{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T16:12:16Z","timestamp":1759335136849,"version":"build-2065373602"},"reference-count":73,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T00:00:00Z","timestamp":1759104000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009042","name":"University of Seville","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100009042","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Sensors"],"abstract":"<jats:p>Spinocerebellar ataxia type 2 (SCA2) is a neurodegenerative disorder marked by progressive brainstem and cerebellar atrophy, leading to gait ataxia. Quantifying this atrophy in magnetic resonance imaging (MRI) is critical for tracking disease progression in both symptomatic patients and preclinical subjects. However, manual segmentation of brainstem subregions (mesencephalon, pons, and medulla) is time-consuming and prone to human error. This work presents an automated deep learning framework to assess brainstem atrophy in SCA2. Using T1-weighted MRI scans from patients, preclinical carriers, and healthy controls, a U-shaped convolutional neural network (CNN) was trained to segment brainstem subregions and quantify volume loss. The model achieved strong agreement with manual segmentations, significantly outperforming four U-Net-based benchmarks (mean Dice scores: whole brainstem 0.96 vs. 0.93\u20130.95, pons 0.96 vs. 0.91\u20130.94, mesencephalon 0.96 vs. 0.89\u20130.93, and medulla 0.95 vs. 0.91\u20130.93). Results revealed severe atrophy in preclinical and symptomatic cohorts, with pons volumes reduced by nearly 50% compared to controls (p &lt; 0.001). The mesencephalon and medulla showed milder degeneration, underscoring regional vulnerability differences. This automated approach enables rapid, precise assessment of brainstem atrophy, advancing early diagnosis and monitoring in SCA2.<\/jats:p>","DOI":"10.3390\/s25196009","type":"journal-article","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T08:21:51Z","timestamp":1759220511000},"page":"6009","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning-Based Assessment of Brainstem Volume Changes in Spinocerebellar Ataxia Type 2 (SCA2): A Study on Patients and Preclinical Subjects"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4719-8264","authenticated-orcid":false,"given":"Robin","family":"Cabeza-Ruiz","sequence":"first","affiliation":[{"name":"Center for CAD\/CAM Studies, University of Holgu\u00edn, Holgu\u00edn 80100, Cuba"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1628-2703","authenticated-orcid":false,"given":"Luis","family":"Vel\u00e1zquez-P\u00e9rez","sequence":"additional","affiliation":[{"name":"Cuban Academy of Sciences, Havana 10200, Cuba"},{"name":"Centre for the Research and Rehabilitation of Hereditary Ataxias, Holgu\u00edn 80100, Cuba"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4569-2103","authenticated-orcid":false,"given":"Evelio","family":"Gonz\u00e1lez-Dalmau","sequence":"additional","affiliation":[{"name":"Cuban Neuroscience Center, Havana 10200, Cuba"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6056-740X","authenticated-orcid":false,"given":"Alejandro","family":"Linares-Barranco","sequence":"additional","affiliation":[{"name":"Robotics and Technology of Computers Lab, University of Seville, 41012 Seville, Spain"},{"name":"Escuela Polit\u00e9cnica Superior (EPS), University of Seville, 41011 Seville, Spain"},{"name":"Smart Computer Systems Research and Engineering Lab (SCORE), Research Institute of Computer Engineering (I3US), University of Seville, 41012 Seville, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5741-5168","authenticated-orcid":false,"given":"Roberto","family":"P\u00e9rez-Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Center for CAD\/CAM Studies, University of Holgu\u00edn, Holgu\u00edn 80100, Cuba"},{"name":"Cuban Academy of Sciences, Havana 10200, Cuba"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00401-012-1000-x","article-title":"Brain pathology of spinocerebellar ataxias","volume":"124","author":"Seidel","year":"2012","journal-title":"Acta Neuropathol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e89410","DOI":"10.1371\/journal.pone.0089410","article-title":"Progression of Brain Atrophy in Spinocerebellar Ataxia Type 2: A Longitudinal Tensor-Based Morphometry Study","volume":"9","author":"Mascalchi","year":"2014","journal-title":"PLoS ONE"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1111\/jon.12534","article-title":"Structural Complexity of the Cerebellum and Cerebral Cortex is Reduced in Spinocerebellar Ataxia Type 2","volume":"28","author":"Marzi","year":"2018","journal-title":"J. Neuroimaging"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1002\/acn3.437","article-title":"The Multiple Faces of Spinocerebellar Ataxia type 2","volume":"4","author":"Antenora","year":"2017","journal-title":"Ann. Clin. Transl. Neurol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1007\/s12311-020-01107-9","article-title":"Hereditary Ataxias in Cuba: A Nationwide Epidemiological and Clinical Study in 1001 Patients","volume":"19","author":"Rodes","year":"2020","journal-title":"Cerebellum"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"105985","DOI":"10.1016\/j.parkreldis.2023.105985","article-title":"Spinocerebellar ataxia type 2 has multiple ancestral origins","volume":"120","author":"Sena","year":"2024","journal-title":"Park. Relat. Disord."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1007\/s10072-008-1005-3","article-title":"Spinocerebellar ataxias","volume":"29","author":"Mascalchi","year":"2008","journal-title":"Neurol. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"34","DOI":"10.62347\/SOGE3962","article-title":"The role of nuclear medicine in neurodegenerative diseases: A narrative review","volume":"14","author":"Riahi","year":"2025","journal-title":"Am. J. Neurodegener. Dis."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/bs.irn.2018.09.011","article-title":"Neuroimaging Applications in Chronic Ataxias","volume":"143","author":"Mascalchi","year":"2018","journal-title":"Int. Rev. Neurobiol."},{"key":"ref_10","first-page":"931","article-title":"MR Imaging in Ataxias: Consensus Recommendations by the Ataxia Global Initiative Working Group on MRI Biomarkers","volume":"23","author":"Cocozza","year":"2024","journal-title":"Cerebellum"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1007\/s12311-025-01856-5","article-title":"Biomarkers in Spinocerebellar Ataxias","volume":"24","author":"Klockgether","year":"2025","journal-title":"Cerebellum"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1007\/s00234-023-03260-4","article-title":"Progressive white matter degeneration in patients with spinocerebellar ataxia type 2","volume":"66","author":"Gao","year":"2024","journal-title":"Neuroradiology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.crad.2023.10.020","article-title":"White matter integrity assessment in spinocerebellar ataxia type 2 (SCA2) patients","volume":"79","author":"Hannoun","year":"2024","journal-title":"Clin. Radiol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1318","DOI":"10.4103\/0028-3886.329596","article-title":"Cortical and Subcortical Brain Area Atrophy in SCA1 and SCA2 Patients in India: The Structural MRI Underpinnings and Correlative Insight Among the Atrophy and Disease Attributes","volume":"69","author":"Tamuli","year":"2021","journal-title":"Neurol. India"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1007\/s12013-011-9226-0","article-title":"Clinical Manifestation, Imaging, and Genotype Analysis of Two Pedigrees with Spinocerebellar Ataxia","volume":"61","author":"Liu","year":"2011","journal-title":"Cell Biochem. Biophys."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s12311-011-0292-z","article-title":"Spinocerebellar Ataxia Types 1, 2, 3 and 6: The Clinical Spectrum of Ataxia and Morphometric Brainstem and Cerebellar Findings","volume":"11","author":"Jacobi","year":"2012","journal-title":"Cerebellum"},{"key":"ref_17","first-page":"207","article-title":"A De Novo Missense Mutation in the Inositol 1, 4, 5-Triphosphate Receptor Type 1 Gene Causing Severe Pontine and Cerebellar Hypoplasia: Expanding the Phenotype of ITPR1 -Related Spinocerebellar Ataxia \u2019s","volume":"173","author":"Barth","year":"2016","journal-title":"Am. J. Med. Genet. Part A"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2714","DOI":"10.1167\/iovs.15-18732","article-title":"MRI evidence of cerebellar and extraocular muscle atrophy differently contributing to eye movement abnormalities in SCA2 and SCA28 diseases","volume":"57","author":"Politi","year":"2016","journal-title":"Investig. Opthalmology Vis. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mascalchi, M., and Vella, A. (2020). Neuroimaging biomarkers in SCA2 gene carriers. Int. J. Mol. Sci., 21.","DOI":"10.3390\/ijms21031020"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e0135449","DOI":"10.1371\/journal.pone.0135449","article-title":"Extensive white matter alterations and its correlations with ataxia severity in SCA 2 patients","volume":"10","author":"Galvez","year":"2015","journal-title":"PLoS ONE"},{"key":"ref_21","first-page":"79","article-title":"Handwritten Digit Classification Using Deep Learning Convolutional Neural Network","volume":"5","author":"Khorsheed","year":"2024","journal-title":"J. Soft Comput. Data Min."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zangana, H.M., and Mustafa, F.M. (2024). Review of Hybrid Denoising Approaches in Face Recognition: Bridging Wavelet Transform and Deep Learning. Indones. J. Comput. Sci., 13.","DOI":"10.33022\/ijcs.v13i4.4209"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3691","DOI":"10.1007\/s00607-024-01334-6","article-title":"Automatic video captioning using tree hierarchical deep convolutional neural network and ASRNN-bi-directional LSTM","volume":"106","author":"Kavitha","year":"2024","journal-title":"Computing"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, H.Y., Lin, C.H., Lai, J.W., and Chan, Y.K. (2023). Convolutional neural network-based automated system for dog tracking and emotion recognition in video surveillance. Appl. Sci., 13.","DOI":"10.3390\/app13074596"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Stoean, C., Stoean, R., Atencia, M., Abdar, M., Vel\u00e1zquez-P\u00e9rez, L., Khosrabi, A., Nahavandi, S., Acharya, U.R., and Joya, G. (2020). Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals. Sensors, 20.","DOI":"10.3390\/s20113032"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4741","DOI":"10.1109\/JBHI.2025.3541453","article-title":"Optimal Electroencephalogram and Electrooculogram Signal Combination for Deep Learning-Based Sleep Staging","volume":"29","author":"Tashakori","year":"2025","journal-title":"IEEE J. Biomed. Health Informatics"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"23","DOI":"10.24018\/ejece.2023.7.1.483","article-title":"Prediction of brain stroke using machine learning algorithms and deep neural network techniques","volume":"7","author":"Rahman","year":"2023","journal-title":"Eur. J. Electr. Eng. Comput. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kanna, R.K., Sahoo, S.K., Madhavi, B.K., Mohan, V., Babu, G.S., and Panigrahi, B.S. (2024). Detection of Brain Tumour based on Optimal Convolution Neural Network. EAI Endorsed Trans. Pervasive Health Technol., 10.","DOI":"10.4108\/eetpht.10.5464"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"615","DOI":"10.12694\/scpe.v25i1.2097","article-title":"Convolution neural networks for disease prediction: Applications and challenges","volume":"25","author":"Mushtaq","year":"2024","journal-title":"Scalable Comput. Pract. Exp."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1109\/TNSRE.2021.3051093","article-title":"Deep Learning for Accelerometric Data Assessment and Ataxic Gait Monitoring","volume":"29","author":"Cejnar","year":"2021","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Trabassi, D., Castiglia, S.F., Bini, F., Marinozzi, F., Ajoudani, A., Lorenzini, M., Chini, G., Varrecchia, T., Ranavolo, A., and De Icco, R. (2024). Optimizing Rare Disease Gait Classification through Data Balancing and Generative AI: Insights from Hereditary Cerebellar Ataxia. Sensors, 24.","DOI":"10.3390\/s24113613"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"45","DOI":"10.2478\/acss-2024-0006","article-title":"Determination of Ataxia with EfficientNet Models in Person with Early MS using Plantar Pressure Distribution Signals","volume":"29","author":"Tuncer","year":"2024","journal-title":"Appl. Comput. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lai, M.-H., Liu, D.-G., Cheng, C.-H., and Lai, C.-Y. (2024, January 19\u201321). Quantitative Assessment of Gait in Spinocerebellar Ataxia Using Deep Learning. Proceedings of the 2024 IEEE 4th International Conference on Electronic Communications, Internet of Things and Big Data (ICEIB), Taipei, Taiwan.","DOI":"10.1109\/ICEIB61477.2024.10602581"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lee, S., Park, C., Ha, E., Hong, J., Kim, S.H., and Kim, Y. (2025). Determination of Spatiotemporal Gait Parameters Using a Smartphone\u2019s IMU in the Pocket: Threshold-Based and Deep Learning Approaches. Sensors, 25.","DOI":"10.3390\/s25144395"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"62328","DOI":"10.1109\/ACCESS.2024.3393243","article-title":"Sensitive Quantification of Cerebellar Speech Abnormalities Using Deep Learning Models","volume":"12","author":"Vattis","year":"2024","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1007\/s00415-023-12091-5","article-title":"Differentiation of speech in Parkinson\u2019s disease and spinocerebellar degeneration using deep neural networks","volume":"21","author":"Eguchi","year":"2024","journal-title":"J. Neurol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Saifullah, S., and Dre\u017cewski, R. (2024). Automatic brain tumor segmentation using convolutional neural networks: U-net framework with pso-tuned hyperparameters. Parallel Problem Solving from Nature\u2014PPSN XVIII, Springer.","DOI":"10.1007\/978-3-031-70071-2_21"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"116819","DOI":"10.1016\/j.neuroimage.2020.116819","article-title":"Automatic Cerebellum Anatomical Parcellation using U-Net with Locally Constrained Optimization","volume":"218","author":"Han","year":"2020","journal-title":"Neuroimage"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"121063","DOI":"10.1016\/j.neuroimage.2025.121063","article-title":"DeepCERES: A deep learning method for cerebellar lobule segmentation using ultra-high resolution multimodal MRI","volume":"308","author":"Gadea","year":"2025","journal-title":"Neuroimage"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"70063","DOI":"10.1002\/hbm.70063","article-title":"OpenMAP-T1: A Rapid Deep-Learning Approach to Parcellate 280 Anatomical Regions to Cover the Whole Brain","volume":"45","author":"Nishimaki","year":"2024","journal-title":"Hum. Brain Mapp."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Mahendran, R.K., Aishwarya, R., Abinayapriya, R., and Kumar, P. (2024, January 5\u20137). Deep Transfer Learning Based Diagnosis of Multiple Neurodegenerative Disorders. Proceedings of the 2024 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India.","DOI":"10.1109\/ESCI59607.2024.10497320"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"117026","DOI":"10.1016\/j.neuroimage.2020.117026","article-title":"AssemblyNet: A large ensemble of CNNs for 3D whole brain MRI segmentation","volume":"219","author":"Mansencal","year":"2020","journal-title":"Neuroimage"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"102365","DOI":"10.1016\/j.artmed.2022.102365","article-title":"Deep Learning with Multiresolution Handcrafted Features for Brain MRI Segmentation","volume":"131","author":"Mecheter","year":"2022","journal-title":"Artif. Intell. Med."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"119703","DOI":"10.1016\/j.neuroimage.2022.119703","article-title":"CerebNet: A fast and reliable deep-learning pipeline for detailed cerebellum sub-segmentation","volume":"264","author":"Faber","year":"2022","journal-title":"Neuroimage"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"135020","DOI":"10.1109\/ACCESS.2023.3337363","article-title":"Brain tumor detection using 3D-UNet segmentation features and hybrid machine learning model","volume":"11","author":"Mallampati","year":"2023","journal-title":"IEEE Access"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1186\/s12911-023-02174-8","article-title":"An early detection and segmentation of Brain Tumor using Deep Neural Network","volume":"23","author":"Aggarwal","year":"2023","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"72518","DOI":"10.1109\/ACCESS.2023.3289224","article-title":"Brain tumor detection and multi-grade segmentation through hybrid caps-VGGNet model","volume":"11","author":"Jabbar","year":"2023","journal-title":"IEEE Access"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Sille, R., Choudhury, T., Sharma, A., Chauhan, P., Tomar, R., and Sharma, D. (2023). A novel generative adversarial network-based approach for automated brain tumour segmentation. Medicina, 59.","DOI":"10.3390\/medicina59010119"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Cao, Y., Zhang, Q., Li, J., Wang, Y., Liu, D., and Yu, H. (2022, January 21\u201323). An automated segmentation model based on CBAM for MR image glioma tumors. Proceedings of the 2022 2nd International Conference on Bioinformatics and Intelligent Computing, Harbin, China.","DOI":"10.1145\/3523286.3524575"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Shyamala, N., and Mahaboobbasha, S. (2024, January 18\u201320). Convolutional Block Attention Module-based Deep Learning Model for MRI Brain Tumor Identification (ResNet-CBAM). Proceedings of the 2024 5th International Conference on Smart Electronics and Communications (ICOSEC), Trichy, India.","DOI":"10.1109\/ICOSEC61587.2024.10722246"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Cui, Y. (2024, January 20\u201322). EUnet++: Enhanced UNet++ Architecture incorporating 3DCNN and CBAM Modules for Brain Tumor Image Segmentation. Proceedings of the 2024 International Conference on Information Technology, Communication Ecosystem and Management (ITCEM), Bangkok, Thailand.","DOI":"10.1109\/ITCEM65710.2024.00015"},{"key":"ref_52","first-page":"66","article-title":"MRI Segmentation Using Inception-based U-Net Architecture and Up Skip Connections","volume":"21","author":"Hoseini","year":"2024","journal-title":"Karafan J."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"9969","DOI":"10.1007\/s00521-025-11105-9","article-title":"3D Air-UNet: Attention-inception-residual-based U-Net for brain tumor segmentation from multimodal MRI","volume":"37","author":"Sharma","year":"2025","journal-title":"Neural Comput. Appl."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Cabeza-Ruiz, R., Vel\u00e1zquez-P\u00e9rez, L., Linares-Barranco, A., and P\u00e9rez-Rodr\u00edguez, R. (2022). Convolutional Neural Networks for Segmenting Cerebellar Fissures from Magnetic Resonance Imaging. Sensors, 22.","DOI":"10.3390\/s22041345"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1080\/1448837X.2024.2309427","article-title":"Improved brain tumor segmentation using modified U-Net model with inception and attention modules on multimodal MRI images","volume":"21","author":"Hechri","year":"2024","journal-title":"Aust. J. Electr. Electron. Eng."},{"key":"ref_56","unstructured":"Crimi, A., and Bakas, S. (2021). Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. International MICCAI Brainlesion Workshop, Springer."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Laiton-Bonadiez, C., Sanchez-Torres, G., and Branch-Bedoya, J. (2022). Deep 3D Neural Network for Brain Structures Segmentation Using Self-Attention Modules in MRI Images. Sensors, 22.","DOI":"10.3390\/s22072559"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Cahall, D.E., Rasool, G., Bouaynaya, N.C., and Fathallah-Shaykh, H.M. (2019). Inception Modules Enhance Brain Tumor Segmentation. Front. Comput. Neurosci., 13.","DOI":"10.3389\/fncom.2019.00044"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.1109\/TMI.2010.2046908","article-title":"N4ITK: Improved N3 Bias Correction","volume":"29","author":"Tustison","year":"2010","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"S102","DOI":"10.1016\/S1053-8119(09)70884-5","article-title":"Unbiased nonlinear average age-appropriate brain templates from birth to adulthood","volume":"47","author":"Fonov","year":"2009","journal-title":"Neuroimage"},{"key":"ref_62","first-page":"1","article-title":"Advanced Normalization Tools (ANTS)","volume":"2","author":"Avants","year":"2009","journal-title":"Insight J."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Nishio, M., Wang, X., Cornblath, E.J., Lee, S.-H., Shih, Y.-Y.I., Palomero-Gallagher, N., Arcaro, M.J., Lydon-Staley, D.M., and Mackey, A.P. (2025). Alcohol impacts an fMRI marker of neural inhibition in humans and rodents. bioRxiv.","DOI":"10.1101\/2025.05.07.652668"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1002\/mds.29648","article-title":"Free-Water Imaging in Friedreich Ataxia Using Multi-Compartment Models","volume":"39","author":"Fernandez","year":"2024","journal-title":"Mov. Disord."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1359320","DOI":"10.3389\/fnagi.2024.1359320","article-title":"Morphological changes in the cerebellum during aging: Evidence from convolutional neural networks and shape analysis","volume":"16","author":"Wang","year":"2024","journal-title":"Front. Aging Neurosci."},{"key":"ref_66","unstructured":"Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., and Ghemawat, S. (2016). TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. arXiv."},{"key":"ref_67","unstructured":"Chollet, F. (2018). Keras: The Python deep learning library. Astrophys. Source Code Libr., Available online: https:\/\/ui.adsabs.harvard.edu\/abs\/2018ascl.soft06022C."},{"key":"ref_68","unstructured":"Kingma, D.P., and Ba, J.L. (2015, January 7\u20139). Adam: A method for stochastic optimization. Proceedings of the 3rd International Conference for Learning Representations (ICLR), San Diego, CA, USA."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Magnusson, M., Love, A., and Ellingsen, L.M. (2021, January 15\u201319). Automated brainstem parcellation using multi-atlas segmentation and deep neural network. Proceedings of the Medical Imaging 2021: Image Processing, Online.","DOI":"10.1117\/12.2581129"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1617","DOI":"10.1109\/TMI.2011.2138152","article-title":"Robust brain extraction across datasets and comparison with publicly available methods","volume":"30","author":"Iglesias","year":"2011","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1002\/acn3.504","article-title":"Brain atrophy measures in preclinical and manifest spinocerebellar ataxia type 2","volume":"5","author":"Reetz","year":"2018","journal-title":"Ann. Clin. Transl. Neurol."},{"key":"ref_72","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","volume":"Volume 9351","author":"Ronneberger","year":"2015","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015"},{"key":"ref_73","first-page":"424","article-title":"3D U-net: Learning dense volumetric segmentation from sparse annotation","volume":"Volume 9901","author":"Abdulkadir","year":"2016","journal-title":"Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2016: 19th International Conference"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/19\/6009\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T04:31:10Z","timestamp":1759293070000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/19\/6009"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,29]]},"references-count":73,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["s25196009"],"URL":"https:\/\/doi.org\/10.3390\/s25196009","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2025,9,29]]}}}