融合多个先进模型提升脑肿瘤影像分割精度，助力临床诊疗决策。
Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging
- 采用区域级集成策略融合nnU-Net与Swin UNETR模型输出。
- 在儿科、脑膜瘤和转移瘤任务中，病变级Dice得分最高达0.826。
- 专为肿瘤亚区设计后处理优化，适合医学影像研究者参考。
多参数磁共振成像中的脑肿瘤分割可支持临床试验与个性化患者管理的定量分析，具有影响诊断与预后判断的潜力。2023年，成熟的脑肿瘤分割（BraTS）挑战赛扩展至八个任务，涵盖4,500例脑肿瘤病例。本文提出一种基于深度学习的集成方法，针对新增的三个任务——儿童脑肿瘤（PED）、颅内脑膜瘤（MEN）和脑转移瘤（MET）进行评估。具体通过区域级集成最先进的nnU-Net与Swin UNETR模型，并引入基于交叉验证阈值搜索的针对性后处理策略，优化肿瘤子区域分割效果。在未见测试数据上的评估结果显示：PED任务中增强肿瘤、肿瘤核心、全肿瘤的病变级Dice分数分别为0.653、0.809、0.826；MEN任务中对应分数为0.876、0.867、0.849；MET任务中为0.555、0.600、0.580。本方法在PED任务排名第一，MEN任务排名第三，MET任务排名第四。
原文摘要 · Abstract (English)
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.
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