arXiv 2410.186842024-10-24cs.CV

每个组件都很重要：重新思考多实例分割任务中医学语义分割的成功衡量标准

Every Component Counts: Rethinking the Measure of Success for Medical Semantic Segmentation in Multi-Instance Segmentation Tasks

Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；HIDSS4Health - Helmholtz Information and Data Science School for Health（HIDSS4Health——亥姆霍兹健康信息与数据科学学院）；University Hospital Essen（埃森大学医院）；University of Duisburg-Essen（杜伊斯堡-埃森大学）；German Cancer Consortium (DKTK)（德国癌症联盟（DKTK））

Alexander Jaus, Constantin Seibold, Simon Reiß, Zdravko Marinov, Keyi Li, Zeling Ye, Stefan Krieg, Jens Kleesiek, Rainer Stiefelhagen

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AI总结：

本文提出 CC-Metrics 评估协议，通过逐连通组件匹配与局部计算分割指标，消除大病灶对 Dice、Hausdorff 等指标的偏差，使多实例医学分割评估更符合临床价值。

摘要：

我们提出了 Connected-Component（CC）-Metrics，这是一种新颖的语义分割评估协议，旨在使现有语义分割指标与每个连通组件都至关重要的多实例检测场景保持一致。我们在全身 PET/CT 中语义转移灶分割这一常见医学场景中推动了这一设置。我们展示了现有语义分割指标如何偏向较大的连通组件，这与扫描结果的临床评估相矛盾，因为在临床评估中，肿瘤大小与临床相关性并不相关。为了重新平衡现有的分割指标，我们建议在每个组件的基础上对其进行评估，从而赋予每个肿瘤相同的权重，而不考虑其大小。为了将预测结果与真实分段相匹配，我们采用了基于邻近度的匹配标准，在感兴趣的组件局部评估常用指标。通过这种方法，我们摆脱了针对 Dice 或 Surface Dice 等基于重叠的指标由大转移灶引入的偏差。CC-Metrics 还改进了 Hausdorff Distances 等基于距离的指标，这些指标对于不影响最大值或第 95 百分位数的微小变化无法提供有效信息，同时避免了像 Panoptic Quality 中那样直接将基于计数的指标与基于重叠的指标相结合所带来的隐患。

英文摘要：

We present Connected-Component~(CC)-Metrics, a novel semantic segmentation evaluation protocol, targeted to align existing semantic segmentation metrics to a multi-instance detection scenario in which each connected component matters. We motivate this setup in the common medical scenario of semantic metastases segmentation in a full-body PET/CT. We show how existing semantic segmentation metrics suffer from a bias towards larger connected components contradicting the clinical assessment of scans in which tumor size and clinical relevance are uncorrelated. To rebalance existing segmentation metrics, we propose to evaluate them on a per-component basis thus giving each tumor the same weight irrespective of its size. To match predictions to ground-truth segments, we employ a proximity-based matching criterion, evaluating common metrics locally at the component of interest. Using this approach, we break free of biases introduced by large metastasis for overlap-based metrics such as Dice or Surface Dice. CC-Metrics also improves distance-based metrics such as Hausdorff Distances which are uninformative for small changes that do not influence the maximum or 95th percentile, and avoids pitfalls introduced by directly combining counting-based metrics with overlap-based metrics as it is done in Panoptic Quality.

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