{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T16:58:10Z","timestamp":1726851490897},"reference-count":35,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,6]],"date-time":"2023-01-06T00:00:00Z","timestamp":1672963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shenzhen Overseas High-level Talent Innovation and Entrepreneurship Special Fund","award":["KQTD20180413181834876"]},{"name":"Shenzhen Science and Technology Program","award":["KCXFZ20211020163408012"]},{"DOI":"10.13039\/501100012571","name":"Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province","doi-asserted-by":"publisher","award":["2020B1212060051"],"id":[{"id":"10.13039\/501100012571","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Fetal brain tissue segmentation is essential for quantifying the presence of congenital disorders in the developing fetus. Manual segmentation of fetal brain tissue is cumbersome and time-consuming, so using an automatic segmentation method can greatly simplify the process. In addition, the fetal brain undergoes a variety of changes throughout pregnancy, such as increased brain volume, neuronal migration, and synaptogenesis. In this case, the contrast between tissues, especially between gray matter and white matter, constantly changes throughout pregnancy, increasing the complexity and difficulty of our segmentation. To reduce the burden of manual refinement of segmentation, we proposed a new deep learning-based segmentation method. Our approach utilized a novel attentional structural block, the contextual transformer block (CoT-Block), which was applied in the backbone network model of the encoder\u2013decoder to guide the learning of dynamic attentional matrices and enhance image feature extraction. Additionally, in the last layer of the decoder, we introduced a hybrid dilated convolution module, which can expand the receptive field and retain detailed spatial information, effectively extracting the global contextual information in fetal brain MRI. We quantitatively evaluated our method according to several performance measures: dice, precision, sensitivity, and specificity. In 80 fetal brain MRI scans with gestational ages ranging from 20 to 35 weeks, we obtained an average Dice similarity coefficient (DSC) of 83.79%, an average Volume Similarity (VS) of 84.84%, and an average Hausdorff95 Distance (HD95) of 35.66 mm. We also used several advanced deep learning segmentation models for comparison under equivalent conditions, and the results showed that our method was superior to other methods and exhibited an excellent segmentation performance.<\/jats:p>","DOI":"10.3390\/s23020655","type":"journal-article","created":{"date-parts":[[2023,1,6]],"date-time":"2023-01-06T08:54:49Z","timestamp":1672995289000},"page":"655","source":"Crossref","is-referenced-by-count":11,"title":["Deep Learning-Based Multiclass Brain Tissue Segmentation in Fetal MRIs"],"prefix":"10.3390","volume":"23","author":[{"given":"Xiaona","family":"Huang","sequence":"first","affiliation":[{"name":"Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"},{"name":"Department of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Shenzhen Maternity and Child Healthcare Hospital, Shenzhen 518027, China"}]},{"given":"Yuhan","family":"Li","sequence":"additional","affiliation":[{"name":"Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"},{"name":"Department of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Keying","family":"Qi","sequence":"additional","affiliation":[{"name":"Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"},{"name":"Department of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Ang","family":"Gao","sequence":"additional","affiliation":[{"name":"Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"}]},{"given":"Bowen","family":"Zheng","sequence":"additional","affiliation":[{"name":"Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"}]},{"given":"Dong","family":"Liang","sequence":"additional","affiliation":[{"name":"Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"}]},{"given":"Xiaojing","family":"Long","sequence":"additional","affiliation":[{"name":"Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1819","DOI":"10.1016\/j.neuroimage.2012.01.128","article-title":"Multi-Atlas Multi-Shape Segmentation of Fetal Brain MRI for Volumetric and Morphometric Analysis of Ventriculomegaly","volume":"60","author":"Gholipour","year":"2012","journal-title":"NeuroImage"},{"key":"ref_2","first-page":"161","article-title":"Longitudinal Analysis of Fetal MRI in Patients with Prenatal Spina Bifida Repair","volume":"Volume 11798","author":"Payette","year":"2019","journal-title":"Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/978-3-030-60334-2_29","article-title":"Efficient Multi-Class Fetal Brain Segmentation in High Resolution MRI Reconstructions with Noisy Labels","volume":"Volume 12437","author":"Payette","year":"2020","journal-title":"Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1016\/j.jtcvs.2004.07.022","article-title":"Preoperative Cerebral Blood Flow Is Diminished in Neonates with Severe Congenital Heart Defects","volume":"128","author":"Licht","year":"2004","journal-title":"J. Thorac. Cardiovasc. Surg."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1002\/pd.2632","article-title":"Posterior Brain in Fetuses with Open Spina Bifida at 11 to 13 Weeks: OPEN SPINA BIFIDA AT 11 TO 13 WEEKS","volume":"31","author":"Lachmann","year":"2011","journal-title":"Prenat. Diagn."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1002\/uog.3865","article-title":"Prenatal Diagnosis of Open and Closed Spina Bifida","volume":"28","author":"Ghi","year":"2006","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1348","DOI":"10.1002\/hbm.20935","article-title":"Atlas-Based Segmentation of Developing Tissues in the Human Brain with Quantitative Validation in Young Fetuses","volume":"31","author":"Habas","year":"2010","journal-title":"Hum. Brain Mapp."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1016\/j.neuroimage.2010.06.054","article-title":"A Spatiotemporal Atlas of MR Intensity, Tissue Probability and Shape of the Fetal Brain with Application to Segmentation","volume":"53","author":"Habas","year":"2010","journal-title":"NeuroImage"},{"key":"ref_9","first-page":"1","article-title":"A Multi-Channel 4D Probabilistic Atlas of the Developing Brain: Application to Fetuses and Neonates","volume":"2012","author":"Serag","year":"2012","journal-title":"Ann. BMVA"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.neuroimage.2014.01.034","article-title":"Automatic Quantification of Normal Cortical Folding Patterns from Fetal Brain MRI","volume":"91","author":"Wright","year":"2014","journal-title":"NeuroImage"},{"key":"ref_11","unstructured":"Ledig, C., Wright, R., Serag, A., and Aljabar, P. (2012, January 1\u20135). Neonatal Brain Segmentation Using Second Order Neighborhood Information. Proceedings of the Workshop on Perinatal and Paediatric Imaging: PaPI, Medical Image Computing and Computer-Assisted Intervention: MICCAI, Nice, France."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, S., Sui, X., Luo, X., Xu, X., Liu, Y., and Goh, R. (2021). Medical Image Segmentation Using Squeeze-and-Expansion Transformers. arXiv.","DOI":"10.24963\/ijcai.2021\/112"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","article-title":"CE-Net: Context Encoder Network for 2D Medical Image Segmentation","volume":"38","author":"Gu","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1109\/TMI.2020.3046579","article-title":"A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI","volume":"40","author":"Dou","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_15","unstructured":"Lei, Z., Qi, L., Wei, Y., and Zhou, Y. (2019). Infant Brain MRI Segmentation with Dilated Convolution Pyramid Downsampling and Self-Attention. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.mri.2019.05.020","article-title":"Automatic Brain Tissue Segmentation in Fetal MRI Using Convolutional Neural Networks","volume":"64","author":"Khalili","year":"2019","journal-title":"Magn. Reson. Imaging"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1038\/s42256-019-0058-8","article-title":"Developing Brain Atlas through Deep Learning","volume":"1","author":"Iqbal","year":"2019","journal-title":"Nat. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1038\/s41597-021-00946-3","article-title":"An Automatic Multi-Tissue Human Fetal Brain Segmentation Benchmark Using the Fetal Tissue Annotation Dataset","volume":"8","author":"Payette","year":"2021","journal-title":"Sci. Data"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1016\/j.neuroimage.2015.06.018","article-title":"An Efficient Total Variation Algorithm for Super-Resolution in Fetal Brain MRI with Adaptive Regularization","volume":"118","author":"Tourbier","year":"2015","journal-title":"NeuroImage"},{"key":"ref_20","unstructured":"Gholipour, A., and Warfield, S.K. (2009, January 24). Super-Resolution Reconstruction of Fetal Brain MRI. Proceedings of the MICCAI Workshop on Image Analysis for the Developing Brain (IADB\u2019 2009), London, UK."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1550","DOI":"10.1016\/j.media.2012.07.004","article-title":"Reconstruction of Fetal Brain MRI with Intensity Matching and Complete Outlier Removal","volume":"16","author":"Quaghebeur","year":"2012","journal-title":"Med. Image Anal."},{"key":"ref_22","unstructured":"Yu, F., and Koltun, V. (2015). Multi-Scale Context Aggregation by Dilated Convolutions. arXiv."},{"key":"ref_23","unstructured":"Li, Y., Yao, T., Pan, Y., and Mei, T. (2022). Contextual Transformer Networks for Visual Recognition. IEEE Trans. Pattern Anal. Mach. Intell., 1\u201311."},{"key":"ref_24","first-page":"8026","article-title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","volume":"32","author":"Paszke","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_25","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., and Ronneberger, O. (2016). 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. arXiv.","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.-A. (2016, January 25\u201328). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chen, C., Liu, X., Ding, M., Zheng, J., and Li, J. (2019, January 13\u201317). 3D Dilated Multi-Fiber Network for Real-Time Brain Tumor Segmentation in MRI. Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2019, Shenzhen, China.","DOI":"10.1007\/978-3-030-32248-9_21"},{"key":"ref_29","first-page":"234","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"Volume 9351","author":"Ronneberger","year":"2015","journal-title":"Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015"},{"key":"ref_30","first-page":"011005","article-title":"Evaluation of Deep Learning Methods for Parotid Gland Segmentation from CT Images","volume":"6","author":"Schwier","year":"2018","journal-title":"J. Med. Imaging"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.compbiomed.2018.08.018","article-title":"Robust Liver Vessel Extraction Using 3D U-Net with Variant Dice Loss Function","volume":"101","author":"Huang","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3303","DOI":"10.1109\/TIP.2016.2567072","article-title":"Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation","volume":"25","author":"An","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_33","unstructured":"Chen, Y., Kalantidis, Y., Li, J., Yan, S., and Feng, J. (2018, January 3\u20138). Multi-Fiber Networks for Video Recognition. Proceedings of the 2018 Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.ejrad.2005.11.020","article-title":"MRI of Normal Fetal Brain Development","volume":"57","author":"Prayer","year":"2006","journal-title":"Eur. J. Radiol."},{"key":"ref_35","first-page":"382","article-title":"Volumetric Analysis of the Germinal Matrix and Lateral Ventricles Performed Using MR Images of Postmortem Fetuses","volume":"22","author":"Kinoshita","year":"2001","journal-title":"Am. J. Neuroradiol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/655\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T15:58:38Z","timestamp":1724515118000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/655"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,6]]},"references-count":35,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23020655"],"URL":"https:\/\/doi.org\/10.3390\/s23020655","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,1,6]]}}}