DeepAI AI Chat
Log In Sign Up

Deep learning to estimate the physical proportion of infected region of lung for COVID-19 pneumonia with CT image set

06/09/2020
by   Wei Wu, et al.
26

Utilizing computed tomography (CT) images to quickly estimate the severity of cases with COVID-19 is one of the most straightforward and efficacious methods. Two tasks were studied in this present paper. One was to segment the mask of intact lung in case of pneumonia. Another was to generate the masks of regions infected by COVID-19. The masks of these two parts of images then were converted to corresponding volumes to calculate the physical proportion of infected region of lung. A total of 129 CT image set were herein collected and studied. The intrinsic Hounsfiled value of CT images was firstly utilized to generate the initial dirty version of labeled masks both for intact lung and infected regions. Then, the samples were carefully adjusted and improved by two professional radiologists to generate the final training set and test benchmark. Two deep learning models were evaluated: UNet and 2.5D UNet. For the segment of infected regions, a deep learning based classifier was followed to remove unrelated blur-edged regions that were wrongly segmented out such as air tube and blood vessel tissue etc. For the segmented masks of intact lung and infected regions, the best method could achieve 0.972 and 0.757 measure in mean Dice similarity coefficient on our test benchmark. As the overall proportion of infected region of lung, the final result showed 0.961 (Pearson's correlation coefficient) and 11.7 infected regions of lung could be used as a visual evidence to assist clinical physician to determine the severity of the case. Furthermore, a quantified report of infected regions can help predict the prognosis for COVID-19 cases which were scanned periodically within the treatment cycle.

READ FULL TEXT

page 5

page 6

page 8

page 9

page 11

page 14

page 15

page 16

04/05/2021

Automated lung segmentation from CT images of normal and COVID-19 pneumonia patients

Automated semantic image segmentation is an essential step in quantitati...
09/26/2020

Deep Learning-based Four-region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis

Purpose. Imaging plays an important role in assessing severity of COVID ...
07/05/2022

A Deep Ensemble Learning Approach to Lung CT Segmentation for COVID-19 Severity Assessment

We present a novel deep learning approach to categorical segmentation of...
03/03/2020

Visualizing intestines for diagnostic assistance of ileus based on intestinal region segmentation from 3D CT images

This paper presents a visualization method of intestine (the small and l...
01/23/2022

SpiroMask: Measuring Lung Function Using Consumer-Grade Masks

According to the World Health Organisation (WHO), 235 million people suf...
09/10/2020

Comprehensive Comparison of Deep Learning Models for Lung and COVID-19 Lesion Segmentation in CT scans

Recently there has been an explosion in the use of Deep Learning (DL) me...
11/11/2022

Treatment classification of posterior capsular opacification (PCO) using automated ground truths

Determination of treatment need of posterior capsular opacification (PCO...