An Encoder–Decoder-Based Method for Segmentation of COVID-19 Lung Infection in CT Images

Abstract

The novelty of the COVID-19 Disease and the speed of spread, created colossal chaotic, impulse all the worldwide researchers to exploit all resources and capabilities to understand and analyze characteristics of the coronavirus in terms of spread ways and virus incubation time. For that, the existing medical features such as CT-scan and X-ray images are used. For example, CT-scan images can be used for the detection of lung infection. However, the quality of these images and infection characteristics limit the effectiveness of these features. Using artificial intelligence (AI) tools and computer vision algorithms, the accuracy of detection can be more accurate and can help to overcome these issues. In this paper, we propose a multi-task deep-learning-based method for lung infection segmentation on CT-scan images. Our proposed method starts by segmenting the lung regions that may be infected. Then, segmenting the infections in these regions. In addition, to perform a multi-class segmentation the proposed model is trained using the two-stream inputs. The multi-task learning used in this paper allows us to overcome the shortage of labeled data. In addition, the multi-input stream allows the model to learn from many features that can improve the results. To evaluate the proposed method, many metrics have been used including Sorensen–Dice similarity, Sensitivity, Specificity, Precision, and MAE metrics. As a result of experiments, the proposed method can segment lung infections with high performance even with the shortage of data and labeled images. In addition, comparing with the state-of-the-art method our method achieves good performance results. For example, the proposed method reached 78..6% for Dice, 71.1% for Sensitivity metric, 99.3% for Specificity 85.6% for Precision, and 0.062 for Mean Average Error metric, which demonstrates the effectiveness of the proposed method for lung infection segmentation.

COLI‐Net: Deep learning‐assisted fully automated COVID‐19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images

“…The results were evaluated on F I G U R E 7 Mean relative error of different first-order and shape radiomic features for different datasets in lung and infection regions 427 slices achieving a Dice coefficient of 0.83. Elharrouss et al 59 adopted an encoder-decoder for infectious lesions segmentation using 20 clinical studies from the Italian Society of Medical and Interventional Radiology to report a Dice coefficient of 0.786. They compared the results with U-Net (Dice: 0.439), 60 Attention-UNet (Dice: 0.583), 61 Gated-UNet (Dice: 0.623), 62 Dense-UNet (Dice: 0.515), 63 U-Net++ (Dice: 0.422), 57 and Inf-Net (Dice: 0.739).…”

EffViT-COVID: A dual-path network for COVID-19 percentage estimation

“…This section entails providing an in-depth explanation of the state-of-the-art research utilizing CT scan images and deep learning for COVID detection. At a broader level, these research studies can be classified into two types (a) COVID Detection ( Bougourzi, Contino et al, 2021 , Lassau et al, 2021 , Li et al, 2021 , Song et al, 2021 , Ye et al, 2022 ) (b) COVID Segmentation ( Amara et al, 2022 , Bose et al, 2022 , Elharrouss et al, 2022 , Fan et al, 2020 , Hu et al, 2022 , Stefano and Comelli, 2021 ). The research studies falling into the aforementioned categories are explained in the subsequent paragraph.…”

A Review on Deep Learning based diagnosis of COVID-19 from X-ray and CT Images

“…Hasan et al [25] Elharrouss et al [29] proposed encoder-decoder architecture for segmenting out COVID region in the CT scans. They utilized two encoder-decoder network.…”