Semi-supervised COVID-19 CT image segmentation using deep generative models

Abstract

Background A recurring problem in image segmentation is a lack of labelled data. This problem is especially acute in the segmentation of lung computed tomography (CT) of patients with Coronavirus Disease 2019 (COVID-19). The reason for this is simple: the disease has not been prevalent long enough to generate a great number of labels. Semi-supervised learning promises a way to learn from data that is unlabelled and has seen tremendous advancements in recent years. However, due to the complexity of its label space, those advancements cannot be applied to image segmentation. That being said, it is this same complexity that makes it extremely expensive to obtain pixel-level labels, making semi-supervised learning all the more appealing. This study seeks to bridge this gap by proposing a novel model that utilizes the image segmentation abilities of deep convolution networks and the semi-supervised learning abilities of generative models for chest CT images of patients with the COVID-19. Results We propose a novel generative model called the shared variational autoencoder (SVAE). The SVAE utilizes a five-layer deep hierarchy of latent variables and deep convolutional mappings between them, resulting in a generative model that is well suited for lung CT images. Then, we add a novel component to the final layer of the SVAE which forces the model to reconstruct the input image using a segmentation that must match the ground truth segmentation whenever it is present. We name this final model StitchNet. Conclusion We compare StitchNet to other image segmentation models on a high-quality dataset of CT images from COVID-19 patients. We show that our model has comparable performance to the other segmentation models. We also explore the potential limitations and advantages in our proposed algorithm and propose some potential future research directions for this challenging issue.

COVID-19 detection from chest X-ray images using CLAHE-YCrCb, LBP, and machine learning algorithms

“…Whereas, MobilNetV2 generated an overall accuracy of 81% on the second dataset. Zammit et al [ 7 ] developed a generative model (shared variational auto-encoder) using a five-layer deep hierarchy of latent variables and deep convolutional mappings.…”

“…To overcome RT-PCR limitations, researchers suggested deep learning, machine learning, and transfer learning models [ 7 – 9 ]. Nevertheless, deep learning and machine learning models [ 10 , 11 ] exhibit notable limitations, such as the need for large datasets to train, expensive computational resources graphical processing unit (GPUs), more extensive trainable parameters, feature vector size, longer running, training, and testing time.…”