  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.
