Optimal graph based segmentation using flow lines with application to airway wall segmentation

This paper introduces a novel optimal graph construction method that is applicable to multi-dimensional, multi-surface segmentation problems. Such problems are often solved by refining an initial coarse surface within the space given by graph columns. Conventional columns are not well suited for surfaces with high curvature or complex shapes but the proposed columns, based on properly generated flow lines, which are non-intersecting, guarantee solutions that do not self-intersect and are better able to handle such surfaces. The method is applied to segment human airway walls in computed tomography images. Comparison with manual annotations on 649 cross-sectional images from 15 different subjects shows significantly smaller contour distances and larger area of overlap than are obtained with recently published graph based methods. Airway abnormality measurements obtained with the method on 480 scan pairs from a lung cancer screening trial are reproducible and correlate significantly with lung function

Carotid artery lumen segmentation in 3D free-hand ultrasound images using surface graph cuts

We present a new approach for automated segmentation of the carotid lumen bifurcation from 3D free-hand ultrasound using a 3D surface graph cut method. The method requires only the manual selection of single seed points in the internal, external, and common carotid arteries. Subsequently, the centerline between these points is automatically traced, and the optimal lumen surface is found around the centerline using graph cuts. To refine the result, the latter process was iterated. The method was tested on twelve carotid arteries from six subjects including three patients with a moderate carotid artery stenosis. Our method successfully segmented the lumen in all cases. We obtained an average dice overlap with respect to a manual segmentation of 84% for healthy volunteers. For the patient data, we obtained a dice overlap of 66.7%

Existing and Potential Statistical and Computational Approaches for the Analysis of 3D CT Images of Plant Roots

Scanning technologies based on X-ray Computed Tomography (CT) have been widely used in many scientific fields including medicine, nanosciences and materials research. Considerable progress in recent years has been made in agronomic and plant science research thanks to X-ray CT technology. X-ray CT image-based phenotyping methods enable high-throughput and non-destructive measuring and inference of root systems, which makes downstream studies of complex mechanisms of plants during growth feasible. An impressive amount of plant CT scanning data has been collected, but how to analyze these data efficiently and accurately remains a challenge. We review statistical and computational approaches that have been or may be effective for the analysis of 3D CT images of plant roots. We describe and comment on different approaches to aspects of the analysis of plant roots based on images, namely, (1) root segmentation, i.e., the isolation of root from non-root matter; (2) root-system reconstruction; and (3) extraction of higher-level phenotypes. As many of these approaches are novel and have yet to be applied to this context, we limit ourselves to brief descriptions of the methodologies. With the rapid development and growing use of X-ray CT scanning technologies to generate large volumes of data relevant to root structure, it is timely to review existing and potential quantitative and computational approaches to the analysis of such data. Summaries of several computational tools are included in the Appendix