Brain tumor segmentation, and cross-modality domain adaptation for medical image segmentation : MICCAI challenges, BraTS 2023 and CrossMoDA 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12 and 8, 2024 : proc...最新文献
Daniel Capellán-Martín, Zhifan Jiang, Abhijeet Parida, Xinyang Liu, Van Lam, Hareem Nisar, Austin Tapp, Sarah Elsharkawi, María J Ledesma-Carbayo, Syed Muhammad Anwar, Marius George Linguraru
{"title":"Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging.","authors":"Daniel Capellán-Martín, Zhifan Jiang, Abhijeet Parida, Xinyang Liu, Van Lam, Hareem Nisar, Austin Tapp, Sarah Elsharkawi, María J Ledesma-Carbayo, Syed Muhammad Anwar, Marius George Linguraru","doi":"10.1007/978-3-031-76163-8_20","DOIUrl":"10.1007/978-3-031-76163-8_20","url":null,"abstract":"<p><p>Segmenting brain tumors in multi-parametric magnetic resonance imaging enables performing quantitative analysis in support of clinical trials and personalized patient care. This analysis provides the potential to impact clinical decision-making processes, including diagnosis and prognosis. In 2023, the well-established Brain Tumor Segmentation (BraTS) challenge presented a substantial expansion with eight tasks and 4,500 brain tumor cases. In this paper, we present a deep learning-based ensemble strategy that is evaluated for newly included tumor cases in three tasks: pediatric brain tumors (PED), intracranial meningioma (MEN), and brain metastases (MET). In particular, we ensemble outputs from state-of-the-art nnU-Net and Swin UNETR models on a region-wise basis. Furthermore, we implemented a targeted post-processing strategy based on a cross-validated threshold search to improve the segmentation results for tumor sub-regions. The evaluation of our proposed method on unseen test cases for the three tasks resulted in lesion-wise Dice scores for PED: 0.653, 0.809, 0.826; MEN: 0.876, 0.867, 0.849; and MET: 0.555, 0.6, 0.58; for the enhancing tumor, tumor core, and whole tumor, respectively. Our method was ranked first for PED, third for MEN, and fourth for MET, respectively.</p>","PeriodicalId":521168,"journal":{"name":"Brain tumor segmentation, and cross-modality domain adaptation for medical image segmentation : MICCAI challenges, BraTS 2023 and CrossMoDA 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12 and 8, 2024 : proc...","volume":"14669 ","pages":"221-232"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12720496/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145822418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}