无需标注即可估算医学图像分割模型性能，提升临床落地可靠性。
Performance Estimation for Supervised Medical Image Segmentation Models on Unlabeled Data Using UniverSeg
- 基于未标注数据构建性能评估框架，适配多种模型与指标
- 在6个数据集上实现0.956相关性与0.025平均绝对误差
- 可无缝集成训练流程，适合临床部署前的快速验证
医学图像分割模型的性能通常通过Dice分数和Hausdorff距离等指标，对比预测掩码与真实标注来评估。但在实际临床应用中，对所有未见数据进行标注往往不现实，导致模型性能难以确定。为此，我们提出分割性能评估器（SPE），一种在无标注数据上估计分割模型性能的框架。该框架适用于多种评估指标与模型架构。在六个公开数据集上，涵盖像素级指标（如Dice分数）与距离级指标（如HD95），实验验证了其通用性与有效性：在独立测试集上与真实Dice分数的相关性达0.956±0.046，平均绝对误差仅为0.025±0.019。结果表明，该方法能在无需标注的情况下可靠估计模型性能。SPE框架可无缝嵌入任意模型训练流程，不增加训练开销，支持性能预估，促进医学图像分割算法在真实场景中的应用。源代码已公开。
原文摘要 · Abstract (English)
The performance of medical image segmentation models is usually evaluated using metrics like the Dice score and Hausdorff distance, which compare predicted masks to ground truth annotations. However, when applying the model to unseen data, such as in clinical settings, it is often impractical to annotate all the data, making the model's performance uncertain. To address this challenge, we propose the Segmentation Performance Evaluator (SPE), a framework for estimating segmentation models' performance on unlabeled data. This framework is adaptable to various evaluation metrics and model architectures. Experiments on six publicly available datasets across six evaluation metrics including pixel-based metrics such as Dice score and distance-based metrics like HD95, demonstrated the versatility and effectiveness of our approach, achieving a high correlation (0.956$\pm$0.046) and low MAE (0.025$\pm$0.019) compare with real Dice score on the independent test set. These results highlight its ability to reliably estimate model performance without requiring annotations. The SPE framework integrates seamlessly into any model training process without adding training overhead, enabling performance estimation and facilitating the real-world application of medical image segmentation algorithms. The source code is publicly available
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