使用多平面UNet对脑MRI数据进行全自动肿瘤分割
Fully Automated Tumor Segmentation for Brain MRI data using Multiplanner UNet
AI总结：
本文评估了多平面U-Net（MPUnet）在PED、MET和SSA三个脑肿瘤数据集上的分割性能，发现肿瘤核心分割精度较高，而水肿和增强肿瘤区域分割存在变异性，表明该方法仍需进一步改进并纳入更多MRI数据。
摘要：
对脑肿瘤不同区域的自动分割对于儿童脑肿瘤的准确诊断和治疗规划至关重要。本研究评估了多平面U-Net（MPUnet）方法在三个具有挑战性的数据集上分割不同肿瘤子区域的效果，这三个数据集分别是：儿童肿瘤挑战赛（PED）、脑转移瘤挑战赛（MET）和撒哈拉以南非洲成人胶质瘤（SSA）。这些数据集代表了不同的场景和解剖变异，使其适合评估MPUnet模型的鲁棒性和泛化能力。通过利用多平面信息，MPUnet架构旨在提高分割精度。我们的结果显示，在不同评估挑战中性能水平各不相同，其中肿瘤核心（TC）类别表现出相对较高的分割精度。然而，在其他类别的分割中观察到变异性，例如水肿和增强肿瘤（ET）区域。这些发现强调了脑肿瘤分割的复杂性，并凸显了进一步改进MPUnet方法以及纳入更多MRI数据和预处理的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
