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.

MPUnet 在分割儿童脑肿瘤的不同区域方面显示出差异性能水平，其中肿瘤核心（TC）类表现出相对较高的分割准确性，然而在水肿和增强肿瘤（ET）区域的分割中观察到变异，这凸显了脑肿瘤分割的复杂性并强调了进一步完善 MPUnet 方法和包括 MRI 更多数据和预处理的潜力。