A Novel Distance-Based Metric for Quality Assessment in Image Segmentation
This addresses the need for interpretable and comparable quality metrics in image segmentation, which is incremental as it builds on existing distance-based approaches.
The paper tackles the problem of assessing segmentation quality by introducing the Surface Consistency Coefficient (SCC), a novel distance-based metric that quantifies spatial error distribution near structure surfaces, demonstrating its robustness and effectiveness through analysis on synthetic and real data.
The assessment of segmentation quality plays a fundamental role in the development, optimization, and comparison of segmentation methods which are used in a wide range of applications. With few exceptions, quality assessment is performed using traditional metrics, which are based on counting the number of erroneous pixels but do not capture the spatial distribution of errors. Established distance-based metrics such as the average Hausdorff distance are difficult to interpret and compare for different methods and datasets. In this paper, we introduce the Surface Consistency Coefficient (SCC), a novel distance-based quality metric that quantifies the spatial distribution of errors based on their proximity to the surface of the structure. Through a rigorous analysis using synthetic data and real segmentation results, we demonstrate the robustness and effectiveness of SCC in distinguishing errors near the surface from those further away. At the same time, SCC is easy to interpret and comparable across different structural contexts.