# Segmentation of Cortical Spreading Depression Wavefronts Through Local Similarity Metric

## TL;DR

This paper introduces a new region-based segmentation method for cortical spreading depression in microscopy images, improving accuracy and robustness against noise and inhomogeneity.

## Contribution

The proposed method uses a local intensity similarity measure with distance maps, achieving higher accuracy and robustness than existing techniques.

## Key findings

- DICE index of 0.9859, 6% higher than previous methods
- 79.9% reduction in root mean square error
- Robust segmentation boundary in noisy, inhomogeneous images

## Abstract

In this paper, we present a novel region-based segmentation method for cortical spreading depressions in 2-photon microscopy images. Fluorescent microscopy has become an important tool in neuroscience, but segmentation approaches are challenged by the opaque properties and structures of brain tissue. These challenges are made more extreme when segmenting events such as cortical spreading depressions, where low signal-to-noise ratios and intensity inhomogeneity dominate images. The method we propose uses a local intensity similarity measure that takes advantage of normalized Euclidean and geodesic distance maps of the image. This method provides a smooth segmentation boundary which is robust to the noise and inhomogeneity within cortical spreading depression images. Experimental results yielded a DICE index of 0.9859, an increase of 6% over the current state-of-the-art, and a reduction of root mean square error by 79.9%.