适用于多样化脑肿瘤的自适应分割流程：放射组学引导的亚型分类与病灶级模型集成
Adaptable Segmentation Pipeline for Diverse Brain Tumors with Radiomic-Guided Subtyping and Lesion-Wise Model Ensemble
AI总结：
提出一种灵活模块化的自适应分割流程，通过放射组学特征检测肿瘤亚型并平衡训练，结合病灶级性能指标优化模型集成与后处理，在BraTS 2025挑战赛中达到顶尖性能，支持临床定量肿瘤测量。
摘要：
在多参数磁共振成像（MRI）上对脑肿瘤进行鲁棒且可泛化的分割仍然困难，因为肿瘤类型差异很大。BraTS 2025 Lighthouse挑战赛在多种高质量成人及儿童肿瘤数据集上对分割方法进行基准测试：多联盟国际儿童脑肿瘤分割（PED）、术前脑膜瘤肿瘤分割（MEN）、脑膜瘤放射治疗分割（MEN-RT）以及治疗前后脑转移瘤分割（MET）。我们提出了一种灵活、模块化且自适应的流程，通过选择和组合最先进的模型，并在训练前后应用肿瘤和病灶特定的处理，来提高分割性能。从MRI中提取的放射组学特征有助于检测肿瘤亚型，确保更平衡的训练。自定义的病灶级性能指标决定了每个模型在集成中的影响力，并优化了进一步细化预测的后处理，使工作流能够针对每个病例定制每一步。在BraTS测试集上，我们的流程在多个挑战中取得了与顶尖算法相当的性能。这些发现证实，自定义的病灶感知处理与模型选择能够产生鲁棒的分割，而无需将方法锁定在特定的网络架构上。我们的方法在临床实践中具有定量肿瘤测量的潜力，支持诊断和预后。
英文摘要：
Robust and generalizable segmentation of brain tumors on multi-parametric magnetic resonance imaging (MRI) remains difficult because tumor types differ widely. The BraTS 2025 Lighthouse Challenge benchmarks segmentation methods on diverse high-quality datasets of adult and pediatric tumors: multi-consortium international pediatric brain tumor segmentation (PED), preoperative meningioma tumor segmentation (MEN), meningioma radiotherapy segmentation (MEN-RT), and segmentation of pre- and post-treatment brain metastases (MET). We present a flexible, modular, and adaptable pipeline that improves segmentation performance by selecting and combining state-of-the-art models and applying tumor- and lesion-specific processing before and after training. Radiomic features extracted from MRI help detect tumor subtype, ensuring a more balanced training. Custom lesion-level performance metrics determine the influence of each model in the ensemble and optimize post-processing that further refines the predictions, enabling the workflow to tailor every step to each case. On the BraTS testing sets, our pipeline achieved performance comparable to top-ranked algorithms across multiple challenges. These findings confirm that custom lesion-aware processing and model selection yield robust segmentations yet without locking the method to a specific network architecture. Our method has the potential for quantitative tumor measurement in clinical practice, supporting diagnosis and prognosis.
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