ABSTRACT
The Covid-19 infection challenges medical staff to make rapid diagnoses of patients. In just a few days, the Covid-19 virus infection could affect the performance of the lungs. On the other hand, semantic segmentation using the Convolutional Neural Network (CNN) on Lung CT-scan images had attracted the attention of researchers for several years, even before the Covid-19 pandemic. Ground Glass Opacity (GGO), in the form of white patches caused by Covid-19 infection, is detected inside the patient's lung area and occasionally at the edge of the lung, but no research has specifically paid attention to the edges of the lungs. This study proposes to display a 3D visualization of the lung surface of Covid-19 patients based on CT-scan image segmentation using U-Net architecture with a training dataset from typical lung images. Then the resulting CNN model is used to segment the lungs of Covid-19 patients. The segmentation results are selected as some slices to be reconstructed into a 3D lung shape and displayed in 3D animation. Visualizing the results of this segmentation can help medical staff diagnose the lungs of Covid-19 patients, especially on the surface of the lungs of patients with GGO at the edges. From the lung segmentation experiment results on ten patients in the Zenodo dataset, we have a Mean-IoU score = of 76.86%, while the visualization results show that 7 out of 10 patients (70%) have eroded lung surfaces. It can be seen clearly through 3D visualization.
ABSTRACT
Lung segmentation is the first step in medical image processing to determine various lung diseases. Currently, the image segmentation process will be more optimal by using deep learning through the convolution process. Various Convolution Neural Network (CNN) based architectures for image segmentation were created by many researchers, however U-Net is the current state of the art for medical image segmentation. Nevertheless, the modification of U-Net continues, and MultiResUNet is one of the new architectures claimed to be better. In this study, we use MultiResUNet for lung segmentation on Computed Tomography (CT) images as the first step to Covid-19 infection segmentation, and the results will be compared using the U-Net architecture. Based on the results of the segmentation experiment, we got satisfactory results. Using the Mean-IoU evaluation metric, it was concluded that the MultiResUNet score was slightly better than the U-Net score for patient lung segmentation, where there was an increase in the score of 1.33% (MultiResUNet=93.05%, U-Net=91.83%) in the dataset which we use. © 2022 IEEE.
ABSTRACT
In medical image analysis, lung segmentation is needed as an initial step in diagnosing various diseases in the lung area, including Covid-19 infection. Deep Learning has been used for image segmentation in recent years. One of the Deep Learning-based architectures widely used in medical image segmentation is U-Net CNN. U-Net employs a semantic segmentation approach, which has the benefit of being accurate in segmenting even though the model is trained on a limited quantity of data. Our work intends to assist radiologists in providing a more detailed visualization of COVID-19 infection on CT scans, including infection categories and lung conditions. We conduct preliminary work to segment lung regions using U-Net CNN. The dataset used is relatively small, consisting of 267 CT-scan images split into 240 (90%) images for training and 27 (10%) images for testing. The model is evaluated using the K-fold cross-validation (k=10) approach, which has been believed to be appropriate for models created with limited training data. The metric used for experiments is Mean-IoU. It is commonly used in evaluating the segmentation processes. The results achieved were satisfactory, with Mean-IoU scores ranging from 90.2% to 95.3% in each test phase (k1 – k10), with an average value of 93.3%. © 2022 IEEE.