COVSeg-NET: A deep convolution neural network for COVID-19 lung CT image segmentation

LDANet: Automatic lung parenchyma segmentation from CT images

Automatic segmentation of the lung parenchyma from computed tomography (CT) images is helpful for the subsequent diagnosis and treatment of patients. In this paper, based on a deep learning algorithm, a lung dense attention network (LDANet) is proposed with two mechanisms: residual spatial attention (RSA) and gated channel attention (GCA). RSA is utilized to weight the spatial information of the lung parenchyma and suppress feature activation in irrelevant regions, while the weights of each channel are adaptively calibrated using GCA to implicitly predict potential key features. Then, a dual attention guidance module (DAGM) is designed to maximize the integration of the advantages of both mechanisms. In addition, LDANet introduces a lightweight dense block (LDB) that reuses feature information and a positioned transpose block (PTB) that realizes accurate positioning and gradually restores the image resolution until the predicted segmentation map is generated. Experiments are conducted on two public datasets, LIDC-IDRI and COVID-19 CT Segmentation, on which LDANet achieves Dice similarity coefficient values of 0.98430 and 0.98319, respectively, outperforming a state-of-the-art lung segmentation model. Additionally, the effectiveness of the main components of LDANet is demonstrated through ablation experiments.

COVID-19 lung infection segmentation from chest CT images based on CAPA-ResUNet

Coronavirus disease 2019 (COVID-19) epidemic has devastating effects on personal health around the world. It is significant to achieve accurate segmentation of pulmonary infection regions, which is an early indicator of disease. To solve this problem, a deep learning model, namely, the content-aware pre-activated residual UNet (CAPA-ResUNet), was proposed for segmenting COVID-19 lesions from CT slices. In this network, the pre-activated residual block was used for down-sampling to solve the problems of complex foreground and large fluctuations of distribution in datasets during training and to avoid gradient disappearance. The area loss function based on the false segmentation regions was proposed to solve the problem of fuzzy boundary of the lesion area. This model was evaluated by the public dataset (COVID-19 Lung CT Lesion Segmentation Challenge-2020) and compared its performance with those of classical models. Our method gains an advantage over other models in multiple metrics. Such as the Dice coefficient, specificity (Spe), and intersection over union (IoU), our CAPA-ResUNet obtained 0.775 points, 0.972 points, and 0.646 points, respectively. The Dice coefficient of our model was 2.51% higher than Content-aware residual UNet (CARes-UNet). The code is available at https://github.com/malu108/LungInfectionSeg.

Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment

OBJECTIVE

To report an overview and update on Artificial Intelligence (AI) and COVID-19 using chest Computed Tomography (CT) scan and chest X-ray images (CXR). Machine Learning and Deep Learning Approaches for Diagnosis and Treatment were identified.

METHODS

Several electronic datasets were analyzed. The search covered the years from January 2019 to June 2021. The inclusion criteria were studied evaluating the use of AI methods in COVID-19 disease reporting performance results in terms of accuracy or precision or area under Receiver Operating Characteristic (ROC) curve (AUC).

RESULTS

Twenty-two studies met the inclusion criteria: 13 papers were based on AI in CXR and 10 based on AI in CT. The summarized mean value of the accuracy and precision of CXR in COVID-19 disease were 93.7% ± 10.0% of standard deviation (range 68.4-99.9%) and 95.7% ± 7.1% of standard deviation (range 83.0-100.0%), respectively. The summarized mean value of the accuracy and specificity of CT in COVID-19 disease were 89.1% ± 7.3% of standard deviation (range 78.0-99.9%) and 94.5 ± 6.4% of standard deviation (range 86.0-100.0%), respectively. No statistically significant difference in summarized accuracy mean value between CXR and CT was observed using the Chi square test (p value > 0.05).

CONCLUSIONS

Summarized accuracy of the selected papers is high but there was an important variability; however, less in CT studies compared to CXR studies. Nonetheless, AI approaches could be used in the identification of disease clusters, monitoring of cases, prediction of the future outbreaks, mortality risk, COVID-19 diagnosis, and disease management.