{"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/real-time-automatic-fetal-brain-extraction-in","title":"Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning","arxiv_id":"1710.09338","date":"2017-10-25","proceeding":null,"authors":["Seyed Sadegh Mohseni Salehi","Seyed Raein Hashemi","Clemente Velasco-Annis","Abdelhakim Ouaalam","Judy A. Estroff","Deniz Erdogmus","Simon K. Warfield","Ali Gholipour"],"abstract":"Brain segmentation is a fundamental first step in neuroimage analysis. In the\ncase of fetal MRI, it is particularly challenging and important due to the\narbitrary orientation of the fetus, organs that surround the fetal head, and\nintermittent fetal motion. Several promising methods have been proposed but are\nlimited in their performance in challenging cases and in real-time\nsegmentation. We aimed to develop a fully automatic segmentation method that\nindependently segments sections of the fetal brain in 2D fetal MRI slices in\nreal-time. To this end, we developed and evaluated a deep fully convolutional\nneural network based on 2D U-net and autocontext, and compared it to two\nalternative fast methods based on 1) a voxelwise fully convolutional network\nand 2) a method based on SIFT features, random forest and conditional random\nfield. We trained the networks with manual brain masks on 250 stacks of\ntraining images, and tested on 17 stacks of normal fetal brain images as well\nas 18 stacks of extremely challenging cases based on extreme motion, noise, and\nseverely abnormal brain shape. Experimental results show that our U-net\napproach outperformed the other methods and achieved average Dice metrics of\n96.52% and 78.83% in the normal and challenging test sets, respectively. With\nan unprecedented performance and a test run time of about 1 second, our network\ncan be used to segment the fetal brain in real-time while fetal MRI slices are\nbeing acquired. This can enable real-time motion tracking, motion detection,\nand 3D reconstruction of fetal brain MRI.","url_abs":"http://arxiv.org/abs/1710.09338v1","url_pdf":"http://arxiv.org/pdf/1710.09338v1.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":"real-time-automatic-fetal-brain-extraction-in","repo_url":"https://bitbucket.org/bchradiology/u-net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"3d-reconstruction","task_name":"3D Reconstruction"},{"task_slug":"brain-segmentation","task_name":"Brain Segmentation"},{"task_slug":"motion-detection","task_name":"Motion Detection"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"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}