Segmentation and quantification of COVID-19 infections in CT using pulmonary vessels extraction and deep learning

Abstract

At the end of 2019, the World Health Organization (WHO) reported pneumonia that started in Wuhan, China, as a global emergency problem. Researchers quickly advanced in research to try to understand this COVID-19 and sough solutions for the front-line professionals fighting this fatal disease. One of the tools to aid in the detection, diagnosis, treatment, and prevention of this disease is computed tomography (CT). CT images provide valuable information on how this new disease affects the lungs of patients. However, the analysis of these images is not trivial, especially when researchers are searching for quick solutions. Detecting and evaluating this disease can be tiring, time-consuming, and susceptible to errors. Thus, in this study, we aim to automatically segment infections caused by COVID19 and provide quantitative measures of these infections to specialists, thus serving as a support tool. We use a database of real clinical cases from Pedro Ernesto University Hospital of the State of Rio de Janeiro, Brazil. The method involves five steps: lung segmentation, segmentation and extraction of pulmonary vessels, infection segmentation, infection classification, and infection quantification. For the lung segmentation and infection segmentation tasks, we propose modifications to the traditional U-Net, including batch normalization, leaky ReLU, dropout, and residual block techniques, and name it as Residual U-Net. The proposed method yields an average Dice value of 77.1% and an average specificity of 99.76%. For quantification of infectious findings, the proposed method achieves results like that of specialists, and no measure presented a value of ρ < 0.05 in the paired t-test. The results demonstrate the potential of the proposed method as a tool to help medical professionals combat COVID-19. fight the COVID-19.

Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models

“…Lack of fine-grained pixel-level annotations and provision of patient-level labels (i.e., class labels) that indicate whether the person is infected in the majority of the currently available COVID-19 datasets makes CNN models trained. Despite the establishment of several diagnosis systems for testing suspected COVID-19 cases by CT scan [ 27 ], most of them exhibit two constraints: (1) they are not suitably robust for versatile COVID-19 infections because they are trained on small-scale datasets; (2) they lack explainable transparency in assisting doctors during medical diagnosis because classification is performed on the basis of black-box CNNs.…”

Methods for segmentation of spinal cord and esophagus in radiotherapy planning computed tomography

“…We also propose the use of Atlas to reduce the scope of segmentation and, consequently, the computational load. We propose a new Res-U-Net, composed of residual blocks, dropout, leakyReLU, batch normalization, this network showed promise also reaching expressive results for COVID-19 segmentation [11] and heart [12]. Furthermore, we propose a post-processing step, which sought to reduce the false positives generated.…”

Enhanced lung image segmentation using deep learning

“…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. Ferreira et al [ 42 ] proposed a modified U-Net model for automatic detection of infection caused by COVID-19. Trained and evaluated on the CT database of the actual clinical case from Pedro Ernesto University Hospital of the state of Rio de Janeiro, this model achieved a dice value of 77.1% and an average specificity of 99.76%.…”