{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T00:33:31Z","timestamp":1778286811370,"version":"3.51.4"},"reference-count":34,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,10,1]],"date-time":"2021-10-01T00:00:00Z","timestamp":1633046400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000664","name":"Health Technology Assessment Programme","doi-asserted-by":"publisher","award":["NIHR HTA 09\/06\/01"],"award-info":[{"award-number":["NIHR HTA 09\/06\/01"]}],"id":[{"id":"10.13039\/501100000664","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>In this work, we develop the Single-Input Multi-Output U-Net (SIMOU-Net), a hybrid network for foetal brain segmentation inspired by the original U-Net fused with the holistically nested edge detection (HED) network. The SIMOU-Net is similar to the original U-Net but it has a deeper architecture and takes account of the features extracted from each side output. It acts similar to an ensemble neural network, however, instead of averaging the outputs from several independently trained models, which is computationally expensive, our approach combines outputs from a single network to reduce the variance of predications and generalization errors. Experimental results using 200 normal foetal brains consisting of over 11,500 2D images produced Dice and Jaccard coefficients of 94.2 \u00b1 5.9% and 88.7 \u00b1 6.9%, respectively. We further tested the proposed network on 54 abnormal cases (over 3500 images) and achieved Dice and Jaccard coefficients of 91.2 \u00b1 6.8% and 85.7 \u00b1 6.6%, respectively.<\/jats:p>","DOI":"10.3390\/jimaging7100200","type":"journal-article","created":{"date-parts":[[2021,10,1]],"date-time":"2021-10-01T10:55:40Z","timestamp":1633085740000},"page":"200","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Single-Input Multi-Output U-Net for Automated 2D Foetal Brain Segmentation of MR Images"],"prefix":"10.3390","volume":"7","author":[{"given":"Andrik","family":"Rampun","sequence":"first","affiliation":[{"name":"Academic Unit of Radiology, Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield S10 2RX, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deborah","family":"Jarvis","sequence":"additional","affiliation":[{"name":"Academic Unit of Radiology, Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield S10 2RX, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul D.","family":"Griffiths","sequence":"additional","affiliation":[{"name":"Academic Unit of Radiology, Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield S10 2RX, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4360-0896","authenticated-orcid":false,"given":"Reyer","family":"Zwiggelaar","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Aberystwyth University, Wales SY23 3DB, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bryan W.","family":"Scotney","sequence":"additional","affiliation":[{"name":"School of Computing, Ulster University, Jordanstown, County Antrim BT37 0QB, Northern Ireland, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul A.","family":"Armitage","sequence":"additional","affiliation":[{"name":"Academic Unit of Radiology, Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield S10 2RX, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1016\/S0140-6736(16)31723-8","article-title":"Use of MRI in the diagnosis of fetal brain abnormalities in utero (MERIDIAN): A multicentre, prospective cohort study","volume":"389","author":"Griffiths","year":"2017","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"20130168","DOI":"10.1259\/bjr.20130168","article-title":"MRI of the foetal brain using a rapid 3D steady-state sequence","volume":"86","author":"Griffiths","year":"2013","journal-title":"Br. 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