":"Rachmatullah","sequence":"additional","affiliation":[{"name":"Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ade Iriani","family":"Sapitri","sequence":"additional","affiliation":[{"name":"Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0229-5717","authenticated-orcid":false,"given":"Annisa","family":"Darmawahyuni","sequence":"additional","affiliation":[{"name":"Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bambang","family":"Tutuko","sequence":"additional","affiliation":[{"name":"Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2791-3486","authenticated-orcid":false,"given":"Firdaus","family":"Firdaus","sequence":"additional","affiliation":[{"name":"Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Radiyati Umi","family":"Partan","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nuswil","family":"Bernolian","sequence":"additional","affiliation":[{"name":"Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Mohammad Hoesin General Hospital, Palembang 30126, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1017\/S1047951116000639","article-title":"Neonatal management and outcomes of prenatally diagnosed CHDs","volume":"27","author":"Bensemlali","year":"2017","journal-title":"Cardiol. 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