{"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/rs-net-regression-segmentation-3d-cnn-for","title":"RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tumours","arxiv_id":"1807.10972","date":"2018-07-28","proceeding":null,"authors":["Raghav Mehta","Tal Arbel"],"abstract":"Accurate synthesis of a full 3D MR image containing tumours from available\nMRI (e.g. to replace an image that is currently unavailable or corrupted) would\nprovide a clinician as well as downstream inference methods with important\ncomplementary information for disease analysis. In this paper, we present an\nend-to-end 3D convolution neural network that takes a set of acquired MR image\nsequences (e.g. T1, T2, T1ce) as input and concurrently performs (1) regression\nof the missing full resolution 3D MRI (e.g. FLAIR) and (2) segmentation of the\ntumour into subtypes (e.g. enhancement, core). The hypothesis is that this\nwould focus the network to perform accurate synthesis in the area of the\ntumour. Experiments on the BraTS 2015 and 2017 datasets [1] show that: (1) the\nproposed method gives better performance than state-of-the-art methods in terms\nof established global evaluation metrics (e.g. PSNR), (2) replacing real MR\nvolumes with the synthesized MRI does not lead to significant degradation in\ntumour and sub-structure segmentation accuracy. The system further provides\nuncertainty estimates based on Monte Carlo (MC) dropout [11] for the\nsynthesized volume at each voxel, permitting quantification of the system's\nconfidence in the output at each location.","url_abs":"http://arxiv.org/abs/1807.10972v1","url_pdf":"http://arxiv.org/pdf/1807.10972v1.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":"rs-net-regression-segmentation-3d-cnn-for","repo_url":"https://github.com/RagMeh11/RS-Net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[{"method_slug":"3d-convolution","method_name":"3D Convolution"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dropout","method_name":"Dropout"}],"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}