Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and Pediatrics
Sarim Hashmi, Juan Lugo, Abdelrahman Elsayed, Dinesh Saggurthi, Mohammed Elseiagy, Alikhan Nurkamal, Jaskaran Walia, Fadillah Adamsyah Maani, Mohammad Yaqub
2024-11-28
Summary
This paper discusses a new method called MedNeXt, which improves the process of identifying brain tumors in MRI scans by using advanced machine learning techniques.
What's the problem?
Identifying brain tumors, especially gliomas, in MRI scans is crucial for patient care, but doing it manually is slow and prone to mistakes. Current machine learning models often struggle when applied to different types of MRI data, which can vary in quality and patient demographics, making them less reliable.
What's the solution?
The authors developed MedNeXt to enhance tumor segmentation in brain MRIs. They participated in the BraTS-2024 challenge, using their model to segment tumors in both standard and pediatric datasets. Their approach involved combining various model techniques and thorough post-processing to improve accuracy. They achieved high scores on metrics that measure the quality of tumor identification, demonstrating that their method works well even with different types of data.
Why it matters?
This research is important because it aims to improve the accuracy and efficiency of brain tumor detection using machine learning. By providing better tools for doctors, it can lead to earlier and more accurate diagnoses, ultimately improving treatment outcomes for patients with brain tumors.
Abstract
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