Routine computed tomography (CT) scans often detect a wide range of renal cysts, some of which may be malignant. Early and precise localization of these cysts can significantly aid quantitative image analysis. Current segmentation methods, however, do not offer sufficient interpretability at the feature and pixel levels, emphasizing the necessity for an explainable framework that can detect and rectify model inaccuracies. We developed an interpretable segmentation framework and validated it on a multi-centric dataset. A Variational Autoencoder Generative Adversarial Network (VAE-GAN) was employed to learn the latent representation of 3D input patches and reconstruct input images. Modifications in the latent representation using the gradient of the segmentation model generated counterfactual explanations for varying dice similarity coefficients (DSC). Radiomics features extracted from these counterfactual images, using a ground truth cyst mask, were analyzed to determine their correlation with segmentation performance. The DSCs for the original and VAE-GAN reconstructed images for counterfactual image generation showed no significant differences. Counterfactual explanations highlighted how variations in cyst image features influence segmentation outcomes and showed model discrepancies. Radiomics features correlating positively and negatively with dice scores were identified. The uncertainty of the predicted segmentation masks was estimated using posterior sampling of the weight space. The combination of counterfactual explanations and uncertainty maps provided a deeper understanding of the image features within the segmented renal cysts that lead to high uncertainty. The proposed segmentation framework not only achieved high segmentation accuracy but also increased interpretability regarding how image features impact segmentation performance.

翻译：常规计算机断层扫描（CT）常检测到多种肾囊肿，其中部分可能为恶性。对这些囊肿进行早期精确定位可显著助力定量图像分析。然而，现有分割方法在特征与像素层面未能提供足够的可解释性，这凸显了构建能够检测并修正模型误差的可解释框架的必要性。我们开发了一种可解释分割框架，并在多中心数据集上进行了验证。采用变分自编码生成对抗网络（VAE-GAN）学习三维输入图像块的潜在表示并重建输入图像。利用分割模型梯度对潜在表示进行修改，为不同的戴斯相似系数（DSC）生成反事实解释。通过基于真实囊肿掩模从反事实图像中提取的影像组学特征，分析其与分割性能的相关性。原始图像与用于生成反事实图像的VAE-GAN重建图像之间的DSC未呈现显著差异。反事实解释揭示了囊肿图像特征变化如何影响分割结果，并显示了模型差异。研究识别了与戴斯分数呈正相关及负相关的影像组学特征。通过权重空间的后验采样估算了预测分割掩模的不确定性。反事实解释与不确定性图谱的结合，深化了对分割肾囊肿内导致高不确定性的图像特征的理解。所提出的分割框架不仅实现了高分割精度，同时增强了关于图像特征如何影响分割性能的可解释性。