Topological data analysis visualization for interpretable assessment of AI contouring quality

Advances in artificial intelligence have increased the availability of auto-segmentation tools. However, conventional accuracy metrics cannot capture regional segmentation differences between AI models or with respect to reference segmentations, necessary to interpret contouring variations. To address this, we developed a novel distance metric based on topological data analysis (TDA) to evaluate 3D point cloud representations of segmentations applied to six organs-at-risk (OARs) and lung gross tumor volume (GTV). A total of 34 CTs and 54 CBCTs were analyzed to compare a bespoke AI segmentation method with reference clinical contours. TDA involved: (1) converting segmentations into 3D point clouds, (2) clustering them into regions via K-means with fixed seeds and cluster numbers determined by the Elbow method, (3) constructing directed graphs for AI and reference clusters using centroids as nodes, and (4) computing distances using unbalanced optimal mass transport. Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95) were also calculated. TDA successfully identified local regions of high deviation in both OARs and GTVs of varying shapes. It correlated positively with HD95 and negatively with DSC based on Pearson’s correlation coefficient. Computation was efficient, averaging 1.72 s, and TDA effectively highlighted regions of greatest mismatch, providing quantitative visualization of poor concordance. In conclusion, we developed a new TDA metric for comparing auto-segmentation of GTV and OARs. Importantly, it allows visualization of mismatching regions thus potentially allowing faster contour editing and evaluation.