[论文解读] Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge

Dominic LaBella, Ujjwal Baid+120 more|arXiv (Cornell University)|May 16, 2024

Meningioma and schwannoma managementMedicine被引用 3

一句话总结

本文分析了BraTS 2023年颅内脑膜瘤分割挑战赛，评估了深度学习模型在术前MRI上分割脑膜瘤的性能。研究发现，颅骨剥离是导致肿瘤体积丢失的主要问题，并建议采用MRI_reface等替代匿名化技术，以在放射治疗计划中保留关键的肿瘤区域。

ABSTRACT

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps.

研究动机与目标

• 评估深度学习模型在BraTS 2023数据集上分割颅内脑膜瘤的性能。
• 研究颅骨剥离预处理对脑膜瘤分割精度的影响。
• 识别当前分割任务在临床相关性方面的缺口，特别是与放射治疗计划中肿瘤体积包含的相关性。
• 推荐能够保留肿瘤体积同时确保患者匿名化的改进型预处理技术。
• 使自动化分割与临床指南（如RTOG 0539）保持一致，以定义脑膜瘤放射治疗中的靶区体积。

提出的方法

• 使用了包含1,424例病例的BraTS 2023术前脑膜瘤数据集，其多对比MRI扫描包括T1、T1-CE、T2和FLAIR序列。
• 在训练集（70%）、验证集（10%）和私有测试集（20%）的划分上，使用标准指标评估分割性能。
• 通过测量与颅骨边界相邻的体素，分析颅骨剥离对肿瘤体积的影响。
• 提出替代匿名化技术，如MRI_reface，通过修改面部和耳部区域来保留脑组织。
• 通过与RTOG 0539指南中靶区体积定义的对比，评估分割输出的临床相关性。
• 评估分割标签中是否包含非增强区域（如T2/FLAIR高信号、硬脑膜尾征）与临床实践的一致性。

Figure 1: Meningioma sub-compartments considered in the BraTS Pre-operative Meningioma Dataset. Image panels A-C denote the different tumor sub-compartments included in manual annotations; (A) enhancing tumor (blue) visible on a T1-weighted post-contrast image; (B) the non-enhancing tumor core (red)

实验结果

研究问题

• RQ1 在预处理过程中，颅骨剥离在多大程度上导致了自动分割中脑膜瘤体积的排除？
• RQ2 当前的分割模型在BraTS 2023脑膜瘤数据集上的表现如何，特别是在保留增强肿瘤和术后切除腔体积方面？
• RQ3 哪种预处理技术最能保留临床相关的肿瘤区域，同时确保患者匿名化？
• RQ4 分割标签与临床放射治疗靶区体积定义（如RTOG 0539）的匹配程度如何？
• RQ5 在数据集整理和标注方面，还需进行哪些改进，以更好地支持放射治疗计划中的分割任务？

主要发现

• 颅骨剥离导致1,483例脑膜瘤病例中的1,286例至少有一个肿瘤体素与去除颅骨后的边界相邻，存在临床相关肿瘤体积丢失的风险。
• 研究发现，非增强的T2/FLAIR高信号和线性硬脑膜尾增强常被包含在分割标签中，但在放射治疗计划中不应属于大体肿瘤体积（GTV）。
• MRI_reface被识别为颅骨剥离的有前途替代方案，因其在匿名化面部特征的同时，保留了大部分MR图像。
• RTOG 0539指南规定，GTV应仅包含增强肿瘤和术后切除腔，不应包括水肿或硬脑膜尾征，因为这些与复发无关。
• 当前的分割挑战（如BraTS 2023）包含代表非靶区结构的标签（如SNFH），可能使模型与临床实践产生偏差。
• 训练集和验证集数据在Synapse上公开可获取，而测试集保持私密，以确保未来算法评估的无偏性。

Figure 2: Image panels of the top scored individual test case with a median participant DSC of 1.00, 1.00, and 1.00 and average participant DSC of 0.993, 0.881, and 0.882 for enhancing tumor, tumor core, and whole tumor; respectively. Tumor ground truth sub-compartment labels annotated on axial (A),