Core Concepts
A convolutional neural network-based method for accurate and robust brain tumor segmentation across diverse patient populations, including adults, pediatrics, and underserved sub-Saharan Africa.
Abstract
This work proposes a brain tumor segmentation method as part of the BraTS-GoAT challenge, which aims to segment tumors in brain MRI scans from various populations, such as adults, pediatrics, and underserved sub-Saharan Africa.
The key highlights and insights are:
- The authors employ the MedNeXt architecture, a recent CNN model for medical image segmentation, as their baseline.
- They implement extensive model ensembling and postprocessing techniques to enhance the reliability and accuracy of their predictions.
- The experiments show that their method performs well on the unseen validation set, with an average Dice Similarity Coefficient (DSC) of 85.54% and Hausdorff Distance 95 (HD95) of 27.88.
- The authors note that larger models, such as MedNeXt-M, perform better than smaller models, suggesting that the BraTS-GoAT competition is more challenging than previous BraTS competitions.
- The authors also discuss the importance of postprocessing steps, such as connected component analysis and size-based filtering, to reduce false positives in tumor detection.
Stats
The dataset for this challenge was compiled from diverse populations, including adults, pediatrics, and underrepresented groups from sub-Saharan Africa, comprising 2,251 brain MRI scans in the training set and 360 scans in the validation set.
The provided MRI modalities are T1, T1Gd, T2, and T2-FLAIR.
Each scan includes expert annotations that identify three tumor subtypes: enhancing tumor (ET), tumor core (TC), and whole tumor (WT).
Quotes
"To fill this gap, the organizer introduces a new challenge segment, namely BraTS Generalizability Across Tumors (BraTS-GoAT)."
"It is interesting to note that each MRI scan contains one or more tumors."