Quest for a clinically relevant medical image segmentation metric: the definition and implementation of Medical Similarity Index

TL;DR

This paper introduces the Medical Similarity Index, a new clinically relevant metric for evaluating medical image segmentation, along with an open-source pipeline adaptable to various applications and datasets.

Contribution

The study develops and implements a novel segmentation metric that better reflects clinical relevance, with an accessible Python pipeline and demonstrated adaptability to different datasets.

Findings

Created a sustainable Python-based image processing pipeline.

Developed a new segmentation evaluation metric suitable for clinical applications.

Demonstrated the metric's adaptability with prostate segmentation data.

Abstract

Background: In the field of radiology and radiotherapy, accurate delineation of tissues and organs plays a crucial role in both diagnostics and therapeutics. While the gold standard remains expert-driven manual segmentation, many automatic segmentation methods are emerging. The evaluation of these methods primarily relies on traditional metrics that only incorporate geometrical properties and fail to adapt to various applications. Aims: This study aims to develop and implement a clinically relevant segmentation metric that can be adapted for use in various medical imaging applications. Methods: Bidirectional local distance was defined, and the points of the test contour were paired with points of the reference contour. After correcting for the distance between the test and reference center of mass, Euclidean distance was calculated between the paired points, and a score was given to each test point. The overall medical similarity index was calculated as the average score across all the test points. For demonstration, we used myoma and prostate datasets; nnUNet neural networks were trained for segmentation. Results: An easy-to-use, sustainable image processing pipeline was created using Python. The code is available in a public GitHub repository along with Google Colaboratory notebooks. The algorithm can handle multislice images with multiple masks per slice. Mask splitting algorithm is also provided that can separate the concave masks. We demonstrate the adaptability with prostate segmentation evaluation. Conclusions: A novel segmentation evaluation metric was implemented, and an open-access image processing pipeline was also provided, which can be easily used for automatic measurement of clinical relevance of medical image segmentation.}