用于磁共振成像中脑肿瘤分割的模型集成
Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging
AI总结：
本文提出按区域集成nnU-Net与Swin UNETR并结合交叉验证阈值后处理的脑肿瘤分割方法，在BraTS 2023三个任务中取得领先成绩。
摘要：
在多参数磁共振成像中分割脑肿瘤，使得开展定量分析以支持临床试验和个性化患者护理成为可能。这种分析有可能影响临床决策过程，包括诊断和预后。2023年，成熟的脑肿瘤分割（Brain Tumor Segmentation，BraTS）挑战赛进行了大幅扩展，包含八个任务和4,500个脑肿瘤病例。本文提出一种基于深度学习的集成策略，并在三个任务中新纳入的肿瘤病例上进行评估：儿童脑肿瘤（PED）、颅内脑膜瘤（MEN）和脑转移瘤（MET）。具体而言，我们按区域集成最先进的nnU-Net和Swin UNETR模型的输出。此外，我们实施了一种基于交叉验证阈值搜索的针对性后处理策略，以改善肿瘤子区域的分割结果。在三个任务的未见测试病例上对所提方法进行评估，得到按病灶计算的Dice分数，PED分别为0.653、0.809、0.826；MEN分别为0.876、0.867、0.849；MET分别为0.555、0.6、0.58；三者依次对应增强肿瘤、肿瘤核心和全肿瘤。我们的方法在PED中排名第一，在MEN中排名第三，在MET中排名第四。
英文摘要：
Segmenting brain tumors in multi-parametric magnetic resonance imaging enables performing quantitative analysis in support of clinical trials and personalized patient care. This analysis provides the potential to impact clinical decision-making processes, including diagnosis and prognosis. In 2023, the well-established Brain Tumor Segmentation (BraTS) challenge presented a substantial expansion with eight tasks and 4,500 brain tumor cases. In this paper, we present a deep learning-based ensemble strategy that is evaluated for newly included tumor cases in three tasks: pediatric brain tumors (PED), intracranial meningioma (MEN), and brain metastases (MET). In particular, we ensemble outputs from state-of-the-art nnU-Net and Swin UNETR models on a region-wise basis. Furthermore, we implemented a targeted post-processing strategy based on a cross-validated threshold search to improve the segmentation results for tumor sub-regions. The evaluation of our proposed method on unseen test cases for the three tasks resulted in lesion-wise Dice scores for PED: 0.653, 0.809, 0.826; MEN: 0.876, 0.867, 0.849; and MET: 0.555, 0.6, 0.58; for the enhancing tumor, tumor core, and whole tumor, respectively. Our method was ranked first for PED, third for MEN, and fourth for MET, respectively.
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。
