{"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/brain-tissue-segmentation-using-neuronet-with","title":"Brain Tissue Segmentation Using NeuroNet With Different Pre-processing Techniques","arxiv_id":"1904.00068","date":"2019-03-29","proceeding":null,"authors":["Fakrul Islam Tushar","Basel Alyafi","Md. Kamrul Hasan","Lavsen Dahal"],"abstract":"Automatic segmentation of brain Magnetic Resonance Imaging (MRI) images is\none of the vital steps for quantitative analysis of brain for further\ninspection. In this paper, NeuroNet has been adopted to segment the brain\ntissues (white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF))\nwhich uses Residual Network (ResNet) in encoder and Fully Convolution Network\n(FCN) in the decoder. To achieve the best performance, various hyper-parameters\nhave been tuned, while, network parameters (kernel and bias) were initialized\nusing the NeuroNet pre-trained model. Different pre-processing pipelines have\nalso been introduced to get a robust trained model. The model has been trained\nand tested on IBSR18 data-set. To validate the research outcome, performance\nwas measured quantitatively using Dice Similarity Coefficient (DSC) and is\nreported on average as 0.84 for CSF, 0.94 for GM, and 0.94 for WM. The outcome\nof the research indicates that for the IBSR18 data-set, pre-processing and\nproper tuning of hyper-parameters for NeuroNet model have improvement in DSC\nfor the brain tissue segmentation.","url_abs":"http://arxiv.org/abs/1904.00068v1","url_pdf":"http://arxiv.org/pdf/1904.00068v1.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":"brain-tissue-segmentation-using-neuronet-with","repo_url":"https://github.com/fitushar/Brain-Tissue-Segmentation-Using-Deep-Learning-Pipeline-NeuroNet","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"3d-semantic-segmentation","task_name":"3D Semantic Segmentation"},{"task_slug":"decoder","task_name":"Decoder"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}