Bifurcated Autoencoder for Segmentation of COVID-19 Infected Regions in CT Images
This work addresses the challenge of rapid and accurate diagnosis of COVID-19 for cardiologists, but it is incremental as it builds on existing deep learning approaches for medical image segmentation.
The paper tackles the problem of segmenting COVID-19 infected regions in CT images by proposing a bifurcated autoencoder model with a shared encoder and two separate decoders for healthy and infected regions, achieving better segmentation than state-of-the-art methods on publicly available images.
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.