Fully Automated Tumor Segmentation for Brain MRI data using Multiplanner UNet
This addresses the problem of accurate diagnosis and treatment planning for pediatric and adult brain tumors, but it is incremental as it refines an existing method with multi-planar information.
The study tackled automated segmentation of brain tumor subregions in MRI data using Multi-Planner U-Net (MPUnet) across three diverse datasets, showing higher accuracy for tumor core but variability in other regions like edema and enhancing tumor.
Automated segmentation of distinct tumor regions is critical for accurate diagnosis and treatment planning in pediatric brain tumors. This study evaluates the efficacy of the Multi-Planner U-Net (MPUnet) approach in segmenting different tumor subregions across three challenging datasets: Pediatrics Tumor Challenge (PED), Brain Metastasis Challenge (MET), and Sub-Sahara-Africa Adult Glioma (SSA). These datasets represent diverse scenarios and anatomical variations, making them suitable for assessing the robustness and generalization capabilities of the MPUnet model. By utilizing multi-planar information, the MPUnet architecture aims to enhance segmentation accuracy. Our results show varying performance levels across the evaluated challenges, with the tumor core (TC) class demonstrating relatively higher segmentation accuracy. However, variability is observed in the segmentation of other classes, such as the edema and enhancing tumor (ET) regions. These findings emphasize the complexity of brain tumor segmentation and highlight the potential for further refinement of the MPUnet approach and inclusion of MRI more data and preprocessing.