{"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/isointense-infant-brain-segmentation-with-a","title":"Isointense Infant Brain Segmentation with a Hyper-dense Connected Convolutional Neural Network","arxiv_id":"1710.05956","date":"2017-10-16","proceeding":null,"authors":["Jose Dolz","Ismail Ben Ayed","Jing Yuan","Christian Desrosiers"],"abstract":"Neonatal brain segmentation in magnetic resonance (MR) is a challenging\nproblem due to poor image quality and low contrast between white and gray\nmatter regions. Most existing approaches for this problem are based on\nmulti-atlas label fusion strategies, which are time-consuming and sensitive to\nregistration errors. As alternative to these methods, we propose a\nhyper-densely connected 3D convolutional neural network that employs MR-T1 and\nT2 images as input, which are processed independently in two separated paths.\nAn important difference with previous densely connected networks is the use of\ndirect connections between layers from the same and different paths. Adopting\nsuch dense connectivity helps the learning process by including deep\nsupervision and improving gradient flow. We evaluated our approach on data from\nthe MICCAI Grand Challenge on 6-month infant Brain MRI Segmentation (iSEG),\nobtaining very competitive results. Among 21 teams, our approach ranked first\nor second in most metrics, translating into a state-of-the-art performance.","url_abs":"http://arxiv.org/abs/1710.05956v4","url_pdf":"http://arxiv.org/pdf/1710.05956v4.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":"isointense-infant-brain-segmentation-with-a","repo_url":"https://github.com/josedolz/LiviaNET","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"brain-segmentation","task_name":"Brain Segmentation"},{"task_slug":"infant-brain-mri-segmentation","task_name":"Infant Brain Mri Segmentation"},{"task_slug":"mri-segmentation","task_name":"MRI segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}