Efficient COVID-19 Segmentation from CT Slices Exploiting Semantic Segmentation with Integrated Attention Mechanism

Abstract

Coronavirus (COVID-19) is a pandemic, which caused suddenly unexplained pneumonia cases and caused a devastating effect on global public health. Computerized tomography (CT) is one of the most effective tools for COVID-19 screening. Since some specific patterns such as bilateral, peripheral, and basal predominant ground-glass opacity, multifocal patchy consolidation, crazy-paving pattern with a peripheral distribution can be observed in CT images and these patterns have been declared as the findings of COVID-19 infection. For patient monitoring, diagnosis and segmentation of COVID-19, which spreads into the lung, expeditiously and accurately from CT, will provide vital information about the stage of the disease. In this work, we proposed a SegNet-based network using the attention gate (AG) mechanism for the automatic segmentation of COVID-19 regions in CT images. AGs can be easily integrated into standard convolutional neural network (CNN) architectures with a minimum computing load as well as increasing model precision and predictive accuracy. Besides, the success of the proposed network has been evaluated based on dice, Tversky, and focal Tversky loss functions to deal with low sensitivity arising from the small lesions. The experiments were carried out using a fivefold cross-validation technique on a COVID-19 CT segmentation database containing 473 CT images. The obtained sensitivity, specificity, and dice scores were reported as 92.73%, 99.51%, and 89.61%, respectively. The superiority of the proposed method has been highlighted by comparing with the results reported in previous studies and it is thought that it will be an auxiliary tool that accurately detects automatic COVID-19 regions from CT images.

Decoders configurations based on Unet family and feature pyramid network for COVID-19 segmentation on CT images

“… Zhou, Canu & Ruan (2020) proposed an effective model to segment COVID-19 from CT images. In comparison to other existing studies ( Budak et al, 2021 ), the model obtained comparable results. For each CT slice, the proposed method takes 0.29 s to generate the segmented results and obtained a Dice of 83.1%, Hausdorff of 18.8.…”

“…Our approach achieves a better performance in terms of Dice, Sensitivity, and Precision concerning three other methods. Specifically, the study of Budak et al (2021) acquired a Dice of 0.8961 and a Sensitivity of 0.9273 with SegNet. Also, Zhou, Canu & Ruan (2020) leveraged Unet with attention mechanism and obtained the performance of 0.8310 and 0.8670 by Dice and Sensitivity, respectively.…”

“…Empirically, the authors also stated that the mini-batch size affects the performance negatively. Budak et al (2021) presented a new procedure for automatic segmentation of COVID-19 in CT images using SegNet and attention gate mechanism. A dataset with 473 CT images has been utilized as the evaluation data.…”

“…In recent years, deep learning algorithms have provided great tools for medical segmentation, which plays an essential role in disease diagnosis and is one of the most crucial tasks in medical image processing and analysis. Diagnostic based on segmented medical images holds an essential contribution to improving accuracy, timeliness, and disease diagnosis efficiency (Saood & Hatem, 2021;Budak et al, 2021;Zhou, Canu & Ruan, 2020;Raj et al, 2021;Yakubovskiy, 2020). For example, for ultrasound images, the physician and doctors can accurately detect and measure the size of the solid organs in the abdomen and abnormal masses on the segmented areas in the images (Ouahabi & Taleb-Ahmed, 2021;Zhou et al, 2021).…”

Artificial intelligence-driven assessment of radiological images for COVID-19

“…Overall, segmentation techniques are specifically divided into four categories; (a) manual-based segmentation is defined as the delineation of the contours of anatomical regions that is performed by experts (e.g. radiologists, pathologists) [ 74 ]; (b) model-based segmentation is defined as the assignment of labels to pixels or voxels by matching the a priori known object model to the image data [ 75 ]; (c) DL-based segmentation (dominantly CNN-based) as used for automated feature extraction; and (d) hybrid segmentation methods which combines conventional and DL-based methods [ 76 , 77 ]. Segmentation of the lungs in COVID-19 infected cases consists of delineating the borders of the anatomical structures of lung or pneumonia lesions with computer-assisted contouring.…”