利用MedNeXt對腦腫瘤分割進行優化：BraTS 2024 SSA和兒科

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

在腦部磁共振影像中識別關鍵的病理特徵對於膠質瘤患者的長期存活至關重要。然而，手動分割耗時，需要專家干預，並容易受到人為錯誤的影響。因此，已經有大量研究致力於開發能夠準確分割3D多模態腦部磁共振掃描中腫瘤的機器學習方法。儘管取得了進展，但最先進的模型通常受到其訓練數據的限制，這引發了對於應用於可能引入分布轉移的不同人群時其可靠性的擔憂。這種轉移可能源自較低質量的磁共振技術（例如在撒哈拉以南非洲）或患者人口統計變化（例如兒童）。BraTS-2024挑戰提供了一個平台來解決這些問題。本研究介紹了我們在BraTS-2024 SSA和兒科腫瘤任務中使用MedNeXt、全面模型集成和徹底後處理來分割腫瘤的方法論。我們的方法在未見過的驗證集上表現出色，在BraTS-2024 SSA數據集上實現了平均Dice相似性系數（DSC）為0.896，在BraTS兒科腫瘤數據集上實現了平均DSC為0.830。此外，我們的方法在BraTS-2024 SSA數據集上實現了平均Hausdorff距離（HD95）為14.682，在BraTS兒科腫瘤數據集上實現了平均HD95為37.508。我們的GitHub存儲庫可以在此處訪問：專案存儲庫：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