Novel Evaluation Metrics for Vascular Structure Segmentation

Abstract

For the diagnosis of eye-related diseases segmentation of the retinal vessels and the analysis of the tortuousness, completeness, and thickness of these vessels are the fundamental steps. The assessment of the quality of the retinal vessel segmentation, therefore, plays a crucial role. Conventionally, different evaluation metrics for retinal vessel segmentation have been proposed. Most of them are based on pixel matching. Recently, a novel non-global measure has been introduced. It focuses on the skeletal similarity between vessel segments rather than the pixel-wise overlay and redefines the terms of the confusion matrix. In our work, we re-implement this evaluation algorithm and discover the design flaws in the algorithm. Therefore, we propose modifications to the metric. The basic structure of the algorithm, which combines the thickness and curve similarity is preserved. Meanwhile, the calculation of the curve similarity is modified and extended. Furthermore, our modifications enable us to apply the evaluation metric to three-dimensional data. We show that compared to the conventional pixel matching-based metrics our proposed metric is more representative for cases where vessels are missing, disoriented, or inconsistent in their thickness.