标题： 基于深度学习贝叶斯建模的不确定性感知分割质量预测：皮肤癌和肝脏分割的全面评估与解释 显示英文标题

标题： Uncertainty-Aware Segmentation Quality Prediction via Deep Learning Bayesian Modeling: Comprehensive Evaluation and Interpretation on Skin Cancer and Liver Segmentation

摘要： 图像分割是计算生物医学图像分析中的关键步骤，通常在训练和验证过程中使用Dice系数等指标进行评估。然而，在没有手动标注的临床环境中，评估分割质量变得具有挑战性，缺乏可靠性指标的模型面临采用障碍。为解决这一差距，我们提出了一种新框架，在测试时无需真实标注即可预测分割质量。我们的方法引入了两个互补框架：一个利用预测的分割和不确定性图，另一个整合原始输入图像、不确定性图和预测的分割图。我们提出了两种基准分割模型——SwinUNet和带有ResNet50的特征金字塔网络的贝叶斯适应版本，使用蒙特卡洛Dropout、集成和测试时间增强来量化不确定性。我们在二维皮肤病变和三维肝脏分割数据集上评估了四种不确定性估计：置信度图、熵、互信息和期望成对Kullback-Leibler散度，并分析了它们与分割质量指标的相关性。我们的框架在HAM10000数据集上达到了93.25的R2分数和96.58的皮尔逊相关性，优于之前的分割质量评估方法。对于三维肝脏分割，使用熵的测试时间增强达到了85.03的R2分数和65.02的皮尔逊相关性，展示了跨模态的鲁棒性。此外，我们提出了一种聚合策略，将多个不确定性估计结合成每张图像的一个分数，提供更稳健和全面的分割质量评估。最后，我们使用Grad-CAM和基于UMAP的嵌入分析来解释模型的行为和可靠性，突出了不确定性集成的影响。 显示英文摘要

摘要： Image segmentation is a critical step in computational biomedical image analysis, typically evaluated using metrics like the Dice coefficient during training and validation. However, in clinical settings without manual annotations, assessing segmentation quality becomes challenging, and models lacking reliability indicators face adoption barriers. To address this gap, we propose a novel framework for predicting segmentation quality without requiring ground truth annotations during test time. Our approach introduces two complementary frameworks: one leveraging predicted segmentation and uncertainty maps, and another integrating the original input image, uncertainty maps, and predicted segmentation maps. We present Bayesian adaptations of two benchmark segmentation models-SwinUNet and Feature Pyramid Network with ResNet50-using Monte Carlo Dropout, Ensemble, and Test Time Augmentation to quantify uncertainty. We evaluate four uncertainty estimates: confidence map, entropy, mutual information, and expected pairwise Kullback-Leibler divergence on 2D skin lesion and 3D liver segmentation datasets, analyzing their correlation with segmentation quality metrics. Our framework achieves an R2 score of 93.25 and Pearson correlation of 96.58 on the HAM10000 dataset, outperforming previous segmentation quality assessment methods. For 3D liver segmentation, Test Time Augmentation with entropy achieves an R2 score of 85.03 and a Pearson correlation of 65.02, demonstrating cross-modality robustness. Additionally, we propose an aggregation strategy that combines multiple uncertainty estimates into a single score per image, offering a more robust and comprehensive assessment of segmentation quality. Finally, we use Grad-CAM and UMAP-based embedding analysis to interpret the model's behavior and reliability, highlighting the impact of uncertainty integration.