{"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/v-net-fully-convolutional-neural-networks-for","title":"V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation","arxiv_id":"1606.04797","date":"2016-06-15","proceeding":null,"authors":["Fausto Milletari","Nassir Navab","Seyed-Ahmad Ahmadi"],"abstract":"Convolutional Neural Networks (CNNs) have been recently employed to solve\nproblems from both the computer vision and medical image analysis fields.\nDespite their popularity, most approaches are only able to process 2D images\nwhile most medical data used in clinical practice consists of 3D volumes. 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