Bifurcated Autoencoder for Segmentation of COVID-19 Infected Regions in CT Images

TL;DR

This paper introduces a bifurcated autoencoder model that improves segmentation of COVID-19 infected lung regions in CT images, aiding faster and more accurate diagnosis despite data limitations.

Contribution

A novel bifurcated autoencoder architecture with shared encoder and two decoders for separate segmentation of healthy and infected lung regions.

Findings

Outperforms state-of-the-art segmentation methods

Effective with limited training data

Accurately distinguishes infected from healthy lung tissue

Abstract

The new coronavirus infection has shocked the world since early 2020 with its aggressive outbreak. Rapid detection of the disease saves lives, and relying on medical imaging (Computed Tomography and X-ray) to detect infected lungs has shown to be effective. Deep learning and convolutional neural networks have been used for image analysis in this context. However, accurate identification of infected regions has proven challenging for two main reasons. Firstly, the characteristics of infected areas differ in different images. Secondly, insufficient training data makes it challenging to train various machine learning algorithms, including deep-learning models. This paper proposes an approach to segment lung regions infected by COVID-19 to help cardiologists diagnose the disease more accurately, faster, and more manageable. We propose a bifurcated 2-D model for two types of segmentation. This model uses a shared encoder and a bifurcated connection to two separate decoders. One decoder is for segmentation of the healthy region of the lungs, while the other is for the segmentation of the infected regions. Experiments on publically available images show that the bifurcated structure segments infected regions of the lungs better than state of the art.