{"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/glioma-segmentation-with-cascaded-unet","title":"Glioma Segmentation with Cascaded Unet","arxiv_id":"1810.04008","date":"2018-10-09","proceeding":null,"authors":["Dmitry Lachinov","Evgeny Vasiliev","Vadim Turlapov"],"abstract":"MRI analysis takes central position in brain tumor diagnosis and treatment,\nthus it's precise evaluation is crucially important. However, it's 3D nature\nimposes several challenges, so the analysis is often performed on 2D\nprojections that reduces the complexity, but increases bias. On the other hand,\ntime consuming 3D evaluation, like, segmentation, is able to provide precise\nestimation of a number of valuable spatial characteristics, giving us\nunderstanding about the course of the disease.\\newline Recent studies, focusing\non the segmentation task, report superior performance of Deep Learning methods\ncompared to classical computer vision algorithms. But still, it remains a\nchallenging problem. In this paper we present deep cascaded approach for\nautomatic brain tumor segmentation. Similar to recent methods for object\ndetection, our implementation is based on neural networks; we propose\nmodifications to the 3D UNet architecture and augmentation strategy to\nefficiently handle multimodal MRI input, besides this we introduce approach to\nenhance segmentation quality with context obtained from models of the same\ntopology operating on downscaled data. We evaluate presented approach on BraTS\n2018 dataset and discuss results.","url_abs":"http://arxiv.org/abs/1810.04008v1","url_pdf":"http://arxiv.org/pdf/1810.04008v1.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":"glioma-segmentation-with-cascaded-unet","repo_url":"https://github.com/lachinov/brats2018-graphlabunn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mxnet","reach":null}],"tasks":[{"task_slug":"brain-tumor-segmentation","task_name":"Brain Tumor Segmentation"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"tumor-segmentation","task_name":"Tumor Segmentation"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"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}