Lung segmentation and automatic detection of COVID-19 using radiomic features from chest CT images

Abstract

This paper aims to develop an automatic method to segment pulmonary parenchyma in chest CT images and analyze texture features from the segmented pulmonary parenchyma regions to assist radiologists in COVID-19 diagnosis. A new segmentation method, which integrates a three-dimensional (3D) V-Net with a shape deformation module implemented using a spatial transform network (STN), was proposed to segment pulmonary parenchyma in chest CT images. The 3D V-Net was adopted to perform an end-to-end lung extraction while the deformation module was utilized to restrict the V-Net output according to the prior shape knowledge. The proposed segmentation method was validated against the manual annotation generated by experienced operators. The radiomic features measured from our segmentation results were further analyzed by sophisticated statistical models with high interpretability to discover significant independent features and detect COVID-19 infection. Experimental results demonstrated that compared with the manual annotation, the proposed segmentation method achieved a Dice similarity coefficient of 0.9796, a sensitivity of 0.9840, a specificity of 0.9954, and a mean surface distance error of 0.0318 mm. Furthermore, our COVID-19 classification model achieved an area under curve (AUC) of 0.9470, a sensitivity of 0.9500, and a specificity of 0.9270 when discriminating lung infection with COVID-19 from community-acquired pneumonia and healthy controls using statistically significant radiomic features. The significant features measured from our segmentation results agreed well with those from the manual annotation. Our approach has great promise for clinical use in facilitating automatic diagnosis of COVID-19 infection on chest CT images.

AI-Based human audio processing for COVID-19: A comprehensive overview

“…It would be interesting to evaluate whether multi-modal analysis helps improving the accuracy of C19 detection: Image analysis is providing novel solutions using X-ray [50] , [51] , [52] , [53] , [54] , [55] and chest CT images [56] , [57] , [58] , [59] , [60] , [61] , [62] , [63] . Some of them [50] , [52] , [55] , [58] , [60] , [64] have discriminated C19 from another pulmonary disorder (pneumonia). Hryniewska et al.…”

Fully automatic pipeline of convolutional neural networks and capsule networks to distinguish COVID-19 from community-acquired pneumonia via CT images

“…Deep learning networks have been used to segment the lung field in a preprocessing procedure before the prediction, and high accuracies have been achieved [ 59 , 60 ]. In our study, the lung segmentation module improved the classification performance for COVID-19 and CAP.…”

Enhanced lung image segmentation using deep learning

“…Dice similarity coefficients of 89.42% are achieved on ILD database MedGIFT. Chen Zhou et al [ 40 ] developed an automatic segmentation model by integrating (3D) V-Net and spatial transform network (STN) to segment pulmonary parenchyma in CT images and analyze texture and features from the segmented pulmonary parenchyma regions to assist the radiologist in COVID-19 diagnosis. Mizuho Nishio et al [ 41 ] used U-Net architecture optimized via Bayesian optimization on Japanese and Montgomery and obtained DSC of 0.976 and 0.973 on respective datasets.…”