  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.
