{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T18:59:20Z","timestamp":1771700360656,"version":"3.50.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,11,12]],"date-time":"2022-11-12T00:00:00Z","timestamp":1668211200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,12]],"date-time":"2022-11-12T00:00:00Z","timestamp":1668211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Netw Model Anal Health Inform Bioinforma"],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1007\/s13721-022-00394-y","type":"journal-article","created":{"date-parts":[[2022,11,12]],"date-time":"2022-11-12T04:29:27Z","timestamp":1668227367000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["MTSE U-Net: an architecture for segmentation, and prediction of fetal brain and gestational age from MRI of brain"],"prefix":"10.1007","volume":"11","author":[{"given":"Tuhinangshu","family":"Gangopadhyay","sequence":"first","affiliation":[]},{"given":"Shinjini","family":"Halder","sequence":"additional","affiliation":[]},{"given":"Paramik","family":"Dasgupta","sequence":"additional","affiliation":[]},{"given":"Kingshuk","family":"Chatterjee","sequence":"additional","affiliation":[]},{"given":"Debayan","family":"Ganguly","sequence":"additional","affiliation":[]},{"given":"Surjadeep","family":"Sarkar","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5161-9311","authenticated-orcid":false,"given":"Sudipta","family":"Roy","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,12]]},"reference":[{"key":"394_CR1","first-page":"86","volume-title":"Annals of Neurology","author":"JG Chi","year":"1977","unstructured":"Chi JG, Dooling EC, Gilles FH (1977) Gyral development of the human brain. Annals of Neurology. Wiley, New York, pp 86\u201393"},{"key":"394_CR2","first-page":"424","volume-title":"International conference on medical image computing and computer-assisted intervention","author":"\u00d6 \u00c7i\u00e7ek","year":"2016","unstructured":"\u00c7i\u00e7ek \u00d6, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3D U-Net: learning dense volumetric segmentation from sparse annotation. International conference on medical image computing and computer-assisted intervention. Springer, Cham, pp 424\u2013432"},{"issue":"4","key":"394_CR3","doi-asserted-by":"publisher","first-page":"691","DOI":"10.3390\/diagnostics11040691","volume":"11","author":"N-T Do","year":"2021","unstructured":"Do N-T, Jung S-T, Yang H-J, Kim S-H (2021) Multi-Level Seg-Unet Model with Global and Patch-Based X-ray Images for Knee Bone Tumor Detection In Diagnostics. MDPI AG 11(4):691. https:\/\/doi.org\/10.3390\/diagnostics11040691","journal-title":"MDPI AG"},{"key":"394_CR5","first-page":"321","volume-title":"brain","author":"CR Gale","year":"2004","unstructured":"Gale CR (2004) Critical periods of brain growth and cognitive function in children. brain. Oxford University Press (OUP), Oxford, pp 321\u2013329"},{"key":"394_CR6","doi-asserted-by":"publisher","unstructured":"Hagerty, Jason Stanley, Ronald Stoecker, William. (2017). Medical Image Processing in the Age of Deep Learning-Is There Still Room for Conventional Medical Image Processing Techniques? https:\/\/doi.org\/10.5220\/0006273803060311.","DOI":"10.5220\/0006273803060311"},{"issue":"10","key":"394_CR7","doi-asserted-by":"publisher","first-page":"1431","DOI":"10.1016\/j.mri.2010.06.024","volume":"28","author":"IA Hosny","year":"2010","unstructured":"Hosny IA, Elghawabi HS (2010) Ultrafast MRI of the fetus: an increasingly important tool in prenatal diagnosis of congenital anomalies. Magn Reson Imaging 28(10):1431\u20131439. https:\/\/doi.org\/10.1016\/j.mri.2010.06.024 (Epub 2010 Sep 17 PMID: 20850244)","journal-title":"Magn Reson Imaging"},{"issue":"8","key":"394_CR8","doi-asserted-by":"publisher","first-page":"1525","DOI":"10.3174\/ajnr.A6635","volume":"41","author":"C Jaimes","year":"2020","unstructured":"Jaimes C, Rofeberg V, Stopp C, Ortinau CM, Gholipour A, Friedman KG, Tworetzky W, Estroff J, Newburger JW, Wypij D, Warfield SK, Yang E, Rollins CK (2020) Association of Isolated Congenital Heart Disease with Fetal Brain Maturation. Am J Neuroradiol 41(8):1525\u20131531","journal-title":"Am J Neuroradiol"},{"key":"394_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.15623\/ijret.2014.0313001","volume":"03","author":"R Joseph","year":"2014","unstructured":"Joseph R (2014) Brain tumor mri image segmentation and detection in image processing. Int Jurnal Res Eng Technol 03:1\u20135. https:\/\/doi.org\/10.15623\/ijret.2014.0313001","journal-title":"Int Jurnal Res Eng Technol"},{"key":"394_CR10","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.mri.2019.05.020","volume":"64","author":"N Khalili","year":"2019","unstructured":"Khalili N, Lessmann N, Turk E, Claessens N, de Heus R, Kolk T, Viergever MA, Benders MJNL, I\u0161gum I (2019) Automatic brain tissue segmentation in fetal MRI using convolutional neural networks. Magn Reson Imaging 64:77\u201389. https:\/\/doi.org\/10.1016\/j.mri.2019.05.020","journal-title":"Magn Reson Imaging"},{"key":"394_CR11","doi-asserted-by":"publisher","first-page":"112001","DOI":"10.1289\/ehp2268","volume":"126","author":"L Konkel","year":"2018","unstructured":"Konkel L (2018) The brain before birth: using fMRI to explore the secrets of fetal neurodevelopment in environmental health perspectives. Environ Health Perspect 126:112001. https:\/\/doi.org\/10.1289\/ehp2268","journal-title":"Environ Health Perspect"},{"issue":"1","key":"394_CR12","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1097\/00002142-200102000-00004","volume":"12","author":"D Levine","year":"2001","unstructured":"Levine D (2001) Ultrasound versus magnetic resonance imaging in fetal evaluation. Topic Magn Resonan Imaging 12(1):25\u201338","journal-title":"Topic Magn Resonan Imaging"},{"key":"394_CR13","first-page":"532","volume-title":"NeuroImage: Clinical","author":"J Levman","year":"2015","unstructured":"Levman J, Takahashi E (2015) Multivariate analyses applied to fetal, neonatal and pediatric MRI of neurodevelopmental disorders. NeuroImage: Clinical. Elsevier BV, Amsterdam, pp 532\u2013544"},{"key":"394_CR14","doi-asserted-by":"publisher","unstructured":"Liao L et al (2020) Multi-Branch Deformable Convolutional Neural Network with Label Distribution Learning for Fetal Brain Age Prediction: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). pp 424\u2013427. doi: https:\/\/doi.org\/10.1109\/ISBI45749.2020.9098553","DOI":"10.1109\/ISBI45749.2020.9098553"},{"key":"394_CR15","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1007\/978-3-030-32692-0_68","volume-title":"Machine Learning in Medical Imaging","author":"J Lou","year":"2019","unstructured":"Lou J, Li D, Bui TD, Zhao F, Sun L, Li G, Shen D (2019) Automatic fetal brain extraction using multi-stage U-Net with deep supervision. Machine Learning in Medical Imaging. Springer International Publishing, Cham, pp 592\u2013600"},{"key":"394_CR16","first-page":"155","volume":"156","author":"RM Murray","year":"1991","unstructured":"Murray RM, Jones P, O\u2019Callaghan E (1991) Fetal brain development and later schizophrenia. Child Environ Adult Dis 156:155","journal-title":"Child Environ Adult Dis"},{"key":"394_CR17","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1038\/s41597-021-00946-3","volume":"8","author":"K Payette","year":"2021","unstructured":"Payette K, de Dumast P, Kebiri H et al (2021) An automatic multi-tissue human fetal brain segmentation benchmark using the fetal tissue annotation dataset. Sci Data 8:167. https:\/\/doi.org\/10.1038\/s41597-021-00946-3","journal-title":"Sci Data"},{"key":"394_CR18","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1606.01100","author":"M Rajchl","year":"2016","unstructured":"Rajchl M, Lee MCH, Schrans F, Davidson A, PasseratPalmbach J, Tarroni G, Alansary A, Oktay O, Kainz B, Rueckert D (2016) Learning under distributed weak supervision (Version 1). Arxiv. https:\/\/doi.org\/10.48550\/ARXIV.1606.01100","journal-title":"Arxiv"},{"key":"394_CR19","doi-asserted-by":"crossref","unstructured":"Rampun A, Jarvis D, Griffiths P, Armitage P (2019) Automated 2D fetal brain segmentation of mr images using a deep u-net. In Asian Conference on Pattern Recognition. Springer, Cham 373\u2013386","DOI":"10.1007\/978-3-030-41299-9_29"},{"key":"394_CR20","first-page":"234","volume-title":"In international conference on medical image computing and computer-assisted intervention","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In international conference on medical image computing and computer-assisted intervention. Springer, Cham, pp 234\u2013241"},{"key":"394_CR21","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1016\/j.procs.2016.05.244","volume":"85","author":"S Roy","year":"2016","unstructured":"Roy S, Bandyopadhyay SK (2016) A new method of brain tissues segmentation from MRI with accuracy estimation. Procedia Comput Sci 85:362\u2013369","journal-title":"Procedia Comput Sci"},{"key":"394_CR22","first-page":"159","volume-title":"Image Analysis and Recognition ICIAR 2019 Lecture Notes in Computer Science","author":"S Roy","year":"2019","unstructured":"Roy S, Shoghi KI (2019) Computer-Aided Tumor Segmentation from T2-Weighted MR Images of Patient-Derived Tumor Xenografts. In: Karray F, Campilho A, Yu A (eds) Image Analysis and Recognition ICIAR 2019 Lecture Notes in Computer Science. Springer, Cham, pp 159\u2013179"},{"issue":"4","key":"394_CR23","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1007\/s11704-016-5129-y","volume":"11","author":"S Roy","year":"2017","unstructured":"Roy S, Bhattacharyya D, Bandyopadhyay SK, Kim TH (2017a) An improved brain MR image binarization method as a preprocessing for abnormality detection and features extraction. Front Comp Sci 11(4):717\u2013727","journal-title":"Front Comp Sci"},{"issue":"6","key":"394_CR24","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1080\/03772063.2017.1331757","volume":"63","author":"S Roy","year":"2017","unstructured":"Roy S, Bhattacharyya D, Bandyopadhyay SK, Kim TH (2017b) An iterative implementation of level set for precise segmentation of brain tissues and abnormality detection from MR images. IETE J Res 63(6):769\u2013783","journal-title":"IETE J Res"},{"key":"394_CR25","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1007\/s00259-021-05489-8","volume":"49","author":"S Roy","year":"2022","unstructured":"Roy S, Whitehead TD, Li S et al (2022) Co-clinical FDG-PET radiomic signature in predicting response to neoadjuvant chemotherapy in triple-negative breast cancer. Eur J Nucl Med Mol Imaging 49:550\u2013562. https:\/\/doi.org\/10.1007\/s00259-021-05489-8","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"394_CR26","doi-asserted-by":"publisher","unstructured":"Salehi SSM et al (2018) Real-time automatic fetal brain extraction in fetal MRI by deep learning.IEEE 15 th International Symposium on Biomedical Imaging (ISBI 2018), pp 720\u2013724. doi: https:\/\/doi.org\/10.1109\/ISBI.2018.8363675.","DOI":"10.1109\/ISBI.2018.8363675"},{"issue":"2","key":"394_CR27","first-page":"77","volume":"5","author":"MS Scher","year":"2003","unstructured":"Scher MS (2003) Prenatal contributions to epilepsy: lessons from the bedside. Epileptic Disord 5(2):77\u201391 (PMID: 12875951)","journal-title":"Epileptic Disord"},{"key":"394_CR28","doi-asserted-by":"publisher","first-page":"618","DOI":"10.1109\/ICCV.2017.74","volume":"2017","author":"RR Selvaraju","year":"2017","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-CAM: visual explanations from deep networks via gradient-based localization. IEEE Int Conf Comput vis (ICCV) 2017:618\u2013626. https:\/\/doi.org\/10.1109\/ICCV.2017.74","journal-title":"IEEE Int Conf Comput vis (ICCV)"},{"key":"394_CR29","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","volume":"19","author":"D Shen","year":"2017","unstructured":"Shen D, Wu G, Suk H-I (2017) Deep learning in medical image analysis. In annual review of biomedical engineering. Ann Rev 19:221\u2013248. https:\/\/doi.org\/10.1146\/annurev-bioeng-071516-044442","journal-title":"Ann Rev"},{"key":"394_CR30","doi-asserted-by":"publisher","first-page":"1408","DOI":"10.1038\/s41598-022-05468-5","volume":"12","author":"L Shen","year":"2022","unstructured":"Shen L, Zheng J, Lee EH et al (2022) Attention-guided deep learning for gestational age prediction using fetal brain MRI. Sci Rep 12:1408. https:\/\/doi.org\/10.1038\/s41598-022-05468-5","journal-title":"Sci Rep"},{"key":"394_CR31","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-020-00525-9","author":"Y Shi","year":"2020","unstructured":"Shi Y, Xue Y, Chen C, Lin K, Zhou Z (2020) Association of gestational age with MRI-based biometrics of brain development in fetuses. In BMC Medical Imaging. https:\/\/doi.org\/10.1186\/s12880-020-00525-9 (Springer Science and Business Media LLC)","journal-title":"In BMC Medical Imaging"},{"key":"394_CR4","doi-asserted-by":"publisher","unstructured":"Xu F, Ma H, Sun J,Wu R, Liu X, Kong Y (2019) LSTM Multi-modal UNet for Brain Tumor Segmentation. IEEE 4th International Conference on Image, Vision and Computing (ICIVC) pp 236-240. https:\/\/doi.org\/10.1109\/ICIVC47709.2019.8981027.","DOI":"10.1109\/ICIVC47709.2019.8981027"},{"key":"394_CR32","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume-title":"In deep learning in medical image analysis and multimodal learning for clinical decision support","author":"Z Zhou","year":"2018","unstructured":"Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, Liang J (2018) UNet++: a nested U-Net architecture for medical image segmentation. In deep learning in medical image analysis and multimodal learning for clinical decision support. Springer International Publishing, Cham, pp 3\u201311"}],"container-title":["Network Modeling Analysis in Health Informatics and Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13721-022-00394-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13721-022-00394-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13721-022-00394-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,3]],"date-time":"2022-12-03T08:00:27Z","timestamp":1670054427000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13721-022-00394-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,12]]},"references-count":32,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["394"],"URL":"https:\/\/doi.org\/10.1007\/s13721-022-00394-y","relation":{},"ISSN":["2192-6662","2192-6670"],"issn-type":[{"value":"2192-6662","type":"print"},{"value":"2192-6670","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,12]]},"assertion":[{"value":"11 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 October 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 October 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 November 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflict of interest to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"50"}}