{"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-tumor-segmentation-and-tractographic","title":"Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction","arxiv_id":"1807.07716","date":"2018-07-20","proceeding":null,"authors":["Po-Yu Kao","Thuyen Ngo","Angela Zhang","Jefferson W. Chen","B. S. Manjunath"],"abstract":"This paper introduces a novel methodology to integrate human brain\nconnectomics and parcellation for brain tumor segmentation and survival\nprediction. For segmentation, we utilize an existing brain parcellation atlas\nin the MNI152 1mm space and map this parcellation to each individual subject\ndata. We use deep neural network architectures together with hard negative\nmining to achieve the final voxel level classification. For survival\nprediction, we present a new method for combining features from connectomics\ndata, brain parcellation information, and the brain tumor mask. We leverage the\naverage connectome information from the Human Connectome Project and map each\nsubject brain volume onto this common connectome space. From this, we compute\ntractographic features that describe potential neural disruptions due to the\nbrain tumor. These features are then used to predict the overall survival of\nthe subjects. The main novelty in the proposed methods is the use of normalized\nbrain parcellation data and tractography data from the human connectome project\nfor analyzing MR images for segmentation and survival prediction. Experimental\nresults are reported on the BraTS2018 dataset.","url_abs":"http://arxiv.org/abs/1807.07716v3","url_pdf":"http://arxiv.org/pdf/1807.07716v3.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-tumor-segmentation-and-tractographic","repo_url":"https://github.com/pykao/BraTS2018-tumor-segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"brain-tumor-segmentation","task_name":"Brain Tumor Segmentation"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"survival-prediction","task_name":"Survival Prediction"},{"task_slug":"tumor-segmentation","task_name":"Tumor Segmentation"}],"methods":[],"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}