Computed Tomography (CT) has been widely used for assisting in lung cancer detection/diagnosis and treatment.
In lung cancer diagnosis, suspect lesions or regions of interest (ROIs) are usually analyzed in screening
CT scans. Then, CT-based image-guided minimally invasive procedures are performed for further diagnosis
through bronchoscopic or percutaneous approaches. Thus, ROI segmentation is a preliminary but vital step
for abnormality detection, procedural planning, and intra-procedural guidance. In lung cancer diagnosis, such
ROIs can be tumors, lymph nodes, nodules, etc., which may vary in size, shape, and other complication phenomena.
Manual segmentation approaches are time consuming, user-biased, and cannot guarantee reproducible
results. Automatic methods do not require user input, but they are usually highly application-dependent. To
counterbalance among efficiency, accuracy, and robustness, considerable efforts have been contributed to semi-automatic
strategies, which enable full user control, while minimizing human interactions. Among available
semi-automatic approaches, the live-wire algorithm has been recognized as a valuable tool for segmentation of
a wide range of ROIs from chest CT images. In this paper, a new 3D extension of the traditional 2D live-wire
method is proposed for 3D ROI segmentation. In the experiments, the proposed approach is applied to a set of
anatomical ROIs from 3D chest CT images, and the results are compared with the segmentation derived from
a previous evaluated live-wire-based approach.