Lung cancer is responsible for more fatalities than any other cancer worldwide, with 1.76
million associated deaths reported in 2018. The key issue in the fight against this disease is …

Rationale and Objectives The automated segmentation of organs and tissues throughout the
body using computed tomography and magnetic resonance imaging has been rapidly …

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 accurate identification of malignant lung nodules on chest CT is critical for the early
detection of lung cancer, which also offers patients the best chance of cure. Deep learning …

Objective A novel computer-aided detection (CAD) scheme for lung nodule detection using
a 3D deep convolutional neural network combined with a multi-scale prediction strategy is …

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 …

Internal organs, like lungs, are very often examined by the use of screening methods. For
this purpose, we present an evaluation model based on a composition of fuzzy system …

Convolutional neural networks (CNNs) have been a prevailing technique in the field of
medical CT image processing. Although encoder-decoder CNNs exploit locality for …

Segmentation of lungs with acute respiratory distress syndrome (ARDS) is a challenging
task due to diffuse opacification in dependent regions which results in little to no contrast at …

Automatic identification of brain lesions from magnetic resonance imaging (MRI) scans of
stroke survivors would be a useful aid in patient diagnosis and treatment planning. It would …