{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T18:14:34Z","timestamp":1765995274040,"version":"build-2065373602"},"reference-count":53,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T00:00:00Z","timestamp":1702944000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Regione Autonoma Sardegna","award":["2022BP837R","DOT1329971-1"],"award-info":[{"award-number":["2022BP837R","DOT1329971-1"]}]},{"name":"MUR","award":["2022BP837R","DOT1329971-1"],"award-info":[{"award-number":["2022BP837R","DOT1329971-1"]}]},{"name":"MUR, PON","award":["2022BP837R","DOT1329971-1"],"award-info":[{"award-number":["2022BP837R","DOT1329971-1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Currently, Parkinson\u2019s Disease (PD) is diagnosed primarily based on symptoms by experts clinicians. Neuroimaging exams represent an important tool to confirm the clinical diagnosis. Among them, Brain Parenchyma Sonography (BPS) is used to evaluate the hyperechogenicity of Substantia Nigra (SN), found in more than 90% of PD patients. In this article, we exploit a new dataset of BPS images to investigate an automatic segmentation approach for SN that can increase the accuracy of the exam and its practicability in clinical routine. This study achieves state-of-the-art performance in SN segmentation of BPS images. Indeed, it is found that the modified U-Net network scores a Dice coefficient of 0.859 \u00b1 0.037. The results presented in this study demonstrate the feasibility and usefulness of SN automatic segmentation in BPS medical images, to the point that this study can be considered as the first stage of the development of an end-to-end CAD (Computer Aided Detection) system. Furthermore, the used dataset, which will be further enriched in the future, has proven to be very effective in supporting the training of CNNs and may pave the way for future studies in the field of CAD applied to PD.<\/jats:p>","DOI":"10.3390\/jimaging10010001","type":"journal-article","created":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T04:18:37Z","timestamp":1702959517000},"page":"1","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Segmentation of Substantia Nigra in Brain Parenchyma Sonographic Images Using Deep Learning"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5921-2508","authenticated-orcid":false,"given":"Giansalvo","family":"Gusinu","sequence":"first","affiliation":[{"name":"Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0140-8034","authenticated-orcid":false,"given":"Claudia","family":"Frau","sequence":"additional","affiliation":[{"name":"Department of Medicine, Surgery and Pharmacy, University of Sassari, Viale San Pietro 8, 07100 Sassari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4918-8334","authenticated-orcid":false,"given":"Giuseppe A.","family":"Trunfio","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2982-0665","authenticated-orcid":false,"given":"Paolo","family":"Solla","sequence":"additional","affiliation":[{"name":"Department of Medicine, Surgery and Pharmacy, University of Sassari, Viale San Pietro 8, 07100 Sassari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0566-2049","authenticated-orcid":false,"given":"Leonardo Antonio","family":"Sechi","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2283","DOI":"10.1093\/brain\/114.5.2283","article-title":"Ageing and Parkinson\u2019s disease: Substantia nigra regional selectivity","volume":"114","author":"Fearnley","year":"1991","journal-title":"Brain"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1591","DOI":"10.1002\/mds.26424","article-title":"MDS clinical diagnostic criteria for Parkinson\u2019s disease","volume":"30","author":"Postuma","year":"2015","journal-title":"Mov. Disord."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1212\/WNL.0000000000002350","article-title":"Accuracy of clinical diagnosis of Parkinson disease: A systematic review and meta-analysis","volume":"86","author":"Rizzo","year":"2016","journal-title":"Neurology"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1136\/jnnp.55.3.181","article-title":"Accuracy of clinical diagnosis of idiopathic Parkinson\u2019s disease: A clinico-pathological study of 100 cases","volume":"55","author":"Hughes","year":"1992","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/S1474-4422(04)00736-7","article-title":"Functional brain imaging in the differential diagnosis of Parkinson\u2019s disease","volume":"3","author":"Piccini","year":"2004","journal-title":"Lancet Neurol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1007\/s004150170114","article-title":"Echogenicity of the substantia nigra in Parkinson\u2019s disease and its relation to clinical findings","volume":"248","author":"Berg","year":"2001","journal-title":"J. Neurol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1212\/WNL.45.1.182","article-title":"Degeneration of substantia nigra in chronic Parkinson\u2019s disease visualized by transcranial color-coded real-time sonography","volume":"45","author":"Becker","year":"1995","journal-title":"Neurology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/S1474-4422(08)70067-X","article-title":"The specificity and sensitivity of transcranial ultrasound in the differential diagnosis of Parkinson\u2019s disease: A prospective blinded study","volume":"7","author":"Gaenslen","year":"2008","journal-title":"Lancet Neurol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1212\/WNL.53.5.1026","article-title":"Vulnerability of the nigrostriatal system as detected by transcranial ultrasound","volume":"53","author":"Berg","year":"1999","journal-title":"Neurology"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/S0074-7742(10)90002-0","article-title":"Method and validity of transcranial sonography in movement disorders","volume":"90","author":"Walter","year":"2010","journal-title":"Int. Rev. Neurobiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1002\/mds.20311","article-title":"Five-year follow-up study of hyperechogenicity of the substantia nigra in Parkinson\u2019s disease","volume":"20","author":"Berg","year":"2005","journal-title":"Mov. Disord."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"S429","DOI":"10.1016\/S1353-8020(08)70043-9","article-title":"Ultrasound in the (premotor) diagnosis of Parkinson\u2019s disease","volume":"13","author":"Berg","year":"2007","journal-title":"Park. Relat. Disord."},{"key":"ref_13","unstructured":"Goodfellow, I.J., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press. Available online: http:\/\/www.deeplearningbook.org."},{"key":"ref_14","first-page":"3523","article-title":"Image Segmentation Using Deep Learning: A Survey","volume":"44","author":"Minaee","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_16","unstructured":"Ronneberger, O. (2017). Bildverarbeitung f\u00fcr die Medizin 2017, Springer."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_18","first-page":"S83","article-title":"Segmenting the substantia nigra in ultrasound images for early diagnosis of Parkinson\u2019s disease","volume":"2","author":"Kier","year":"2007","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.ultras.2012.04.005","article-title":"Automated segmentation of transcranial sonographic images in the diagnostics of Parkinson\u2019s disease","volume":"53","author":"Sakalauskas","year":"2013","journal-title":"Ultrasonics"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1109\/83.661186","article-title":"Snakes, shapes, and gradient vector flow","volume":"7","author":"Xu","year":"1998","journal-title":"IEEE Trans. Image Process."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","unstructured":"Ahmadi, S.A., Baust, M., Karamalis, A., Plate, A., Boetzel, K., Klein, T., and Navab, N. (2011, January 18\u201322). Midbrain segmentation in transcranial 3D ultrasound for Parkinson diagnosis. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Toronto, ON, Canada.","DOI":"10.1007\/978-3-642-23626-6_45"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Milletari, F., Ahmadi, S.A., Kroll, C., Hennersperger, C., Tombari, F., Shah, A., Plate, A., Boetzel, K., and Navab, N. (2015, January 5\u20139). Robust segmentation of various anatomies in 3d ultrasound using hough forests and learned data representations. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24571-3_14"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, H., Zheng, Y., Park, J.H., Heng, P.A., and Zhou, S.K. (2016, January 17\u201321). Iterative multi-domain regularized deep learning for anatomical structure detection and segmentation from ultrasound images. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Athens, Greece.","DOI":"10.1007\/978-3-319-46723-8_56"},{"key":"ref_25","unstructured":"Juknevicius, A.R., and Sakalauskas, A. (2016, January 24\u201325). Algorithm for the detection of the mid-brain in B mode ultrasound images. Proceedings of the Biomedical Engineering 2016, Kaunas, Lithuania."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1753","DOI":"10.1002\/jum.14528","article-title":"Transcranial ultrasonographic image analysis system for decision support in parkinson disease","volume":"37","author":"Sakalauskas","year":"2018","journal-title":"J. Ultrasound Med."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1111\/j.1558-5646.2008.00557.x","article-title":"Modeling three-dimensional morphological structures using spherical harmonics","volume":"63","author":"Shen","year":"2009","journal-title":"Evolution"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Pauly, O., Ahmadi, S.A., Plate, A., Boetzel, K., and Navab, N. (2012, January 1\u20135). Detection of substantia nigra echogenicities in 3D transcranial ultrasound for early diagnosis of Parkinson disease. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Nice, France.","DOI":"10.1007\/978-3-642-33454-2_55"},{"key":"ref_29","unstructured":"Rackerseder, J., G\u00f6bl, R., Navab, N., and Hennersperger, C. (2019). Fully automatic segmentation of 3D brain ultrasound: Learning from coarse annotations. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1007\/s10916-018-1088-1","article-title":"Medical image analysis using convolutional neural networks: A review","volume":"42","author":"Anwar","year":"2018","journal-title":"J. Med. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Alzubaidi, M.S., Shah, U., Dhia Zubaydi, H., Dolaat, K., Abd-Alrazaq, A.A., Ahmed, A., and Househ, M. (2021). The role of neural network for the detection of Parkinson\u2019s disease: A scoping review. Healthcare, 9.","DOI":"10.3390\/healthcare9060740"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chen, G., Dai, Y., and Zhang, J. (2022). C-Net: Cascaded convolutional neural network with global guidance and refinement residuals for breast ultrasound images segmentation. Comput. Methods Programs Biomed., 225.","DOI":"10.1016\/j.cmpb.2022.107086"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lin, X., Zhou, X., Tong, T., Nie, X., Wang, L., Zheng, H., Li, J., Xue, E., Chen, S., and Zheng, M. (2022). A Super-resolution Guided Network for Improving Automated Thyroid Nodule Segmentation. Comput. Methods Programs Biomed., 227.","DOI":"10.1016\/j.cmpb.2022.107186"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"101864","DOI":"10.1016\/j.inffus.2023.101864","article-title":"Causal knowledge fusion for 3D cross-modality cardiac image segmentation","volume":"99","author":"Guo","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1504\/IJBET.2022.124188","article-title":"Segmentation of liver computed tomography images using dictionary-based snakes","volume":"39","author":"Shanila","year":"2022","journal-title":"Int. J. Biomed. Eng. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Guo, J., Odu, A., and Pedrosa, I. (2022). Deep learning kidney segmentation with very limited training data using a cascaded convolution neural network. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0267753"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.cmpb.2018.01.025","article-title":"NiftyNet: A deep-learning platform for medical imaging","volume":"158","author":"Gibson","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Li, F.-F. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The Pascal Visual Object Classes (VOC) Challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (2016, January 27\u201330). The Cityscapes Dataset for Semantic Urban Scene Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref_41","unstructured":"Parkhi, O.M., Vedaldi, A., Zisserman, A., and Jawahar, C.V. (2022, July 01). The Oxford-IIIT Pet Dataset. Available online: https:\/\/www.robots.ox.ac.uk\/~vgg\/data\/pets\/."},{"key":"ref_42","unstructured":"kaggle.com (2022, July 01). Kaggle Competition: Ultrasound Nerve Segmentation. Available online: https:\/\/www.kaggle.com\/competitions\/ultrasound-nerve-segmentation\/."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1055\/s-0033-1356415","article-title":"Transcranial sonography (TCS) of brain parenchyma in movement disorders: Quality standards, diagnostic applications and novel technologies","volume":"35","author":"Walter","year":"2014","journal-title":"Ultraschall Der Med.-Eur. J. Ultrasound"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.ultrasmedbio.2006.07.021","article-title":"Transcranial brain parenchyma sonography in movement disorders: State of the art","volume":"33","author":"Walter","year":"2007","journal-title":"Ultrasound Med. Biol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"83002","DOI":"10.1109\/ACCESS.2021.3086530","article-title":"Deep neural architectures for medical image semantic segmentation","volume":"9","author":"Khan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3286","DOI":"10.21037\/qims-20-1356","article-title":"Comparative evaluation of conventional and deep learning methods for semi-automated segmentation of pulmonary nodules on CT","volume":"11","author":"Bianconi","year":"2021","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Behboodi, B., and Rivaz, H. (2019, January 23\u201327). Ultrasound segmentation using u-net: Learning from simulated data and testing on real data. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8857218"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2510","DOI":"10.1109\/TUFFC.2020.3015081","article-title":"Fine-tuning U-Net for ultrasound image segmentation: Different layers, different outcomes","volume":"67","author":"Amiri","year":"2020","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Zhao, H., and Sun, N. (2017, January 11\u201313). Improved U-net model for nerve segmentation. Proceedings of the International Conference on Image and Graphics, Quebec City, QC, Canada.","DOI":"10.1007\/978-3-319-71589-6_43"},{"key":"ref_50","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_51","unstructured":"bonline (2022, September 08). Keras Implementation of Deeplabv3+. Available online: https:\/\/github.com\/bonlime\/keras-deeplab-v3-plus\/."},{"key":"ref_52","unstructured":"Bhatia, V. (2022, September 08). Ultrasound Nerve Seg\u2014UNET from Scratch. Available online: https:\/\/www.kaggle.com\/code\/vidushibhatia\/2-ultrasound-nerve-seg-unet-from-scratch\/."},{"key":"ref_53","first-page":"011007","article-title":"Breast ultrasound lesions recognition: End-to-end deep learning approaches","volume":"6","author":"Yap","year":"2018","journal-title":"J. Med. Imaging"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/10\/1\/1\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:41:14Z","timestamp":1760132474000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/10\/1\/1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,19]]},"references-count":53,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["jimaging10010001"],"URL":"https:\/\/doi.org\/10.3390\/jimaging10010001","relation":{},"ISSN":["2313-433X"],"issn-type":[{"type":"electronic","value":"2313-433X"}],"subject":[],"published":{"date-parts":[[2023,12,19]]}}}