{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fully-convolutional-network-ensembles-for","title":"Fully Convolutional Network Ensembles for White Matter Hyperintensities Segmentation in MR Images","arxiv_id":"1802.05203","date":"2018-02-14","proceeding":null,"authors":["Hongwei Li","Gongfa Jiang","Jian-Guo Zhang","Ruixuan Wang","Zhaolei Wang","Wei-Shi Zheng","Bjoern Menze"],"abstract":"White matter hyperintensities (WMH) are commonly found in the brains of\nhealthy elderly individuals and have been associated with various neurological\nand geriatric disorders. In this paper, we present a study using deep fully\nconvolutional network and ensemble models to automatically detect such WMH\nusing fluid attenuation inversion recovery (FLAIR) and T1 magnetic resonance\n(MR) scans. The algorithm was evaluated and ranked 1 st in the WMH Segmentation\nChallenge at MICCAI 2017. In the evaluation stage, the implementation of the\nalgorithm was submitted to the challenge organizers, who then independently\ntested it on a hidden set of 110 cases from 5 scanners. Averaged dice score,\nprecision and robust Hausdorff distance obtained on held-out test datasets were\n80%, 84% and 6.30mm respectively. These were the highest achieved in the\nchallenge, suggesting the proposed method is the state-of-the-art. In this\npaper, we provide detailed descriptions and quantitative analysis on key\ncomponents of the system. Furthermore, a study of cross-scanner evaluation is\npresented to discuss how the combination of modalities and data augmentation\naffect the generalization capability of the system. The adaptability of the\nsystem to different scanners and protocols is also investigated. A quantitative\nstudy is further presented to test the effect of ensemble size. Additionally,\nsoftware and models of our method are made publicly available. The\neffectiveness and generalization capability of the proposed system show its\npotential for real-world clinical practice.","url_abs":"http://arxiv.org/abs/1802.05203v3","url_pdf":"http://arxiv.org/pdf/1802.05203v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fully-convolutional-network-ensembles-for","repo_url":"https://github.com/hongweilibran/wmh_ibbmTum","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"fully-convolutional-network-ensembles-for","repo_url":"https://github.com/labhracorgi/lbhs_wmh_seg_manuals","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}