{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T02:04:12Z","timestamp":1793325852195,"version":"4.5.0"},"reference-count":48,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,8,26]],"date-time":"2024-08-26T00:00:00Z","timestamp":1724630400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.es","clinicalkey.com.au","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Methods and Programs in Biomedicine"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1016\/j.cmpb.2024.108398","type":"journal-article","created":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T02:01:17Z","timestamp":1724810477000},"page":"108398","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":8,"special_numbering":"C","title":["Beyond pixel: Superpixel-based MRI segmentation through traditional machine learning and graph convolutional network"],"prefix":"10.1016","volume":"256","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7303-338X","authenticated-orcid":false,"given":"Zakia","family":"Khatun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8406-9328","authenticated-orcid":false,"suffix":"Jr.","given":"Halld\u00f3r","family":"J\u00f3nsson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mariella","family":"Tsirilaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5327-3702","authenticated-orcid":false,"given":"Nicola","family":"Maffulli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Oliva","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1454-0566","authenticated-orcid":false,"given":"Pauline","family":"Daval","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5033-9323","authenticated-orcid":false,"given":"Francesco","family":"Tortorella","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paolo","family":"Gargiulo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.cmpb.2024.108398_b1","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1111\/j.1365-2613.2007.00552.x","article-title":"Tendon matrix composition and turnover in relation to functional requirements: Tendon matrix composition and turnover","volume":"88","author":"Birch","year":"2007","journal-title":"Int. J. Exp. Pathol."},{"issue":"5","key":"10.1016\/j.cmpb.2024.108398_b2","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1007\/BF00635880","article-title":"In vivo achilles tendon loading during jumping in humans","volume":"71","author":"Fukashiro","year":"1995","journal-title":"Eur. J. Appl. Physiol."},{"key":"10.1016\/j.cmpb.2024.108398_b3","unstructured":"B.R. Freedman, J.A. Gordon, L.J. Soslowsky, The Achilles Tendon: Fundamental Properties and Mechanisms Governing Healing, arXiv:PMID: 25332943."},{"issue":"2","key":"10.1016\/j.cmpb.2024.108398_b4","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.fcl.2005.01.013","article-title":"Achilles tendon disorders: Etiology and epidemiology","volume":"10","author":"J\u00e4rvinen","year":"2005","journal-title":"Foot Ankle Clin."},{"issue":"11","key":"10.1016\/j.cmpb.2024.108398_b5","doi-asserted-by":"crossref","first-page":"819","DOI":"10.2519\/jospt.2015.0111","article-title":"Guidelines and pathways for clinical practice in tendinopathy: Their role and development","volume":"45","author":"Morrissey","year":"2015","journal-title":"J. Orthop. Sports Phys. Ther."},{"key":"10.1016\/j.cmpb.2024.108398_b6","doi-asserted-by":"crossref","unstructured":"Z. Khatun, M. Tsirilaki, A. Lindemann, F. Tortorella, N. Maffulli, H. Jonsson, P. Gargiulo, The Role of Muscle and Tendon in Predicting Cartilage Degeneration and Tendinopathy, in: 2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE, Rome, Italy, 2022, pp. 289\u2013294.","DOI":"10.1109\/MetroXRAINE54828.2022.9967653"},{"issue":"3","key":"10.1016\/j.cmpb.2024.108398_b7","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1177\/19476035221144746","article-title":"Toward new assessment of knee cartilage degeneration","volume":"14","author":"Aubonnet","year":"2023","journal-title":"Cartilage"},{"issue":"1","key":"10.1016\/j.cmpb.2024.108398_b8","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1186\/1475-925X-13-157","article-title":"Curvelet based automatic segmentation of supraspinatus tendon from ultrasound image: A focused assistive diagnostic method","volume":"13","author":"Gupta","year":"2014","journal-title":"BioMed. Eng. OnLine"},{"key":"10.1016\/j.cmpb.2024.108398_b9","unstructured":"B.-I. Chuang, et al. A medical imaging analysis system for trigger finger using an adaptive texture-based active shape model (ATASM) in ultrasound images, p. 21."},{"issue":"4","key":"10.1016\/j.cmpb.2024.108398_b10","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1109\/JBHI.2017.2723819","article-title":"A new active contours approach for finger extensor tendon segmentation in ultrasound images using prior knowledge and phase symmetry","volume":"22","author":"Martins","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.cmpb.2024.108398_b11","series-title":"Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies","first-page":"33","article-title":"Deformable model-based medical image segmentation","author":"Tsechpenakis","year":"2011"},{"issue":"1","key":"10.1016\/j.cmpb.2024.108398_b12","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1186\/s13018-014-0128-x","article-title":"Reliability of a 3 T MRI protocol for objective grading of supraspinatus tendonosis and partial thickness tears","volume":"9","author":"Bauer","year":"2014","journal-title":"J. Orthop. Surg. Res."},{"issue":"2","key":"10.1016\/j.cmpb.2024.108398_b13","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1177\/0363546519894333","article-title":"Rethinking patellar tendinopathy and partial patellar tendon tears: A novel classification system","volume":"48","author":"Golman","year":"2020","journal-title":"Am. J. Sports Med."},{"issue":"2","key":"10.1016\/j.cmpb.2024.108398_b14","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0055476","article-title":"Three-dimensional spectral-domain optical coherence tomography data analysis for glaucoma detection","volume":"8","author":"Xu","year":"2013","journal-title":"PLoS ONE"},{"key":"10.1016\/j.cmpb.2024.108398_b15","doi-asserted-by":"crossref","DOI":"10.1111\/jmi.12361","article-title":"Breast histopathology image segmentation using spatio-colour-texture based graph partition method: Breast histopathology image segmentation","author":"Belsare","year":"2016","journal-title":"J. Microsc."},{"key":"10.1016\/j.cmpb.2024.108398_b16","doi-asserted-by":"crossref","first-page":"345","DOI":"10.3233\/THC-171338","article-title":"A novel lung cancer detection algorithm for CADs based on SSP and level set","volume":"25","author":"Zhu","year":"2017","journal-title":"THC"},{"key":"10.1016\/j.cmpb.2024.108398_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102046","article-title":"BS-Net: Learning COVID-19 pneumonia severity on a large chest X-ray dataset","volume":"71","author":"Signoroni","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.cmpb.2024.108398_b18","doi-asserted-by":"crossref","DOI":"10.1109\/TGRS.2019.2949180","article-title":"Multiscale dynamic graph convolutional network for hyperspectral image classification","author":"Wan","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"6","key":"10.1016\/j.cmpb.2024.108398_b19","doi-asserted-by":"crossref","first-page":"2759","DOI":"10.1002\/mrm.27229","article-title":"Deep convolutional neural network for segmentation of knee joint anatomy: Zhou et al.","volume":"80","author":"Zhou","year":"2018","journal-title":"Magn. Reson. Med."},{"key":"10.1016\/j.cmpb.2024.108398_b20","doi-asserted-by":"crossref","DOI":"10.3389\/fcell.2023.1196191","article-title":"Attention-guided cascaded network with pixel-importance-balance loss for retinal vessel segmentation","volume":"11","author":"Su","year":"2023","journal-title":"Front. Cell Dev. Biol."},{"key":"10.1016\/j.cmpb.2024.108398_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.109155","article-title":"Source-free unsupervised domain adaptation for cross-modality abdominal multi-organ segmentation","volume":"250","author":"Hong","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.cmpb.2024.108398_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2022.108729","article-title":"Unsupervised domain adaptation for cross-modality liver segmentation via joint adversarial learning and self-learning","volume":"121","author":"Hong","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.cmpb.2024.108398_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106028","article-title":"Source-free unsupervised adaptive segmentation for knee joint MRI","volume":"92","author":"Li","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"issue":"1","key":"10.1016\/j.cmpb.2024.108398_b24","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/s12938-020-00768-1","article-title":"Segmentation of finger tendon and synovial sheath in ultrasound image using deep convolutional neural network","volume":"19","author":"Kuok","year":"2020","journal-title":"BioMed. Eng. OnLine"},{"key":"10.1016\/j.cmpb.2024.108398_b25","series-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II, Vol. 9901","year":"2016"},{"issue":"9","key":"10.1016\/j.cmpb.2024.108398_b26","doi-asserted-by":"crossref","first-page":"4164","DOI":"10.1002\/mp.14327","article-title":"Graph-convolutional-network-based interactive prostate segmentation in MR images","volume":"47","author":"Tian","year":"2020","journal-title":"Med. Phys."},{"key":"10.1016\/j.cmpb.2024.108398_b27","series-title":"2015 International Conference on Intelligent Networking and Collaborative Systems","first-page":"91","article-title":"Human thermal face recognition based on random linear oracle (RLO) ensembles","author":"Gaber","year":"2015"},{"issue":"11","key":"10.1016\/j.cmpb.2024.108398_b28","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC superpixels compared to state-of-the-art superpixel methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.cmpb.2024.108398_b29","unstructured":"D. Jain, Superpixels and SLIC [Online]. Available: https:\/\/darshita1405.medium.com\/superpixels-and-slic-6b2d8a6e4f08."},{"issue":"2","key":"10.1016\/j.cmpb.2024.108398_b30","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1007\/s10043-011-0047-6","article-title":"A spectral invariant representation of spectral reflectance","volume":"18","author":"Ibrahim","year":"2011","journal-title":"Opt. Rev."},{"key":"10.1016\/j.cmpb.2024.108398_b31","doi-asserted-by":"crossref","unstructured":"A. Vedaldi, S. Soatto, Quick Shift and Kernel Methods for Mode Seeking, in: Computer Vision, Vol. 5305, ECCV 2008, 2008, pp. 705\u2013718.","DOI":"10.1007\/978-3-540-88693-8_52"},{"key":"10.1016\/j.cmpb.2024.108398_b32","series-title":"The watershed concept and its use in segmentation: A brief history","author":"Meyer","year":"2012"},{"key":"10.1016\/j.cmpb.2024.108398_b33","doi-asserted-by":"crossref","unstructured":"J. Cheng, J. Liu, Y. Xu, F. Yin, D.W.K. Wong, B.-H. Lee, T.Y. Wong, Superpixel Classification for Initialization in Model-Based Optic Disc Segmentation, in: Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE.","DOI":"10.1109\/EMBC.2012.6346213"},{"key":"10.1016\/j.cmpb.2024.108398_b34","doi-asserted-by":"crossref","unstructured":"M.S. Siddiquee, N.S. Pathan, Optic Disc Segmentation Using Superpixel Based Features and Random Forest Classifier, in: 2019 4th Intl. Conference on Electrical Information and Communication Technology, EICT.","DOI":"10.1109\/EICT48899.2019.9068827"},{"issue":"2","key":"10.1016\/j.cmpb.2024.108398_b35","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1007\/s10278-018-0149-9","article-title":"Suspicious lesion segmentation on brain, mammograms and breast mr images using new optimized spatial feature based superpixel fuzzy C-means clustering","volume":"32","author":"Kumar","year":"2019","journal-title":"J. Digit. Imag."},{"key":"10.1016\/j.cmpb.2024.108398_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.scs.2021.103252","article-title":"Deep transfer learning for COVID-19 detection and infection localization with superpixel based segmentation","volume":"75","author":"Prakash","year":"2021","journal-title":"Sustainable Cities Soc."},{"key":"10.1016\/j.cmpb.2024.108398_b37","unstructured":"A. Ibrahim, E.-S.M. El-kenawy, Image Segmentation Methods Based on Superpixel Techniques: A Survey, Tech. Rep., 2020, p. 11."},{"issue":"11","key":"10.1016\/j.cmpb.2024.108398_b38","doi-asserted-by":"crossref","first-page":"2030","DOI":"10.1049\/iet-ipr.2018.5439","article-title":"Image segmentation algorithm based on superpixel clustering","volume":"12","author":"Cong","year":"2018","journal-title":"IET Image Process."},{"issue":"2","key":"10.1016\/j.cmpb.2024.108398_b39","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: Images are more than pictures, they are data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"key":"10.1016\/j.cmpb.2024.108398_b40","series-title":"Pyradiomics documentation","year":"2023"},{"key":"10.1016\/j.cmpb.2024.108398_b41","series-title":"Sklearn.preprocessing.StandardScaler","author":"Scikit-learn developers","year":"2023"},{"key":"10.1016\/j.cmpb.2024.108398_b42","unstructured":"T.N. Kipf, M. Welling, Semi-supervised classification with graph convolutional networks, in: ICLR, 2016."},{"key":"10.1016\/j.cmpb.2024.108398_b43","article-title":"Automatic classification of severity of COVID-19 patients using texture feature and random forest based on computed tomography images","author":"Amini","year":"2021","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"10.1016\/j.cmpb.2024.108398_b44","article-title":"Learning texture features from GLCM for classification of brain tumor MRI images using random forest classifier","author":"Unknown","year":"2022","journal-title":"WSEAS Trans. Signal Process."},{"key":"10.1016\/j.cmpb.2024.108398_b45","article-title":"Machine learning and image processing for medical image analysis of breast cancer diagnosis","author":"Jasti","year":"2021","journal-title":"J. Med. Imag. Health Inform."},{"key":"10.1016\/j.cmpb.2024.108398_b46","doi-asserted-by":"crossref","unstructured":"T. Alzyadat, S. Praet, G. Chetty, R. Goecke, D. Hughes, D. Kumar, G. Waddington, Automatic segmentation of achilles tendon tissues using deep convolutional neural network, in: Machine Learning in Medical Imaging: 11th Intl. Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Springer Intl. Publishing.","DOI":"10.1007\/978-3-030-59861-7_45"},{"key":"10.1016\/j.cmpb.2024.108398_b47","unstructured":"I. Scott, D. Connell, D. Moulton, S. Waters, A. Namburete, A. Arnab, P. Malliaras, An automated method for tendon image segmentation on ultrasound using grey-level co-occurrence matrix features and hidden Gaussian Markov random fields."},{"key":"10.1016\/j.cmpb.2024.108398_b48","article-title":"Segment anything in medical images","volume":"15","author":"Ma","year":"2024","journal-title":"Nature Commun."}],"container-title":["Computer Methods and Programs in Biomedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260724003912?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260724003912?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T09:11:32Z","timestamp":1726218692000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169260724003912"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11]]},"references-count":48,"alternative-id":["S0169260724003912"],"URL":"https:\/\/doi.org\/10.1016\/j.cmpb.2024.108398","relation":{},"ISSN":["0169-2607"],"issn-type":[{"value":"0169-2607","type":"print"}],"subject":[],"published":{"date-parts":[[2024,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Beyond pixel: Superpixel-based MRI segmentation through traditional machine learning and graph convolutional network","name":"articletitle","label":"Article Title"},{"value":"Computer Methods and Programs in Biomedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cmpb.2024.108398","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"108398"}}