{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T17:12:32Z","timestamp":1785949952148,"version":"3.56.0"},"reference-count":77,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,27]],"date-time":"2022-03-27T00:00:00Z","timestamp":1648339200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, the use of deep learning-based models for developing advanced healthcare systems has been growing due to the results they can achieve. However, the majority of the proposed deep learning-models largely use convolutional and pooling operations, causing a loss in valuable data and focusing on local information. In this paper, we propose a deep learning-based approach that uses global and local features which are of importance in the medical image segmentation process. In order to train the architecture, we used extracted three-dimensional (3D) blocks from the full magnetic resonance image resolution, which were sent through a set of successive convolutional neural network (CNN) layers free of pooling operations to extract local information. Later, we sent the resulting feature maps to successive layers of self-attention modules to obtain the global context, whose output was later dispatched to the decoder pipeline composed mostly of upsampling layers. The model was trained using the Mindboggle-101 dataset. The experimental results showed that the self-attention modules allow segmentation with a higher Mean Dice Score of 0.90 \u00b1 0.036 compared with other UNet-based approaches. The average segmentation time was approximately 0.038 s per brain structure. The proposed model allows tackling the brain structure segmentation task properly. Exploiting the global context that the self-attention modules incorporate allows for more precise and faster segmentation. We segmented 37 brain structures and, to the best of our knowledge, it is the largest number of structures under a 3D approach using attention mechanisms.<\/jats:p>","DOI":"10.3390\/s22072559","type":"journal-article","created":{"date-parts":[[2022,3,27]],"date-time":"2022-03-27T21:31:25Z","timestamp":1648416685000},"page":"2559","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Deep 3D Neural Network for Brain Structures Segmentation Using Self-Attention Modules in MRI Images"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8488-8753","authenticated-orcid":false,"given":"Camilo","family":"Laiton-Bonadiez","sequence":"first","affiliation":[{"name":"Facultad de Minas, Universidad Nacional de Colombia, Medell\u00edn 050041, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9069-0732","authenticated-orcid":false,"given":"German","family":"Sanchez-Torres","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad del Magdalena, Santa Marta 470001, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Branch-Bedoya","sequence":"additional","affiliation":[{"name":"Facultad de Minas, Universidad Nacional de Colombia, Medell\u00edn 050041, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.mric.2016.12.003","article-title":"Neurologic Applications of PET\/MR Imaging","volume":"25","author":"Benzinger","year":"2017","journal-title":"Magn. Reson. Imaging Clin. N. Am."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"249","DOI":"10.3389\/fnhum.2015.00249","article-title":"Advantages in functional imaging of the brain","volume":"9","author":"Mier","year":"2015","journal-title":"Front. Hum. Neurosci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1016\/j.neuroimage.2005.08.062","article-title":"Fully-automated detection of cerebral water content changes: Study of age- and gender-related H2O patterns with quantitative MRI","volume":"29","author":"Neeb","year":"2006","journal-title":"NeuroImage"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0920-9964(01)00163-3","article-title":"A review of MRI findings in schizophrenia","volume":"49","author":"Shenton","year":"2001","journal-title":"Schizophr. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1055\/s-0043-103280","article-title":"MRI Sequences in Head & Neck Radiology\u2014State of the Art","volume":"189","author":"Widmann","year":"2017","journal-title":"ROFO Fortschr. Geb. Rontgenstr. Nuklearmed."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.neucom.2020.04.157","article-title":"Convolutional neural networks for medical image analysis: State-of-the-art, comparisons, improvement and perspectives","volume":"444","author":"Yu","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"101985","DOI":"10.1016\/j.media.2021.101985","article-title":"A survey on incorporating domain knowledge into deep learning for medical image analysis","volume":"69","author":"Xie","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1016\/j.procs.2017.11.282","article-title":"Brain tumor segmentation based on a new threshold approach","volume":"120","author":"Ilhan","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Deng, W., Xiao, W., Deng, H., and Liu, J. (2010, January 16\u201318). MRI brain tumor segmentation with region growing method based on the gradients and variances along and inside of the boundary curve. Proceedings of the 2010 3rd International Conference on Biomedical Engineering and Informatics, Yantai, China.","DOI":"10.1109\/BMEI.2010.5639536"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1016\/j.neuroimage.2005.02.018","article-title":"Unified segmentation","volume":"26","author":"Ashburner","year":"2005","journal-title":"NeuroImage"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liu, J., and Guo, L. (2015, January 26\u201327). A New Brain MRI Image Segmentation Strategy Based on K-means Clustering and SVM. Proceedings of the 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics, Hangzhou, China.","DOI":"10.1109\/IHMSC.2015.182"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.ymeth.2020.09.007","article-title":"Deep learning of brain magnetic resonance images: A brief review","volume":"192","author":"Zhao","year":"2021","journal-title":"Methods"},{"key":"ref_13","first-page":"e450341","article-title":"MRI Segmentation of the Human Brain: Challenges, Methods, and Applications","volume":"2015","author":"Goossens","year":"2015","journal-title":"Comput. Math. Methods Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/S0896-6273(02)00569-X","article-title":"Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain","volume":"33","author":"Fischl","year":"2002","journal-title":"Neuron"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/S1361-8415(02)00054-3","article-title":"BrainSuite: An automated cortical surface identification tool","volume":"6","author":"Shattuck","year":"2002","journal-title":"Med. Image Anal."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1016\/j.neuroimage.2011.09.015","article-title":"FSL","volume":"62","author":"Jenkinson","year":"2012","journal-title":"NeuroImage"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yushkevich, P.A., Gao, Y., and Gerig, G. (2016, January 16\u201320). ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7591443"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Pieper, S., Halle, M., and Kikinis, R. (2004, January 15\u201318). 3D Slicer. Proceedings of the 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821), Arlington, VA, USA.","DOI":"10.1109\/ISBI.2004.1398617"},{"key":"ref_19","unstructured":"(2021, August 24). Please Help Sustain the Horos Project\u2014Horos Project. Available online: https:\/\/horosproject.org\/download-donation\/."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Qin, C., Wu, Y., Liao, W., Zeng, J., Liang, S., and Zhang, X. (2022). Improved U-Net3+ with stage residual for brain tumor segmentation. BMC Med. Imaging, 22.","DOI":"10.1186\/s12880-022-00738-0"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.neucom.2020.10.031","article-title":"Segmentation of the multimodal brain tumor image used the multi-pathway architecture method based on 3D FCN","volume":"423","author":"Sun","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Dai, W., Woo, B., Liu, S., Marques, M., Tang, F., Crozier, S., Engstrom, C., and Chandra, S. (2021, January 13\u201316). Can3d: Fast 3d Knee Mri Segmentation Via Compact Context Aggregation. Proceedings of the 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), Nice, France.","DOI":"10.1109\/ISBI48211.2021.9433784"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Deepthi Murthy, T.S., and Sadashivappa, G. (2014, January 10\u201311). Brain tumor segmentation using thresholding, morphological operations and extraction of features of tumor. Proceedings of the 2014 International Conference on Advances in Electronics Computers and Communications, Bangalore, India.","DOI":"10.1109\/ICAECC.2014.7002427"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1109\/42.650877","article-title":"Computer-aided breast cancer detection and diagnosis of masses using difference of Gaussians and derivative-based feature saliency","volume":"16","author":"Polakowski","year":"1997","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1109\/34.276131","article-title":"Edge-region-based segmentation of range images","volume":"16","author":"Wani","year":"1994","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","unstructured":"Wu, J., Ye, F., Ma, J.-L., Sun, X.-P., Xu, J., and Cui, Z.-M. (2008, January 8\u201311). The Segmentation and Visualization of Human Organs Based on Adaptive Region Growing Method. Proceedings of the 2008 IEEE 8th International Conference on Computer and Information Technology Workshops, Washington, WA, USA."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1002\/jmri.20307","article-title":"Region-growing segmentation of brain vessels: An atlas-based automatic approach","volume":"21","author":"Passat","year":"2005","journal-title":"J. Magn. Reson. Imaging JMRI"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2437","DOI":"10.1088\/0031-9155\/41\/11\/014","article-title":"Tumour volume determination from MR images by morphological segmentation","volume":"41","author":"Gibbs","year":"1996","journal-title":"Phys. Med. Biol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1118\/1.597707","article-title":"Quantitative classification of breast tumors in digitized mammograms","volume":"23","author":"Pohlman","year":"1996","journal-title":"Med. Phys."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hassanien, A.E., Tolba, M.F., and Taher Azar, A. (2014, January 28\u201330). MRI Brain Tumor Segmentation System Based on Hybrid Clustering Techniques. Proceedings of the Advanced Machine Learning Technologies and Applications, Cairo, Egypt.","DOI":"10.1007\/978-3-319-13461-1"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1266","DOI":"10.1109\/TMI.2009.2014372","article-title":"Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data","volume":"28","author":"Artaechevarria","year":"2009","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1016\/j.neuroimage.2010.09.018","article-title":"Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation","volume":"54","author":"Fonov","year":"2011","journal-title":"NeuroImage"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1109\/TPAMI.2012.143","article-title":"Multi-Atlas Segmentation with Joint Label Fusion","volume":"35","author":"Wang","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1016\/j.media.2013.10.013","article-title":"A generative probability model of joint label fusion for multi-atlas based brain segmentation","volume":"18","author":"Wu","year":"2014","journal-title":"Med. Image Anal."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/BF00133570","article-title":"Snakes: Active contour models","volume":"1","author":"Kass","year":"1988","journal-title":"Int. J. Comput. Vis."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.media.2017.11.001","article-title":"Robust brain ROI segmentation by deformation regression and deformable shape model","volume":"43","author":"Wu","year":"2018","journal-title":"Med. Image Anal."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.proeng.2012.01.868","article-title":"Fuzzy Clustering and Deformable Model for Tumor Segmentation on MRI Brain Image: A Combined Approach","volume":"30","author":"Rajendran","year":"2012","journal-title":"Procedia Eng."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bloch, I., Petrosino, A., and Tettamanzi, A.G.B. (2005, January 15\u201317). 3D Brain Tumor Segmentation Using Fuzzy Classification and Deformable Models. Proceedings of the Fuzzy Logic and Applications, Crema, Italy.","DOI":"10.1007\/11676935"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.nic.2020.06.003","article-title":"Overview of Machine Learning: Part 2: Deep Learning for Medical Image Analysis","volume":"30","author":"Le","year":"2020","journal-title":"Neuroimaging Clin. N. Am."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"107187","DOI":"10.1016\/j.knosys.2021.107187","article-title":"Deep learning in ECG diagnosis: A review","volume":"227","author":"Liu","year":"2021","journal-title":"Knowl. Based Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"102020","DOI":"10.1016\/j.artmed.2021.102020","article-title":"A survey of deep learning models in medical therapeutic areas","volume":"112","author":"Nogales","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","article-title":"Recent advances in convolutional neural networks","volume":"77","author":"Gu","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. Big Data"},{"key":"ref_44","unstructured":"Bank, D., Koenigstein, N., and Giryes, R. (2020). Autoencoders. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Mont\u00fafar, G. (2018). Restricted Boltzmann Machines: Introduction and Review. arXiv.","DOI":"10.1007\/978-3-319-97798-0_4"},{"key":"ref_46","unstructured":"Sherstinsky, A. (2018). Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation Applied to Handwritten Zip Code Recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Comput."},{"key":"ref_48","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention Is All You Need. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF00344251","article-title":"Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position","volume":"36","author":"Fukushima","year":"1980","journal-title":"Biol. Cybern."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Nie, D., Wang, L., Gao, Y., and Shen, D. (2016, January 13\u201316). Fully convolutional networks for multi-modality isointense infant brain image segmentation. Proceedings of the 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, Czech Republic.","DOI":"10.1109\/ISBI.2016.7493515"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1080\/21681163.2016.1182072","article-title":"Multi-scale structured CNN with label consistency for brain MR image segmentation","volume":"6","author":"Bao","year":"2018","journal-title":"Comput. Methods Biomech. Biomed. Eng. Imaging Vis."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"117012","DOI":"10.1016\/j.neuroimage.2020.117012","article-title":"FastSurfer\u2014A fast and accurate deep learning based neuroimaging pipeline","volume":"219","author":"Henschel","year":"2020","journal-title":"NeuroImage"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1109\/TMI.2016.2528821","article-title":"Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation","volume":"35","author":"Brosch","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.neuroimage.2017.04.034","article-title":"Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach","volume":"155","author":"Valverde","year":"2017","journal-title":"NeuroImage"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1177\/1352458519856843","article-title":"Brain and lesion segmentation in multiple sclerosis using fully convolutional neural networks: A large-scale study","volume":"26","author":"Gabr","year":"2020","journal-title":"Mult. Scler. Houndmills Basingstoke Engl."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","article-title":"Brain tumor segmentation with Deep Neural Networks","volume":"35","author":"Havaei","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Crimi, A., Menze, B., Maier, O., Reyes, M., and Handels, H. (2015, January 5). A Convolutional Neural Network Approach to Brain Tumor Segmentation. Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Munich, Germany.","DOI":"10.1007\/978-3-319-30858-6"},{"key":"ref_58","unstructured":"Crimi, A., Bakas, S., Kuijf, H., Menze, B., and Reyes, M. (2018). Pooling-Free Fully Convolutional Networks with Dense Skip Connections for Semantic Segmentation, with Application to Brain Tumor Segmentation. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Springer International Publishing."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1016\/j.nicl.2017.06.016","article-title":"Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks","volume":"15","author":"Chen","year":"2017","journal-title":"NeuroImage Clin."},{"key":"ref_60","unstructured":"Akkus, Z., Ali, I., Sedlar, J., Kline, T.L., Agrawal, J.P., Parney, I.F., Giannini, C., and Erickson, B.J. (2016). Predicting 1p19q Chromosomal Deletion of Low-Grade Gliomas from MR Images using Deep Learning. arXiv."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Navab, N., Hornegger, J., Wells, W.M., and Frangi, A.F. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015, Munich, Germany.","DOI":"10.1007\/978-3-319-24571-3"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Kumar, P., Nagar, P., Arora, C., and Gupta, A. (2018, January 7\u201310). U-Segnet: Fully Convolutional Neural Network Based Automated Brain Tissue Segmentation Tool. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451295"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","article-title":"UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation","volume":"39","author":"Zhou","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","article-title":"MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation","volume":"121","author":"Ibtehaz","year":"2020","journal-title":"Neural Netw."},{"key":"ref_65","first-page":"1295","article-title":"Capsule Networks\u2014A survey","volume":"34","author":"Edward","year":"2019","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_66","unstructured":"Salehinejad, H., Baarbe, J., Sankar, S., Barfett, J., Colak, E., and Valaee, S. (2018). Recent Advances in Recurrent Neural Networks. arXiv."},{"key":"ref_67","unstructured":"Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding. arXiv."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., and Torr, P.H.S. (2021). Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers. arXiv.","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"ref_69","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., and Zhou, Y. (2021). TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation. arXiv."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Valanarasu, J.M.J., Oza, P., Hacihaliloglu, I., and Patel, V.M. (2021). Medical Transformer: Gated Axial-Attention for Medical Image Segmentation. arXiv.","DOI":"10.1007\/978-3-030-87193-2_4"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"171","DOI":"10.3389\/fnins.2012.00171","article-title":"101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol","volume":"6","author":"Klein","year":"2012","journal-title":"Front. Neurosci."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Sugino, T., Kawase, T., Onogi, S., Kin, T., Saito, N., and Nakajima, Y. (2021). Loss Weightings for Improving Imbalanced Brain Structure Segmentation Using Fully Convolutional Networks. Healthcare, 9.","DOI":"10.3390\/healthcare9080938"},{"key":"ref_73","unstructured":"Cardoso, M.J., Arbel, T., Carneiro, G., Syeda-Mahmood, T., Tavares, J.M.R.S., Moradi, M., Bradley, A., Greenspan, H., Papa, J.P., and Madabhushi, A. (2017). Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer International Publishing."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal Loss for Dense Object Detection. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_75","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.neuroimage.2017.02.035","article-title":"DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy","volume":"170","author":"Wachinger","year":"2018","journal-title":"NeuroImage"},{"key":"ref_77","unstructured":"Roy, A.G., Conjeti, S., Navab, N., and Wachinger, C. (2018). QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy. arXiv."}],"updated-by":[{"DOI":"10.3390\/s26031030","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2022,3,27]],"date-time":"2022-03-27T00:00:00Z","timestamp":1648339200000}}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/7\/2559\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T10:35:50Z","timestamp":1770287750000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/7\/2559"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,27]]},"references-count":77,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["s22072559"],"URL":"https:\/\/doi.org\/10.3390\/s22072559","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,27]]}}}