Artificial Intelligence Based Instance-Aware Semantic Lobe Segmentation on Chest Computed Tomography Images

Abstract

The coronavirus disease (COVID-19) has taken the entire world under its influence, causing a worldwide health crisis. The most concerning complication is acute hypoxemic respiratory failure that results in fatal consequences. To alleviate the effect of COVID-19, the infected region should be analyzed before the treatment. Thus, chest computed tomography (CT) is a popular method to determine the severity level of COVID-19. Besides, the number of lobe regions containing COVID-19 on CT images helps radiologists to diagnose the findings, such as bilateral, multifocal, and multilobar. Lobe regions can be distinguished manually by radiologists, but this may result in misdiagnosis due to human intervention. Therefore, in this study, a new tool has been developed that can automatically extract lobe regions using artificial intelligence-based instance-aware semantic lobe segmentation. Convolution neural networks (CNNs) offer automatic feature extraction in the instance-aware semantic lobe segmentation task that extracts the lobe regions on CT images. In this paper, CNN-based architectures, including DeepLabV3+ with VGG-16, VGG-19, and ResNet-50, were utilized to create a benchmark for the instance-aware semantic lobe segmentation task. For further improvement in segmentation results, images were preprocessed to detect the lung region prior to lobe segmentation. In the experimental evaluations, a large-scale dataset including 9036 images with pixel-level annotations for lung and lobe regions, has been created. DeepLabV3+ with ResNet-50 showed the highest performance in terms of dice similarity coefficient (DSC) and intersection over union (IOU) for lobe segmentation at 99.59 % and 99.19 %, respectively. The experiments demonstrated that our approach outperformed several state-of-the-art methods for the instance-aware semantic lobe segmentation task. Furthermore, a new desktop application called LobeChestApp was developed for the segmentation of lobe regions on chest CT images.

Beyin Bilgisayarlı Tomografi Görüntülerinde Derin Öğrenme Tabanlı İskemik İnme Hastalığı Segmentasyonu

“…For feature extraction, deep learning uses network architectures, such as convolutional neural networks (CNNs) (Ağralı et al;Akosman, Öktem, Moral, & Kılıç, 2021;Çaylı, Kılıç, Onan, & Wang, 2022;Keskin, Moral, Kılıç, & Onan, 2021;B. Kilic, Dogan, Kilic, & Kahyaoglu, 2022;Sayraci, Agrali, & Kilic, 2023;Şen et al, 2022;Yüzer, Doğan, Kılıç, & Şen, 2022), reinforcement learning (Agrali, Soydemir, Gökçen, & Sahin, 2021), and recurrent neural networks (RNNs) (Aydın, Çaylı, Kılıç, & Onan, 2022;Fetiler, Caylı, Moral, Kılıc, & Onan, 2021;Gölcez, Kiliç, & Şen, 2019;Keskin, Çaylı, Moral, Kılıc, & Onan, 2021;Kılıc, 2021;Volkan Kılıç;Kökten & Kılıç, 2021;Mercan, Doğan, & Kılıç, 2020;Mercan & Kılıç, 2021;Palaz, Doğan, & Kılıç, 2021). Among these architectures, CNN offers remarkable performance on ischemic stroke disease segmentation.…”