{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T01:17:29Z","timestamp":1777511849379,"version":"3.51.4"},"reference-count":43,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,28]],"date-time":"2021-11-28T00:00:00Z","timestamp":1638057600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"publisher","award":["1R21DK123569-01"],"award-info":[{"award-number":["1R21DK123569-01"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000275","name":"Leverhulme Trust","doi-asserted-by":"publisher","award":["LTRF1920\\16\\26"],"award-info":[{"award-number":["LTRF1920\\16\\26"]}],"id":[{"id":"10.13039\/501100000275","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>There is a growing demand for fast, accurate computation of clinical markers to improve renal function and anatomy assessment with a single study. However, conventional techniques have limitations leading to overestimations of kidney function or failure to provide sufficient spatial resolution to target the disease location. In contrast, the computer-aided analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) could generate significant markers, including the glomerular filtration rate (GFR) and time\u2013intensity curves of the cortex and medulla for determining obstruction in the urinary tract. This paper presents a dual-stage fully modular framework for automatic renal compartment segmentation in 4D DCE-MRI volumes. (1) Memory-efficient 3D deep learning is integrated to localise each kidney by harnessing residual convolutional neural networks for improved convergence; segmentation is performed by efficiently learning spatial\u2013temporal information coupled with boundary-preserving fully convolutional dense nets. (2) Renal contextual information is enhanced via non-linear transformation to segment the cortex and medulla. The proposed framework is evaluated on a paediatric dataset containing 60 4D DCE-MRI volumes exhibiting varying conditions affecting kidney function. Our technique outperforms a state-of-the-art approach based on a GrabCut and support vector machine classifier in mean dice similarity (DSC) by 3.8% and demonstrates higher statistical stability with lower standard deviation by 12.4% and 15.7% for cortex and medulla segmentation, respectively.<\/jats:p>","DOI":"10.3390\/s21237942","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"7942","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Improving Automatic Renal Segmentation in Clinically Normal and Abnormal Paediatric DCE-MRI via Contrast Maximisation and Convolutional Networks for Computing Markers of Kidney Function"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7251-9464","authenticated-orcid":false,"given":"Hykoush","family":"Asaturyan","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, University of Westminster, London W1W 6UW, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Barbara","family":"Villarini","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Westminster, London W1W 6UW, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karen","family":"Sarao","sequence":"additional","affiliation":[{"name":"Department of Radiology, Harvard Medical School and Boston Children\u2019s Hospital, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeanne S.","family":"Chow","sequence":"additional","affiliation":[{"name":"Department of Radiology, Harvard Medical School and Boston Children\u2019s Hospital, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Onur","family":"Afacan","sequence":"additional","affiliation":[{"name":"Department of Radiology, Harvard Medical School and Boston Children\u2019s Hospital, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sila","family":"Kurugol","sequence":"additional","affiliation":[{"name":"Department of Radiology, Harvard Medical School and Boston Children\u2019s Hospital, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1093\/ckj\/sfw134","article-title":"International Society of Nephrology\u2019s oby25 initiative (zero preventable deaths from acute kidney injury by 2025): Focus on diagnosis of acute kidney injury in low-income countries","volume":"11","author":"Raimann","year":"2018","journal-title":"Clin. 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