Accurate lung nodule segmentation from computed tomography (CT) images is of great
importance for image-driven lung cancer analysis. However, the heterogeneity of lung …

Early detection of lung nodules is extremely important for the diagnosis and clinical
management of lung cancer. In this paper, a novel computer aided detection (CAD) system …

Deep learning techniques have been extensively used in computerized pulmonary nodule
analysis in recent years. Many reported studies still utilized hybrid methods for diagnosis, in …

We present new pulmonary nodule segmentation algorithms for computed tomography (CT).
These include a fully-automated (FA) system, a semi-automated (SA) system, and a hybrid …

Accurate segmentation of a pulmonary nodule is an important and active area of research in
medical image processing. Although many algorithms have been reported in literature for …

In computer-aided diagnosis systems for lung cancer, segmentation of lung nodules is
important for analyzing image features of lung nodules on computed tomography (CT) …

Purpose: Lung lesions vary considerably in size, density, and shape, and can attach to
surrounding anatomic structures such as chest wall or mediastinum. Automatic …

Lung cancer is one of the leading causes of cancer-related deaths globally, and accurate
segmentation of lung nodules is critical for its early detection and diagnosis. However, small …

Accurate segmentation of lung nodules is an important basis for the subsequent
differentiation of benign and malignant pathological types, which is conducive to early …

Accurate pulmonary nodule segmentation, an essential pre-requisite in every computer-
aided diagnosis (CAD) system, significantly helps in the risk assessment of lung cancer. In …