{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T23:07:28Z","timestamp":1792796848734,"version":"4.5.0"},"reference-count":41,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T00:00:00Z","timestamp":1669161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Spanish Government","award":["PID2019-105789RB-I00"],"award-info":[{"award-number":["PID2019-105789RB-I00"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>To completely comprehend neurodevelopment in healthy and congenitally abnormal fetuses, quantitative analysis of the human fetal brain is essential. This analysis requires the use of automatic multi-tissue fetal brain segmentation techniques. This paper proposes an end-to-end automatic yet effective method for a multi-tissue fetal brain segmentation model called IRMMNET. It includes a inception residual encoder block (EB) and a dense spatial attention (DSAM) block, which facilitate the extraction of multi-scale fetal-brain-tissue-relevant information from multi-view MRI images, enhance the feature reuse, and substantially reduce the number of parameters of the segmentation model. Additionally, we propose three methods for predicting gestational age (GA)\u2014GA prediction by using a 3D autoencoder, GA prediction using radiomics features, and GA prediction using the IRMMNET segmentation model\u2019s encoder. Our experiments were performed on a dataset of 80 pathological and non-pathological magnetic resonance fetal brain volume reconstructions across a range of gestational ages (20 to 33 weeks) that were manually segmented into seven different tissue categories. The results showed that the proposed fetal brain segmentation model achieved a Dice score of 0.791\u00b10.18, outperforming the state-of-the-art methods. The radiomics-based GA prediction methods achieved the best results (RMSE: 1.42). We also demonstrated the generalization capabilities of the proposed methods for tasks such as head and neck tumor segmentation and the prediction of patients\u2019 survival days.<\/jats:p>","DOI":"10.3390\/e24121708","type":"journal-article","created":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T03:48:12Z","timestamp":1669175292000},"page":"1708","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Effective Approaches to Fetal Brain Segmentation in MRI and Gestational Age Estimation by Utilizing a Multiview Deep Inception Residual Network and Radiomics"],"prefix":"10.3390","volume":"24","author":[{"given":"Moona","family":"Mazher","sequence":"first","affiliation":[{"name":"Departament d\u2019Enginyeria Informatica i Matem\u00e1tiques, Universitat Rovira i Virgili, 43007 Tarragona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdul","family":"Qayyum","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering and Imaging Sciences, Kings College London, London SE1 9RT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0562-4205","authenticated-orcid":false,"given":"Domenec","family":"Puig","sequence":"additional","affiliation":[{"name":"Departament d\u2019Enginyeria Informatica i Matem\u00e1tiques, Universitat Rovira i Virgili, 43007 Tarragona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1074-2441","authenticated-orcid":false,"given":"Mohamed","family":"Abdel-Nasser","sequence":"additional","affiliation":[{"name":"Electronics and Communication Engineering Section, Electrical Engineering Department, Aswan University, Aswan 81528, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"116324","DOI":"10.1016\/j.neuroimage.2019.116324","article-title":"An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI","volume":"206","author":"Ebner","year":"2020","journal-title":"NeuroImage"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"126.e1","DOI":"10.1016\/j.ajog.2013.04.008","article-title":"Differences in cortical development assessed by fetal MRI in late-onset intrauterine growth restriction","volume":"209","author":"Figueras","year":"2013","journal-title":"Am. 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