The Coupled TuFF-BFF Algorithm for Automatic 3D Segmentation of Microglia

TL;DR

This paper introduces an automatic 3D segmentation algorithm for microglia in microscopy images, effectively capturing fine structures and outperforming existing methods in accuracy and detail.

Contribution

The coupled TuFF-BFF algorithm is a novel method that improves 3D microglia segmentation by evolving level sets with directional tubularity and blobness measures.

Findings

20% performance increase over state-of-the-art methods

40% improvement in ramification index accuracy

Effective segmentation in noisy and heterogeneous images

Abstract

We propose an automatic 3D segmentation algorithm for multiphoton microscopy images of microglia. Our method is capable of segmenting tubular and blob-like structures from noisy images. Current segmentation techniques and software fail to capture the fine processes and soma of the microglia cells, useful for the study of the microglia role in the brain during healthy and diseased states. Our coupled tubularity flow field (TuFF)-blob flow field (BFF) method evolves a level set toward the object boundary using the directional tubularity and blobness measure of 3D images. Our method found a 20% performance increase against state of the art segmentation methods on a dataset of 3D images of microglia even in images with intensity heterogeneity throughout the object. The coupled TuFF-BFF segmentation results also yielded 40% improvement in accuracy for the ramification index of the processes, which displays the efficacy of our method.