{"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-radiomics","title":"Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge","arxiv_id":"1802.10508","date":"2018-02-28","proceeding":null,"authors":["Fabian Isensee","Philipp Kickingereder","Wolfgang Wick","Martin Bendszus","Klaus H. Maier-Hein"],"abstract":"Quantitative analysis of brain tumors is critical for clinical decision\nmaking. While manual segmentation is tedious, time consuming and subjective,\nthis task is at the same time very challenging to solve for automatic\nsegmentation methods. In this paper we present our most recent effort on\ndeveloping a robust segmentation algorithm in the form of a convolutional\nneural network. Our network architecture was inspired by the popular U-Net and\nhas been carefully modified to maximize brain tumor segmentation performance.\nWe use a dice loss function to cope with class imbalances and use extensive\ndata augmentation to successfully prevent overfitting. Our method beats the\ncurrent state of the art on BraTS 2015, is one of the leading methods on the\nBraTS 2017 validation set (dice scores of 0.896, 0.797 and 0.732 for whole\ntumor, tumor core and enhancing tumor, respectively) and achieves very good\nDice scores on the test set (0.858 for whole, 0.775 for core and 0.647 for\nenhancing tumor). We furthermore take part in the survival prediction\nsubchallenge by training an ensemble of a random forest regressor and\nmultilayer perceptrons on shape features describing the tumor subregions. Our\napproach achieves 52.6% accuracy, a Spearman correlation coefficient of 0.496\nand a mean square error of 209607 on the test set.","url_abs":"http://arxiv.org/abs/1802.10508v1","url_pdf":"http://arxiv.org/pdf/1802.10508v1.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-radiomics","repo_url":"https://github.com/Jack-Etheredge/Brain-Tumor-Segmentation-3D-UNet-CNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"brain-tumor-segmentation-and-radiomics","repo_url":"https://github.com/SWKoreaBME/sangwook_kits2019","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"brain-tumor-segmentation-and-radiomics","repo_url":"https://github.com/angadbajwa23/Brain-Image-Segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"brain-tumor-segmentation-and-radiomics","repo_url":"https://github.com/angadbajwa23/Brain-Tumor-Segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"brain-tumor-segmentation-and-radiomics","repo_url":"https://github.com/angadbajwa23/Segmentation-of-2D-Brain-MR-Images-using-Deep-Neural-Architectures","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"brain-tumor-segmentation-and-radiomics","repo_url":"https://github.com/pykao/Modified-3D-UNet-Pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"brain-tumor-segmentation-and-radiomics","repo_url":"https://github.com/shalabh147/Brain-Tumor-Segmentation-and-Survival-Prediction-using-Deep-Neural-Networks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"brain-tumor-segmentation","task_name":"Brain Tumor Segmentation"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"survival-prediction","task_name":"Survival Prediction"},{"task_slug":"tumor-segmentation","task_name":"Tumor Segmentation"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dice-loss","method_name":"Dice Loss"},{"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":{"atlas_url":"https://app.syntology.ai/?focus=1802.10508","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.10508"}},"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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