利用MedNeXt优化脑肿瘤分割：BraTS 2024撒哈拉以南非洲与儿科任务
Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and Pediatrics
AI总结：
针对手动分割脑肿瘤耗时易错、现有模型受训练数据限制且在分布偏移人群中可靠性不足的问题，本研究提出基于MedNeXt、模型集成和后处理的方法，在BraTS 2024相关任务验证集上取得优异分割性能。
摘要：
识别脑MRI中的关键病理特征对胶质瘤患者的长期生存至关重要。然而，手动分割耗时且需要专家干预，还易受人为错误影响。因此，大量研究致力于开发机器学习方法，以准确分割3D多模态脑MRI扫描中的肿瘤。尽管取得进展，最先进的模型常受限于训练数据，在应用于可能引入分布偏移的多样化人群时，其可靠性引发担忧。此类偏移可能源于较低质量的MRI技术（如撒哈拉以南非洲地区）或患者人口统计学特征的差异（如儿童）。BraTS-2024挑战赛为解决这些问题提供了平台。本研究介绍了我们在BraTS-2024撒哈拉以南非洲（SSA）和儿科肿瘤任务中分割肿瘤的方法，该方法使用MedNeXt、全面的模型集成和彻底的后处理。我们的方法在未见过的验证集上表现优异，在BraTS-2024 SSA数据集上达到平均Dice相似系数（DSC）0.896，在BraTS儿科肿瘤数据集上达到平均DSC 0.830。此外，我们的方法在BraTS-2024 SSA数据集上实现平均Hausdorff距离（HD95）14.682，在BraTS儿科数据集上实现平均HD95 37.508。我们的GitHub仓库可通过以下链接访问：Project Repository : https://github.com/python-arch/BioMbz-Optimizing-Brain-Tumor-Segmentation-with-MedNeXt-BraTS-2024-SSA-and-Pediatrics
英文摘要：
Identifying key pathological features in brain MRIs is crucial for the long-term survival of glioma patients. However, manual segmentation is time-consuming, requiring expert intervention and is susceptible to human error. Therefore, significant research has been devoted to developing machine learning methods that can accurately segment tumors in 3D multimodal brain MRI scans. Despite their progress, state-of-the-art models are often limited by the data they are trained on, raising concerns about their reliability when applied to diverse populations that may introduce distribution shifts. Such shifts can stem from lower quality MRI technology (e.g., in sub-Saharan Africa) or variations in patient demographics (e.g., children). The BraTS-2024 challenge provides a platform to address these issues. This study presents our methodology for segmenting tumors in the BraTS-2024 SSA and Pediatric Tumors tasks using MedNeXt, comprehensive model ensembling, and thorough postprocessing. Our approach demonstrated strong performance on the unseen validation set, achieving an average Dice Similarity Coefficient (DSC) of 0.896 on the BraTS-2024 SSA dataset and an average DSC of 0.830 on the BraTS Pediatric Tumor dataset. Additionally, our method achieved an average Hausdorff Distance (HD95) of 14.682 on the BraTS-2024 SSA dataset and an average HD95 of 37.508 on the BraTS Pediatric dataset. Our GitHub repository can be accessed here: Project Repository : https://github.com/python-arch/BioMbz-Optimizing-Brain-Tumor-Segmentation-with-MedNeXt-BraTS-2024-SSA-and-Pediatrics
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
