COVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural Network Solution

Abstract

A novel coronavirus disease 2019 (COVID-19) was detected and has spread rapidly across various countries around the world since the end of the year 2019, Computed Tomography (CT) images have been used as a crucial alternative to the time-consuming RT-PCR test. However, pure manual segmentation of CT images faces a serious challenge with the increase of suspected cases, resulting in urgent requirements for accurate and automatic segmentation of COVID-19 infections. Unfortunately, since the imaging characteristics of the COVID-19 infection are diverse and similar to the backgrounds, existing medical image segmentation methods cannot achieve satisfactory performance. In this work, we try to establish a new deep convolutional neural network tailored for segmenting the chest CT images with COVID-19 infections. We firstly maintain a large and new chest CT image dataset consisting of 165,667 annotated chest CT images from 861 patients with confirmed COVID-19. Inspired by the observation that the boundary of the infected lung can be enhanced by adjusting the global intensity, in the proposed deep CNN, we introduce a feature variation block which adaptively adjusts the global properties of the features for segmenting COVID-19 infection. The proposed FV block can enhance the capability of feature representation effectively and adaptively for diverse cases. We fuse features at different scales by proposing Progressive Atrous Spatial Pyramid Pooling to handle the sophisticated infection areas with diverse appearance and shapes. We conducted experiments on the data collected in China and Germany and show that the proposed deep CNN can produce impressive performance effectively.

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• Multi-stage transfer learning for lung segmentation using portable X-ray devices for patients with COVID-19
• COVID-19 Detection using Transfer Learning with Convolutional Neural Network
• CHS-Net: A Deep learning approach for hierarchical segmentation of COVID-19 infected CT images
• A Weakly Supervised Region-Based Active Learning Method for COVID-19 Segmentation in CT Images
• Intra-model Variability in COVID-19 Classification Using Chest X-ray Images
• Does Non-COVID19 Lung Lesion Help? Investigating Transferability in COVID-19 CT Image Segmentation
• COVID-Net CT-S: 3D Convolutional Neural Network Architectures for COVID-19 Severity Assessment using Chest CT Images
• Lung infection and normal region segmentation from CT volumes of COVID-19 cases
• Deep learning to estimate the physical proportion of infected region of lung for COVID-19 pneumonia with CT image set
• Automated lung segmentation from CT images of normal and COVID-19 pneumonia patients
• Comprehensive Comparison of Deep Learning Models for Lung and COVID-19 Lesion Segmentation in CT scans
• COVID-19 Infection Segmentation from Chest CT Images Based on Scale Uncertainty
• CovSegNet: A Multi Encoder-Decoder Architecture for Improved Lesion Segmentation of COVID-19 Chest CT Scans
• Development and evaluation of a 3D annotation software for interactive COVID-19 lesion segmentation in chest CT
• Segmentation of Lungs COVID Infected Regions by Attention Mechanism and Synthetic Data