{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T23:57:37Z","timestamp":1790639857238,"version":"4.1.0"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"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":["Phys Eng Sci Med"],"DOI":"10.1007\/s13246-026-01809-0","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T10:24:58Z","timestamp":1790591098000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Atherosclerosis diagnosis from magnetic resonance images: an empirical approach to automate carotid vessel wall segmentation and feature-based prediction"],"prefix":"10.1007","author":[{"given":"Sriman Bidhan","family":"Baray","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rusab","family":"Sarmun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. Sajidul","family":"Islam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5839-2826","authenticated-orcid":false,"given":"Md Shaheenur Islam","family":"Sumon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adam","family":"Mushtak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M.","family":"Murugappan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haniza","family":"Yazid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad E. H.","family":"Chowdhury","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,28]]},"reference":[{"key":"1809_CR1","doi-asserted-by":"publisher","DOI":"10.1186\/s40809-016-0014-5","author":"M Kontzialis","year":"2016","unstructured":"Kontzialis M, Wasserman BA (2016) Intracranial vessel wall imaging: current applications and clinical implications. Neurovasc Imaging. https:\/\/doi.org\/10.1186\/s40809-016-0014-5","journal-title":"Neurovasc Imaging"},{"key":"1809_CR2","volume-title":"MRI: basic principles and applications","author":"MA Brown","year":"2011","unstructured":"Brown MA, Semelka RC (2011) MRI: basic principles and applications. John Wiley & Sons"},{"issue":"5","key":"1809_CR3","doi-asserted-by":"publisher","first-page":"1266","DOI":"10.1002\/mrm.25507","volume":"74","author":"R Ahmad","year":"2015","unstructured":"Ahmad R, Xue H, Giri S, Ding Y, Craft J, Simonetti OP (2015) Variable density incoherent spatiotemporal acquisition (VISTA) for highly accelerated cardiac MRI. Magn Reson Med 74(5):1266\u20131278","journal-title":"Magn Reson Med"},{"issue":"1","key":"1809_CR4","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1002\/jmri.22592","volume":"34","author":"Y Qiao","year":"2011","unstructured":"Qiao Y, Anwar J, Zhang JJ, Hui X, Wasserman DJ, Jarso DE, Zeiler DA (2011) Intracranial arterial wall imaging using three-dimensional high isotropic resolution black blood MRI at 3.0 Tesla. J Magn Reson Imaging 34(1):22\u201330","journal-title":"J Magn Reson Imaging"},{"key":"1809_CR5","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/272691","author":"WH Lin","year":"2013","unstructured":"Lin WH, Zhang H, Zhang Y-T (2013) Investigation on cardiovascular risk prediction using physiological parameters. Comput Math Methods Med. https:\/\/doi.org\/10.1155\/2013\/272691","journal-title":"Comput Math Methods Med"},{"issue":"12","key":"1809_CR6","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1007\/s11517-014-1203-5","volume":"52","author":"CP Loizou","year":"2014","unstructured":"Loizou CP (2014) A review of ultrasound common carotid artery image and video segmentation techniques. Med Biol Eng Comput 52(12):1073\u20131093","journal-title":"Med Biol Eng Comput"},{"issue":"1","key":"1809_CR7","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/s12652-021-03295-6","volume":"14","author":"MK Abd-Ellah","year":"2023","unstructured":"Abd-Ellah MK, Khalaf AA, Gharieb RR, Hassanin DA (2023) Automatic diagnosis of common carotid artery disease using different machine learning techniques. J Ambient Intell Humaniz Comput 14(1):113\u2013129","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"9","key":"1809_CR8","doi-asserted-by":"publisher","first-page":"1353","DOI":"10.7863\/jum.2008.27.9.1353","volume":"27","author":"F Faita","year":"2008","unstructured":"Faita F, Gemignani V, Bianchini E, Giannarelli C, Ghiadoni L, Demi M (2008) Real-time measurement system for evaluation of the carotid intima-media thickness with a robust edge operator. J Ultrasound Med 27(9):1353\u20131361","journal-title":"J Ultrasound Med"},{"issue":"5","key":"1809_CR9","doi-asserted-by":"publisher","first-page":"1112","DOI":"10.1109\/TUFFC.2010.1522","volume":"57","author":"F Molinari","year":"2010","unstructured":"Molinari F, Zeng G, Suri JS (2010) Intima-media thickness: setting a standard for a completely automated method of ultrasound measurement. IEEE Trans Ultrason Ferroelectr Freq Control 57(5):1112\u20131124","journal-title":"IEEE Trans Ultrason Ferroelectr Freq Control"},{"issue":"1","key":"1809_CR10","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/1049-9660(92)90003-L","volume":"55","author":"DJ Williams","year":"1992","unstructured":"Williams DJ, Shah M (1992) A fast algorithm for active contours and curvature estimation. CVGIP Image Underst 55(1):14\u201326","journal-title":"CVGIP Image Underst"},{"issue":"4","key":"1809_CR11","doi-asserted-by":"publisher","first-page":"4489","DOI":"10.14419\/ijet.v7i4.19005","volume":"7","author":"M Jena","year":"2018","unstructured":"Jena M, Mishra SP, Mishra D (2018) A survey on applications of machine learning techniques for medical image segmentation. Int J Eng Technol 7(4):4489\u20134495","journal-title":"Int J Eng Technol"},{"key":"1809_CR12","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1016\/j.neucom.2014.10.027","volume":"151","author":"J Alam","year":"2015","unstructured":"Alam J, Hassan M, Khan A, Chaudhry A (2015) Robust fuzzy RBF network based image segmentation and intelligent decision making system for carotid artery ultrasound images. Neurocomputing 151:745\u2013755","journal-title":"Neurocomputing"},{"key":"1809_CR13","doi-asserted-by":"crossref","unstructured":"Xie M, Li Y, Xue Y, Huntress L, Beckerman W, Rahimi S, Ady J, Roshan U (2020) Vessel lumen segmentation in carotid artery ultrasounds with the U-Net convolutional neural network. In: 2020 IEEE international conference on bioinformatics and biomedicine (BIBM). pp. 2680\u20132684. IEEE.","DOI":"10.1109\/BIBM49941.2020.9313434"},{"issue":"1","key":"1809_CR14","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1186\/s12880-021-00568-6","volume":"21","author":"M Ziegler","year":"2021","unstructured":"Ziegler M, Alfraeus J, Bustamante M, Good E, Engvall J, de Muinck E, Dyverfeldt P (2021) Automated segmentation of the individual branches of the carotid arteries in contrast-enhanced MR angiography using DeepMedic. BMC Med Imaging 21(1):38","journal-title":"BMC Med Imaging"},{"issue":"4","key":"1809_CR15","doi-asserted-by":"publisher","first-page":"045033","DOI":"10.1088\/1361-6560\/abd4bb","volume":"66","author":"C Zhu","year":"2021","unstructured":"Zhu C, Dong L, Bi J, Lin Y, Luo J, Liu L (2021) Cascaded residual U-net for fully automatic segmentation of 3D carotid artery in high-resolution multi-contrast MR images. Phys Med Biol 66(4):045033","journal-title":"Phys Med Biol"},{"key":"1809_CR16","first-page":"237","volume":"12032","author":"D Alblas","year":"2022","unstructured":"Alblas D, Brune C, Wolterink JM (2022) Deep-learning-based carotid artery vessel wall segmentation in black-blood MRI using anatomical priors. Medical Imag 2022 Image Process 2022 12032:237\u2013244","journal-title":"Medical Imag 2022 Image Process 2022"},{"key":"1809_CR17","doi-asserted-by":"publisher","first-page":"1835","DOI":"10.1007\/s11548-020-02248-2","volume":"15","author":"LA Groves","year":"1835","unstructured":"Groves LA, VanBerlo B, Veinberg N, Alboog A, Peters TM, Chen EC (1835) Automatic segmentation of the carotid artery and internal jugular vein from 2D ultrasound images for 3D vascular reconstruction. Int J Comput Assist Radiol Surg 15:1835\u20131846","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"1809_CR18","doi-asserted-by":"crossref","unstructured":"Singh S, Sahani AK (2020) A machine learning approach to carotid wall localization in A-mode ultrasound. In: 2020 IEEE international symposium on medical measurements and applications (MeMeA), 2020: IEEE, pp. 1\u20135","DOI":"10.1109\/MeMeA49120.2020.9137228"},{"issue":"13","key":"1809_CR19","doi-asserted-by":"publisher","first-page":"1766","DOI":"10.1093\/bioinformatics\/bts238","volume":"28","author":"VA Huynh-Thu","year":"2012","unstructured":"Huynh-Thu VA, Saeys Y, Wehenkel L, Geurts P (2012) Statistical interpretation of machine learning-based feature importance scores for biomarker discovery. Bioinformatics 28(13):1766\u20131774","journal-title":"Bioinformatics"},{"issue":"9","key":"1809_CR20","doi-asserted-by":"publisher","first-page":"1092","DOI":"10.1161\/CIRCRESAHA.117.311312","volume":"121","author":"B Ambale-Venkatesh","year":"2017","unstructured":"Ambale-Venkatesh B, Yang C, Wu CH, Almeida DS, Donekal BR, Liu H, Tracy PJ, Bluemke M, Lima JA (2017) Cardiovascular event prediction by machine learning: the multi-ethnic study of atherosclerosis. Circ Res 121(9):1092\u20131101","journal-title":"Circ Res"},{"key":"1809_CR21","doi-asserted-by":"crossref","unstructured":"Terrada O, Cherradi B, Raihani A, Bouattane O (2019) Classification and Prediction of atherosclerosis diseases using machine learning algorithms. In: 2019 5th international conference on optimization and applications (ICOA), 2019: IEEE, pp. 1\u20135","DOI":"10.1109\/ICOA.2019.8727688"},{"key":"1809_CR22","doi-asserted-by":"crossref","unstructured":"Xie J, Wu R, Wang H, Kong Y, Li H, Zhang W (2019) A novel weight learning approach based on density for accurate prediction of atherosclerosis. In: Intelligent computing theories and application: 15th international conference, ICIC 2019, Nanchang, China, August 3\u20136, 2019, Proceedings, Part II 15. Springer, pp 190\u2013200","DOI":"10.1007\/978-3-030-26969-2_18"},{"key":"1809_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11883-018-0736-8","volume":"20","author":"A Boi","year":"2018","unstructured":"Boi A, Montisci G, Piga A, Mura LC (2018) A survey on coronary atherosclerotic plaque tissue characterization in intravascular optical coherence tomography. Curr Atheroscler Rep 20:1\u201317","journal-title":"Curr Atheroscler Rep"},{"key":"1809_CR24","doi-asserted-by":"publisher","first-page":"172","DOI":"10.3389\/fcvm.2019.00172","volume":"6","author":"N Hampe","year":"2019","unstructured":"Hampe N, Wolterink JM, Van Velzen SG, Leiner T, I\u0161gum I (2019) Machine learning for assessment of coronary artery disease in cardiac CT: a survey. Front Cardiovasc Med 6:172","journal-title":"Front Cardiovasc Med"},{"issue":"2","key":"1809_CR25","doi-asserted-by":"publisher","first-page":"1203","DOI":"10.1364\/BOE.8.001203","volume":"8","author":"A Abdolmanafi","year":"2017","unstructured":"Abdolmanafi A, Duong L, Dahdah N, Cheriet F (2017) Deep feature learning for automatic tissue classification of coronary artery using optical coherence tomography. Biomed Opt Express 8(2):1203\u20131220","journal-title":"Biomed Opt Express"},{"issue":"12","key":"1809_CR26","doi-asserted-by":"publisher","first-page":"5544","DOI":"10.1002\/mp.13739","volume":"46","author":"J Wu","year":"2019","unstructured":"Wu J, Sun Y, Wang J, Luo Y, Liu J, Liu M, Li X (2019) Deep morphology aided diagnosis network for segmentation of carotid artery vessel wall and diagnosis of carotid atherosclerosis on black-blood vessel wall MRI. Med Phys 46(12):5544\u20135561","journal-title":"Med Phys"},{"key":"1809_CR27","unstructured":"Hu S, Liao Z, Xia Y (2022) Label propagation for 3D carotid vessel wall segmentation and atherosclerosis diagnosis. arXiv preprint arXiv:2208.13337"},{"key":"1809_CR28","doi-asserted-by":"publisher","first-page":"102402","DOI":"10.1016\/j.compmedimag.2024.102402","volume":"116","author":"J Wang","year":"2024","unstructured":"Wang J, Yu F, Zhang M, Lu J, Qian Z (2024) A 3D framework for segmentation of carotid artery vessel wall and identification of plaque compositions in multi-sequence MR images. Comput Med Imaging Graph 116:102402","journal-title":"Comput Med Imaging Graph"},{"issue":"4","key":"1809_CR29","doi-asserted-by":"publisher","first-page":"044503","DOI":"10.1117\/1.JMI.11.4.044503","volume":"11","author":"H Rahlfs","year":"2024","unstructured":"Rahlfs H, H\u00fcllebrand M, Schmitter S, Strecker C, Harloff A, Hennemuth A (2024) Learning carotid vessel wall segmentation in black-blood MRI using sparsely sampled cross-sections from 3D data. J Med Imaging 11(4):044503\u2013044503","journal-title":"J Med Imaging"},{"issue":"1","key":"1809_CR30","doi-asserted-by":"publisher","first-page":"13894","DOI":"10.1038\/s41598-024-64265-4","volume":"14","author":"SW Yoo","year":"2024","unstructured":"Yoo SW, Yang S, Kim JE, Huh KH, Lee SS, Heo MS, Yi WJ (2024) CACSNet for automatic robust classification and segmentation of carotid artery calcification on panoramic radiographs using a cascaded deep learning network. Sci Rep 14(1):13894","journal-title":"Sci Rep"},{"key":"1809_CR31","doi-asserted-by":"crossref","unstructured":"Li H, Song W, Liu A, Qin P (2025) DBF-UNet: a two-stage framework for carotid artery segmentation with pseudo-label generation. In: 2025 international joint conference on neural networks (IJCNN) (pp. 1\u20138). IEEE","DOI":"10.1109\/IJCNN64981.2025.11227897"},{"key":"1809_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2025.102607","author":"H Gan","year":"2025","unstructured":"Gan H, Liu L, Wang F, Yang Z, Huang Z, Zhou R (2025) SML-Net: semi-supervised multi-task learning network for carotid plaque segmentation and classification. Comput Med Imaging Graph. https:\/\/doi.org\/10.1016\/j.compmedimag.2025.102607","journal-title":"Comput Med Imaging Graph"},{"issue":"1","key":"1809_CR33","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/s42979-025-04535-8","volume":"7","author":"R Amrit","year":"2025","unstructured":"Amrit R, Nithin JY, Areeckal AS (2025) SWRAU-Net: shearlet-integrated convolution based wavelet residual attention U-Net for segmentation of carotid artery vessel wall in 3D-MERGE MRI. SN Comput Sci 7(1):27","journal-title":"SN Comput Sci"},{"key":"1809_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpbup.2025.100230","author":"MC Pali","year":"2025","unstructured":"Pali MC, Schwaiger C, Galijasevic M, Ladenhauf VK, Mangesius S, Gizewski ER (2025) Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation. Comput Methods Programs Biomed Update. https:\/\/doi.org\/10.1016\/j.cmpbup.2025.100230","journal-title":"Comput Methods Programs Biomed Update"},{"key":"1809_CR35","doi-asserted-by":"publisher","first-page":"1502830","DOI":"10.3389\/fmed.2025.1502830","volume":"12","author":"ZH Ahmed Abu Alregal","year":"2025","unstructured":"Ahmed Abu Alregal ZH, Amran GA, Al-Bakhrani AA, Abdul Amir Mohammad S, Alabrah A, Alkhalil L, Ibrahim A, Ghaffar M (2025) Carotid plaque segmentation and classification using MRI-based plaque texture analysis and convolutional neural network. Front Med 12:1502830","journal-title":"Front Med"},{"key":"1809_CR36","unstructured":"Fu F, Lin Z, Yang X, Huang X, Chen X, Meng H, Li B (2026) Deep learning for high-resolution magnetic resonance vessel"},{"key":"1809_CR37","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-026-42847-8","author":"CH Tsou","year":"2026","unstructured":"Tsou CH, Liu HM, Huang A (2026) Deep learning\u2013based basilar artery wall and lumen segmentation from 1-mm MR vessel wall imaging. Sci Rep. https:\/\/doi.org\/10.1038\/s41598-026-42847-8","journal-title":"Sci Rep"},{"key":"1809_CR38","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference, Munich, Germany, October 5\u20139, 2015, Proceedings, Part III 18. Springer, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1809_CR39","doi-asserted-by":"crossref","unstructured":"Guo C, Szemenyei M, Yi Y, Wang W, Chen B, Fan C (2021) Sa-unet: spatial attention u-net for retinal vessel segmentation. In: 2020 25th international conference on pattern recognition (ICPR), 2021: IEEE, pp. 1236\u20131242","DOI":"10.1109\/ICPR48806.2021.9413346"},{"key":"1809_CR40","doi-asserted-by":"crossref","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: 4th international workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4, 2018: Springer, pp. 3\u201311","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"1809_CR41","doi-asserted-by":"publisher","first-page":"105002","DOI":"10.1016\/j.compbiomed.2021.105002","volume":"139","author":"AM Tahir","year":"2021","unstructured":"Tahir AM, Alrahabi MM, Mujtaba KA, Yerima SY, Khan MS, Rehman A (2021) COVID-19 infection localization and severity grading from chest X-ray images. Comput Biol Med 139:105002","journal-title":"Comput Biol Med"},{"key":"1809_CR42","doi-asserted-by":"publisher","first-page":"105620","DOI":"10.1016\/j.compbiomed.2022.105620","volume":"147","author":"S Gul","year":"2022","unstructured":"Gul S, Khan MS, Bibi A, Khandakar A, Ayari MA, Chowdhury ME (2022) Deep learning techniques for liver and liver tumor segmentation: a review. Comput Biol Med 147:105620","journal-title":"Comput Biol Med"},{"issue":"12","key":"1809_CR43","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481\u20132495","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"21","key":"1809_CR44","doi-asserted-by":"publisher","first-page":"8890","DOI":"10.3390\/s23218890","volume":"23","author":"MSA Hossain","year":"2023","unstructured":"Hossain MSA, Gul S, Chowdhury ME, Khan MS, Sumon MSI, Bhuiyan EH, Khandakar A, Hossain M, Sadique A, Al-Hashimi I, Ayari MA (2023) Deep learning framework for liver segmentation from t 1-weighted MRI images. Sensors (Basel) 23(21):8890","journal-title":"Sensors (Basel)"},{"issue":"15","key":"1809_CR45","doi-asserted-by":"publisher","first-page":"2537","DOI":"10.3390\/diagnostics13152537","volume":"13","author":"MM Khan","year":"2023","unstructured":"Khan MM, Barki MA, Rauf MA, Rauf RA, Naz S (2023) A deep learning-based automatic segmentation and 3D visualization technique for intracranial hemorrhage detection using computed tomography images. Diagnostics 13(15):2537","journal-title":"Diagnostics"},{"key":"1809_CR46","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"1809_CR47","volume-title":"Variational analysis","author":"RT Rockafellar","year":"2009","unstructured":"Rockafellar RT, Wets RJ-B (2009) Variational analysis. Springer"},{"key":"1809_CR48","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/0-387-33006-2_4","volume-title":"System modeling and optimization","author":"T Birsan","year":"2006","unstructured":"Birsan T, Tiba D (2006) One hundred years since the introduction of the set distance by Dimitrie Pompeiu. System modeling and optimization. Springer US, Boston, pp 35\u201339"},{"key":"1809_CR49","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. pp. 785\u2013794","DOI":"10.1145\/2939672.2939785"},{"issue":"17","key":"1809_CR50","doi-asserted-by":"publisher","first-page":"5658","DOI":"10.3390\/jcm12175658","volume":"12","author":"KR Islam","year":"2023","unstructured":"Islam KR, Hasan MR, Rahman MM, Khandakar MA, Ayubli M (2023) Machine learning-based early prediction of sepsis using electronic health records: a systematic review. J Clin Med 12(17):5658","journal-title":"J Clin Med"},{"key":"1809_CR51","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The elements of statistical learning: data mining, inference, and prediction","author":"T Hastie","year":"2009","unstructured":"Hastie T, Tibshirani R, Friedman JH, Friedman JH (2009) The elements of statistical learning: data mining, inference, and prediction. Springer"},{"key":"1809_CR52","doi-asserted-by":"crossref","unstructured":"Ho TK (1995) Random decision forests. In Proceedings of 3rd international conference on document analysis and recognition. Vol. 1, pp. 278\u2013282. IEEE","DOI":"10.1109\/ICDAR.1995.598994"},{"key":"1809_CR53","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-10-213","volume":"10","author":"BH Menze","year":"2009","unstructured":"Menze BH, Kelm BM, Masuch R, Himmelreich U, Hamprecht FA (2009) A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinf 10:1\u201316","journal-title":"BMC Bioinf"},{"key":"1809_CR54","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","volume":"63","author":"P Geurts","year":"2006","unstructured":"Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Mach Learn 63:3\u201342","journal-title":"Mach Learn"},{"key":"1809_CR55","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1007\/978-3-319-60801-3_27","volume-title":"Geomatic approaches for modeling land change scenarios","author":"H Taud","year":"2018","unstructured":"Taud H, Mas J (2018) Multilayer perceptron (MLP). Geomatic approaches for modeling land change scenarios. pp 451\u2013455"},{"key":"1809_CR56","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1007\/978-0-387-78189-1_8","volume-title":"Modern multivariate statistical techniques: regression, classification, and manifold learning","author":"AJ Izenman","year":"2013","unstructured":"Izenman AJ (2013) Linear discriminant analysis. Modern multivariate statistical techniques: regression, classification, and manifold learning. Springer, pp 237\u2013280"},{"key":"1809_CR57","unstructured":"Wright RE (1995) Logistic regression. Ready to submit your research"},{"issue":"4","key":"1809_CR58","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/5254.708428","volume":"13","author":"MA Hearst","year":"1998","unstructured":"Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B (1998) Support vector machines. IEEE Intell Syst 13(4):18\u201328","journal-title":"IEEE Intell Syst"},{"key":"1809_CR59","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/978-3-642-41136-6_5","volume-title":"Empirical inference: festschrift in honor of Vladimir N. Vapnik","author":"RE Schapire","year":"2013","unstructured":"Schapire RE (2013) Explaining adaboost. Empirical inference: festschrift in honor of Vladimir N. Vapnik. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 37\u201352"},{"issue":"2","key":"1809_CR60","doi-asserted-by":"publisher","first-page":"1883","DOI":"10.4249\/scholarpedia.1883","volume":"4","author":"LE Peterson","year":"2009","unstructured":"Peterson LE (2009) K-nearest neighbor. Scholarpedia 4(2):1883","journal-title":"Scholarpedia"},{"key":"1809_CR61","doi-asserted-by":"publisher","first-page":"21","DOI":"10.3389\/fnbot.2013.00021","volume":"7","author":"A Natekin","year":"2013","unstructured":"Natekin A, Knoll A (2013) Gradient boosting machines, a tutorial. Front Neurorobot 7:21","journal-title":"Front Neurorobot"},{"key":"1809_CR62","unstructured":"Wehenkel L (2015) Ensembles of extremely randomized trees and some generic applications. In: Proceedings of robust methods for power system state estimation and load forecasting"},{"key":"1809_CR63","first-page":"1063","volume":"13","author":"G Biau","year":"2012","unstructured":"Biau G (2012) Analysis of a random forests model. J Mach Learn Res 13:1063\u20131095","journal-title":"J Mach Learn Res"},{"key":"1809_CR64","doi-asserted-by":"publisher","first-page":"102035","DOI":"10.1016\/j.media.2021.102035","volume":"71","author":"J Ma","year":"2021","unstructured":"Ma J, Zhang K, Ding Y, Liu Y, Shi Z, Wang H (2021) Loss odyssey in medical image segmentation. Med Image Anal 71:102035","journal-title":"Med Image Anal"},{"key":"1809_CR65","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"1809_CR66","unstructured":"Thamm F, Denzinger F, Rist L, Vicario CM, Kordon F, Maier A (2022) Segmentation of the carotid lumen and vessel wall using deep learning and location priors. arXiv preprint arXiv:2201.06259"}],"container-title":["Physical and Engineering Sciences in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13246-026-01809-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13246-026-01809-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13246-026-01809-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T23:02:28Z","timestamp":1790636548000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13246-026-01809-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,28]]},"references-count":66,"alternative-id":["1809"],"URL":"https:\/\/doi.org\/10.1007\/s13246-026-01809-0","relation":{},"ISSN":["2662-4729","2662-4737"],"issn-type":[{"value":"2662-4729","type":"print"},{"value":"2662-4737","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,28]]},"assertion":[{"value":"27 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 September 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 September 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no conflict of interest.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This work has utilized the CarOtid vessel wall SegMentation and atherosclerOsis diagnosiS challenge (COSMOS 2022) dataset (\n                      \n                      ) published at Medical Image Computing and Computer Assisted Intervention Society (MICCAI) 2022 and the dataset has 50 users\u2019 MRI VISTA scans. However, the dataset lacks enough ground truth segmentation masks and atherosclerosis annotated cases, providing annotations for only around 20% of all the 2D slices. We took the initial 30 cases, and all the slices were annotated for segmentation training by researchers who received training from practicing radiologists. The pre-processed dataset and annotated dataset used in this study can be made available upon a reasonable request to the corresponding author. Hence, Institutional Review Board Statement & Informed Consent Statement are Not applicable.","order":2,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":3,"name":"Ethics","label":"Informed Consent","group":{"name":"EthicsHeading","label":"Declarations"}}]}}