Counterfactuals and Uncertainty-Based Explainable Paradigm for the Automated Detection and Segmentation of Renal Cysts in Computed Tomography Images: A Multi-Center Study

TL;DR

This study introduces an explainable segmentation framework for renal cyst detection in CT images, combining counterfactual explanations and uncertainty estimation to improve interpretability and accuracy across multiple centers.

Contribution

It presents a novel, interpretable segmentation method using VAE-GANs and counterfactuals, validated on multi-center data, enhancing understanding of model decisions and uncertainties.

Findings

High segmentation accuracy achieved across datasets

Counterfactuals reveal feature influence on segmentation performance

Uncertainty maps identify regions with low model confidence

Abstract

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.