SAGE: An Approach and Implementation Empowering Quick and Reliable Quantitative Analysis of Segmentation Quality

Abstract

Finding the outline of an object in an image is a fundamental step in many vision-based applications. It is important to demonstrate that the segmentation found accurately represents the contour of the object in the image. The discrepancy measure model for segmentation analysis focuses on selecting an appropriate discrepancy measure to compute a score that indicates how similar a query segmentation is to a gold standard segmentation. Observing that the score depends on the gold standard segmentation, we propose a framework that expands this approach by introducing the consideration of how to establish the gold standard segmentation. The framework shows how to obtain project-specific performance indicators in a principled way that links annotation tools, fusion methods, and evaluation algorithms into a unified model we call SAGE. We also describe a freely available implementation of SAGE that enables quick segmentation validation against either a single annotation or a fused annotation. Finally, three studies are presented to highlight the impact of annotation tools, an-notators, and fusion methods on establishing trusted gold standard segmentations for cell and artery images.