Anatomy-Aware Measurement of Segmentation Accuracy

TL;DR

This paper introduces an anatomy-aware approach to measuring segmentation accuracy in medical images, incorporating internal anatomical zones into metrics like Dice and Jaccard to improve relevance and assessment quality.

Contribution

It proposes new anatomy-aware extensions of standard segmentation metrics and demonstrates their impact on accuracy assessment and user ranking using synthetic prostate ultrasound data.

Findings

Anatomy-aware metrics differ from traditional measures in individual assessments.

Incorporating anatomical zones can alter user ranking in segmentation tasks.

The approach enhances the clinical relevance of segmentation accuracy evaluation.

Abstract

Quantifying the accuracy of segmentation and manual delineation of organs, tissue types and tumors in medical images is a necessary measurement that suffers from multiple problems. One major shortcoming of all accuracy measures is that they neglect the anatomical significance or relevance of different zones within a given segment. Hence, existing accuracy metrics measure the overlap of a given segment with a ground-truth without any anatomical discrimination inside the segment. For instance, if we understand the rectal wall or urethral sphincter as anatomical zones, then current accuracy measures ignore their significance when they are applied to assess the quality of the prostate gland segments. In this paper, we propose an anatomy-aware measurement scheme for segmentation accuracy of medical images. The idea is to create a ``master gold'' based on a consensus shape containing not just the outline of the segment but also the outlines of the internal zones if existent or relevant. To apply this new approach to accuracy measurement, we introduce the anatomy-aware extensions of both Dice coefficient and Jaccard index and investigate their effect using 500 synthetic prostate ultrasound images with 20 different segments for each image. We show that through anatomy-sensitive calculation of segmentation accuracy, namely by considering relevant anatomical zones, not only the measurement of individual users can change but also the ranking of users' segmentation skills may require reordering.