{"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/niftynet-a-deep-learning-platform-for-medical","title":"NiftyNet: a deep-learning platform for medical imaging","arxiv_id":"1709.03485","date":"2017-09-11","proceeding":null,"authors":["Eli Gibson","Wenqi Li","Carole Sudre","Lucas Fidon","Dzhoshkun I. Shakir","Guotai Wang","Zach Eaton-Rosen","Robert Gray","Tom Doel","Yipeng Hu","Tom Whyntie","Parashkev Nachev","Marc Modat","Dean C. Barratt","Sébastien Ourselin","M. Jorge Cardoso","Tom Vercauteren"],"abstract":"Medical image analysis and computer-assisted intervention problems are\nincreasingly being addressed with deep-learning-based solutions. Established\ndeep-learning platforms are flexible but do not provide specific functionality\nfor medical image analysis and adapting them for this application requires\nsubstantial implementation effort. Thus, there has been substantial duplication\nof effort and incompatible infrastructure developed across many research\ngroups. This work presents the open-source NiftyNet platform for deep learning\nin medical imaging. The ambition of NiftyNet is to accelerate and simplify the\ndevelopment of these solutions, and to provide a common mechanism for\ndisseminating research outputs for the community to use, adapt and build upon.\n  NiftyNet provides a modular deep-learning pipeline for a range of medical\nimaging applications including segmentation, regression, image generation and\nrepresentation learning applications. Components of the NiftyNet pipeline\nincluding data loading, data augmentation, network architectures, loss\nfunctions and evaluation metrics are tailored to, and take advantage of, the\nidiosyncracies of medical image analysis and computer-assisted intervention.\nNiftyNet is built on TensorFlow and supports TensorBoard visualization of 2D\nand 3D images and computational graphs by default.\n  We present 3 illustrative medical image analysis applications built using\nNiftyNet: (1) segmentation of multiple abdominal organs from computed\ntomography; (2) image regression to predict computed tomography attenuation\nmaps from brain magnetic resonance images; and (3) generation of simulated\nultrasound images for specified anatomical poses.\n  NiftyNet enables researchers to rapidly develop and distribute deep learning\nsolutions for segmentation, regression, image generation and representation\nlearning applications, or extend the platform to new applications.","url_abs":"http://arxiv.org/abs/1709.03485v2","url_pdf":"http://arxiv.org/pdf/1709.03485v2.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":"niftynet-a-deep-learning-platform-for-medical","repo_url":"https://github.com/NifTK/NiftyNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"niftynet-a-deep-learning-platform-for-medical","repo_url":"https://github.com/Fabienne703/Data","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"niftynet-a-deep-learning-platform-for-medical","repo_url":"https://github.com/ReubenDo/U-HVED","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"niftynet-a-deep-learning-platform-for-medical","repo_url":"https://github.com/charan223/Brain-Tumor-Segmentation-using-Topological-Loss","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"niftynet-a-deep-learning-platform-for-medical","repo_url":"https://github.com/charan223/brain_tumor_topology","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"niftynet-a-deep-learning-platform-for-medical","repo_url":"https://github.com/charan223/topology-aware-networks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"niftynet-a-deep-learning-platform-for-medical","repo_url":"https://github.com/charan223/topology-conscious-networks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"niftynet-a-deep-learning-platform-for-medical","repo_url":"https://github.com/gift-surg/fetal_brain_seg","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"niftynet-a-deep-learning-platform-for-medical","repo_url":"https://github.com/julianbertini/MSNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"niftynet-a-deep-learning-platform-for-medical","repo_url":"https://github.com/black0017/MedicalZooPytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"medical-image-analysis","task_name":"Medical Image Analysis"},{"task_slug":"medical-image-generation","task_name":"Medical Image Generation"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1709.03485","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1709.03485"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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