The computer-based process of identifying the boundaries of lung from surrounding thoracic
tissue on computed tomographic (CT) images, which is called segmentation, is a vital first …

Over the last decade, deep learning (DL) has contributed to a paradigm shift in computer
vision and image recognition creating widespread opportunities of using artificial …

Background Automated segmentation of anatomical structures is a crucial step in image
analysis. For lung segmentation in computed tomography, a variety of approaches exists …

The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening
of COVID-19 patients is attracting much interest from both the clinical and the AI community …

Convolutional neural networks (CNNs) have shown remarkable results over the last several
years for a wide range of computer vision tasks. A new architecture recently introduced by …

To improve the early diagnosis and treatment of lung diseases automated lung
segmentation from CT images is a crucial task for clinical decision. The segmentation of the …

Lung cancer has one of the highest morbidity and mortality rates in the world. Lung nodules
are an early indicator of lung cancer. Therefore, accurate detection and image segmentation …

Abstract The emergence of Middle East respiratory syndrome coronavirus (MERS-CoV) as a
cause of severe respiratory disease highlights the need for effective approaches to CoV …

Abstract Coronavirus Disease 2019 (COVID-19) has become one of the most urgent public
health events worldwide due to its high infectivity and mortality. Computed tomography (CT) …

Robust organ segmentation is a prerequisite for computer-aided diagnosis, quantitative
imaging analysis, pathology detection, and surgical assistance. For organs with high …