Extracting Tree-structures in CT data by Tracking Multiple Statistically Ranked Hypotheses

In this work, we adapt a method based on multiple hypothesis tracking (MHT) that has been shown to give state-of-the-art vessel segmentation results in interactive settings, for the purpose of extracting trees. Regularly spaced tubular templates are fit to image data forming local hypotheses. These local hypotheses are used to construct the MHT tree, which is then traversed to make segmentation decisions. However, some critical parameters in this method are scale-dependent and have an adverse effect when tracking structures of varying dimensions. We propose to use statistical ranking of local hypotheses in constructing the MHT tree, which yields a probabilistic interpretation of scores across scales and helps alleviate the scale-dependence of MHT parameters. This enables our method to track trees starting from a single seed point. Our method is evaluated on chest CT data to extract airway trees and coronary arteries. In both cases, we show that our method performs significantly better than the original MHT method.Comment: Accepted for publication at the International Journal of Medical Physics and Practic

Imaging the developing human external and internal urogenital organs with light sheet fluorescence microscopy.

Technological advances in three-dimensional (3D) reconstruction techniques have previously enabled paradigm shifts in our understanding of human embryonic and fetal development. Light sheet fluorescence microscopy (LSFM) is a recently-developed technique that uses thin planes of light to optically section whole-mount cleared and immunolabeled biologic specimens. The advent of commercially-available light sheet microscopes has facilitated a new generation of research into protein localization and tissue dynamics at extremely high resolution. Our group has applied LSFM to study developing human fetal external genitalia, internal genitalia and kidneys. This review describes LSFM and presents our group's technique for preparing, clearing, immunostaining and imaging human fetal urogenital specimens. We then present light sheet images and videos of each element of the developing human urogenital system. To the extent of our knowledge, the work conducted by our laboratory represents the first description of a method for performing LSFM on the full human urogenital system during the embryonic and fetal periods

Enhancement of HGF-induced tubulogenesis by endothelial cell-derived GDNF

学位記番号：医博甲175

Long-range mechanical force in colony branching and tumor invasion

The most concerned factors for cancer prognosis are tumor invasion and metastasis. The patterns of tumor invasion can be characterized as random infiltration to surrounding extracellular matrix (ECM) or formation of long-range path for collective migration. Recent studies indicate that mechanical force plays an important role in tumor infiltration and collective migration. However, how tumor colonies develop mechanical interactions with each other to initiate various invasion patterns is unclear. Using a micro-patterning technique, we partition cells into clusters to mimic tumor colonies and quantitatively induce colony-ECM interactions. We find that pre-malignant epithelial cells, in response to concentrations of type I collagen in ECM ([COL]), develop various branching patterns resembling those observed in tumor invasion. In contrast with conventional thought, these patterns require long-range (~ 600 μm) transmission of traction force, but not biochemical factors. At low [COL], cell colonies synergistically develop pairwise and directed branching mimicking the formation of long-range path. By contrast, at high [COL] or high colony density, cell colonies develop random branching and scattering patterns independent of each other. Our results suggest that tumor colonies might select different invasive patterns depending on their interactions with each other and with the ECM

A practical workflow for making anatomical atlases for biological research

pre-printAn anatomical atlas provides a detailed map for medical and biological studies of anatomy. These atlases are important for understanding normal anatomy and the development and function of structures, and for determining the etiology of congenital abnormalities. Unfortunately, for biologists, generating such atlases is difficult, especially ones with the informative content and aesthetic quality that characterize human anatomy atlases. Building such atlases requires knowledge of the species being studied and experience with an art form that can faithfully record and present this knowledge, both of which require extensive training in considerably different fields. (For some background on anatomical atlases, see the related sidebar.

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

Robust semi-automated path extraction for visualising stenosis of the coronary arteries

Computed tomography angiography (CTA) is useful for diagnosing and planning treatment of heart disease. However, contrast agent in surrounding structures (such as the aorta and left ventricle) makes 3-D visualisation of the coronary arteries difficult. This paper presents a composite method employing segmentation and volume rendering to overcome this issue. A key contribution is a novel Fast Marching minimal path cost function for vessel centreline extraction. The resultant centreline is used to compute a measure of vessel lumen, which indicates the degree of stenosis (narrowing of a vessel). Two volume visualisation techniques are presented which utilise the segmented arteries and lumen measure. The system is evaluated and demonstrated using synthetic and clinically obtained datasets