Segmentation Evaluation for Medical Image Processing

Abstract

Segmentation is a cornerstone of medical image processing, enabling the identification and delineation of anatomical structures, pathological regions, and treatment targets from imaging modalities such as MRI, CT, ultrasound, and X-ray. The quality of those segmentations can have direct influence on downstream tasks like volumetric analysis, and therapy monitoring.

This talk will review the current approaches used to evaluate segmentation quality in medical imaging, including both overlap and boundary metrics. Common measures such as the Dice similarity coefficient, Jaccard index, precision, recall, Hausdorff distance, and average surface distance are discussed in terms of their strengths, limitations, and suitability for different clinical applications. The presentation also addresses the impact of annotation uncertainty, inter-observer variability, and dataset characteristics on evaluation outcomes.

In addition, we will discuss task-specific requirements with a review of some case studies. This work highlights the need for robust, clinically meaningful, and reproducible evaluation frameworks. The goal is to provide a clear perspective on how segmentation results should be assessed to support reliable medical image analysis and translation into clinical use.