Coronavirus Detection and Analysis on Chest CT with Deep Learning

TL;DR

This paper presents a deep learning pipeline that detects, localizes, and quantifies COVID-19 severity from chest CT scans, aiding radiologists in diagnosis during the pandemic.

Contribution

It introduces a novel deep learning-based algorithm combining lung segmentation, slice classification, and localization for COVID-19 analysis from CT images.

Findings

Effective detection and localization of COVID-19 in chest CT scans.

Unsupervised clustering reveals patterns of disease manifestation.

Results demonstrate potential for clinical support in pandemic response.

Abstract

The outbreak of the novel coronavirus, officially declared a global pandemic, has a severe impact on our daily lives. As of this writing there are approximately 197,188 confirmed cases of which 80,881 are in "Mainland China" with 7,949 deaths, a mortality rate of 3.4%. In order to support radiologists in this overwhelming challenge, we develop a deep learning based algorithm that can detect, localize and quantify severity of COVID-19 manifestation from chest CT scans. The algorithm is comprised of a pipeline of image processing algorithms which includes lung segmentation, 2D slice classification and fine grain localization. In order to further understand the manifestations of the disease, we perform unsupervised clustering of abnormal slices. We present our results on a dataset comprised of 110 confirmed COVID-19 patients from Zhejiang province, China.