Tubule segmentation of fluorescence microscopy images based on convolutional neural networks with inhomogeneity correction

TL;DR

This paper introduces a convolutional neural network-based segmentation method for tubular structures in fluorescence microscopy images, incorporating inhomogeneity correction and data augmentation to improve accuracy over existing techniques.

Contribution

The paper presents a novel CNN-based segmentation approach that effectively handles inhomogeneity and enhances tubular structure detection in microscopy images.

Findings

Outperforms existing segmentation methods in accuracy

Successfully segments multiple tubular structures

Demonstrates robustness with inhomogeneity correction

Abstract

Fluorescence microscopy has become a widely used tool for studying various biological structures of in vivo tissue or cells. However, quantitative analysis of these biological structures remains a challenge due to their complexity which is exacerbated by distortions caused by lens aberrations and light scattering. Moreover, manual quantification of such image volumes is an intractable and error-prone process, making the need for automated image analysis methods crucial. This paper describes a segmentation method for tubular structures in fluorescence microscopy images using convolutional neural networks with data augmentation and inhomogeneity correction. The segmentation results of the proposed method are visually and numerically compared with other microscopy segmentation methods. Experimental results indicate that the proposed method has better performance with correctly segmenting and identifying multiple tubular structures compared to other methods.