利用MedNeXt优化脑肿瘤分割：BraTS 2024 SSA和儿科

Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and Pediatrics

November 24, 2024

作者: Sarim Hashmi, Juan Lugo, Abdelrahman Elsayed, Dinesh Saggurthi, Mohammed Elseiagy, Alikhan Nurkamal, Jaskaran Walia, Fadillah Adamsyah Maani, Mohammad Yaqub

cs.AI

摘要

在脑部磁共振成像中识别关键的病理特征对于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，容易受到人为错误的影响。因此，已经投入了大量研究来开发能够准确分割3D多模态脑部磁共振扫描中肿瘤的机器学习方法。尽管取得了进展，但最先进的模型通常受到其训练数据的限制，引发了对其在应用于可能引入分布转移的不同人群时可靠性的担忧。这种转移可能源自较低质量的磁共振成像技术（例如在撒哈拉以南非洲）或患者人口统计数据的变化（例如儿童）。BraTS-2024挑战提供了一个平台来解决这些问题。本研究介绍了我们在BraTS-2024 SSA和儿科肿瘤任务中使用MedNeXt、全面模型集成和彻底后处理进行肿瘤分割的方法论。我们的方法在未见过的验证集上表现出色，BraTS-2024 SSA数据集上的平均Dice相似系数（DSC）为0.896，BraTS儿科肿瘤数据集上的平均DSC为0.830。此外，我们的方法在BraTS-2024 SSA数据集上的平均Hausdorff距离（HD95）为14.682，在BraTS儿科数据集上的平均HD95为37.508。我们的GitHub存储库可以在以下链接中访问：项目存储库：https://github.com/python-arch/BioMbz-Optimizing-Brain-Tumor-Segmentation-with-MedNeXt-BraTS-2024-SSA-and-Pediatrics

English

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