[论文解读] The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

Ahmed W. Moawad, Anastasia Janas+230 more|arXiv (Cornell University)|Jun 1, 2023

Brain Tumor Detection and Classification被引用 25

一句话总结

一个多机构的 BraTS-METS 2023 挑战，在多组学前治疗MRI上评估脑转移瘤分割，覆盖八个数据集，公开 402 份标注研究，并报告以病灶为单位的性能，对 FP/FN 进行惩罚以对比方法。

ABSTRACT

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space.The BraTS-METS 2023 challenge successfully curated well-annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied clinical environments and providing personalized volumetric reports to patients undergoing BM treatment.

研究动机与目标

• 推动并基准基于 AI 的前治疗MRI脑转移瘤分割，使用多样化、标注完备、真实世界的数据集。

提出的方法

• 在标准MRI序列上对未治疗的脑转移瘤的八个国际数据集进行了分步标注，过程包括基于 UNET 的提案、学生标注者、神经放射科医生以及神经放射科医生的最终批准。

实验结果

研究问题

• RQ1 在多样化数据集上，对未治疗脑转移瘤的病灶级分割方法的性能如何？
• RQ2 假阳性和假阴性如何影响分割算法的评分和排名？
• RQ3 在此任务中，常见错误（例如对小病灶的漏检、掩模错配）对最先进方法的限制是什么？
• RQ4 经过整理的、公开可获取的数据集如何促进脑转移瘤分割在不同临床环境中的转化？

主要发现

• 八个数据集共包含 1303 份研究被标注；402 份研究（3076 个病灶）公开发布用于挑战。
• 31 份研究（139 个病灶）被保留用于验证，59 份研究（218 个病灶）用于测试。
• 按受试者对病灶级 Dice 和 HD95 进行排名，并对 FP/FN 进行惩罚（Dice 为 0；HD95 为 374）。
• 获胜团队的 LesionWise 平均分为 7.9。
• 前列队伍的常见错误包括对小病灶的假阴性和掩模错配。