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。

基于多平面 UNet 的全自动脑 MRI 数据肿瘤分割