Jian Mu

A Multiscale Model of Venous Thrombus Formation with Surface-Mediated Control of Blood Coagulation Cascade

A combination of the extended multiscale model, new image processing algorithms, and biological experiments is used for studying the role of Factor VII (FVII) in venous thrombus formation. A detailed submodel of the tissue factor pathway of blood coagulation is introduced within the framework of the multiscale model to provide a detailed description of coagulation cascade. Surface reactions of the extrinsic coagulation pathway on membranes of platelets are studied under different flow conditions. It is shown that low levels of FVII in blood result in a significant delay in thrombin production, demonstrating that FVII plays an active role in promoting thrombus development at an early stage.

Two-photon intravital imaging of thrombus development

Thrombus development in mouse mesenteric vessels following laser-induced injury was monitored by high-resolution, near-real-time, two-photon, intravital microscopy. In addition to the use of fluorescently tagged fibrin(ogen) and platelets, plasma was labeled with fluorescently tagged dextran. Because blood cells exclude the dextran in the single plane, blood cells appear as black silhouettes. Thus, in addition to monitoring the accumulation of platelets and fibrin in the thrombus, the protocol detects the movement and incorporation of unlabeled cells in and around it. The developing thrombus perturbs the blood flow near the thrombus surface, which affects the incorporation of platelets and blood cells into the structure. The hemodynamic effects and incorporation of blood cells lead to the development of thrombi with heterogeneous domain structures. Additionally, image processing algorithms and simulations were used to quantify structural features of developing thrombi. This analysis suggests a novel mechanism to stop the growth of developing thrombus.

Spatial organization of dendritic cells within tumor draining lymph nodes impacts clinical outcome in breast cancer patients

BackgroundDendritic cells (DCs) are important mediators of anti-tumor immune responses. We hypothesized that an in-depth analysis of dendritic cells and their spatial relationships to each other as well as to other immune cells within tumor draining lymph nodes (TDLNs) could provide a better understanding of immune function and dysregulation in cancer.MethodsWe analyzed immune cells within TDLNs from 59 breast cancer patients with at least 5 years of clinical follow-up using immunohistochemical staining with a novel quantitative image analysis system. We developed algorithms to analyze spatial distribution patterns of immune cells in cancer versus healthy intra-mammary lymph nodes (HLNs) to derive information about possible mechanisms underlying immune-dysregulation in breast cancer. We used the non-parametric Mann–Whitney test for inter-group comparisons, Wilcoxon Matched-Pairs Signed Ranks test for intra-group comparisons and log-rank (Mantel-Cox) test for Kaplan Maier analyses.ResultsDegree of clustering of DCs (in terms of spatial proximity of the cells to each other) was reduced in TDLNs compared to HLNs. While there were more numerous DC clusters in TDLNs compared to HLNs,DC clusters within TDLNs tended to have fewer member DCs and also consisted of fewer cells displaying the DC maturity marker CD83. The average number of T cells within a standardized radius of a clustered DC was increased compared to that of an unclustered DC, suggesting that DC clustering was associated with T cell interaction. Furthermore, the number of T cells within the radius of a clustered DC was reduced in tumor-positive TDLNs compared to HLNs. Importantly, clinical outcome analysis revealed that DC clustering in tumor-positive TDLNs correlated with the duration of disease-free survival in breast cancer patients.ConclusionsThese findings are the first to describe the spatial organization of DCs within TDLNs and their association with survival outcome. In addition, we characterized specific changes in number, size, maturity, and T cell co-localization of such clusters. Strategies to enhance DC function in-vivo, including maturation and clustering, may provide additional tools for developing more efficacious DC cancer vaccines.

Segmentation, reconstruction, and analysis of blood thrombus formation in 3D 2-photonmicroscopy images

We study the problem of segmenting, reconstructing, and analyzing the structure growth of thrombi (clots) in blood vessels in vivo based on 2-photon microscopic image data. First, we develop an algorithm for segmenting clots in 3D microscopic images based on density-based clustering and methods for dealing with imaging artifacts. Next, we apply the union-of-balls (or alpha-shape) algorithm to reconstruct the boundary of clots in 3D. Finally, we perform experimental studies and analysis on the reconstructed clots and obtain quantitative data of thrombus growth and structures. We conduct experiments on laser-induced injuries in vessels of two types of mice (the wild type and the type with low levels of coagulation factor VII) and analyze and compare the developing clot structures based on their reconstructed clots from image data. The results we obtain are of biomedical significance. Our quantitative analysis of the clot composition leads to better understanding of the thrombus development, and is valuable to the modeling and verification of computational simulation of thrombogenesis.

Combined experimental and simulation study of blood clot formation

A multiscale model of blood clot (thrombus) formation is extended by adding a sub-model of the tissue factor (TF) pathway of blood coagulation to provide more biologically relevant description of coagulation process. A combination of experimental approach, image analysis and a multiscale modeling is used for studying role of factor VII and fibrin in limiting growth of a blood clot. The simulations obtained using new extended model, generated a hypothesis that formation of a fiber cap is capable of stopping blood clot growth by blocking release of thrombin, causing activation of platelets, into a blood stream. This was confirmed by comparison with reconstructed three dimensional experimental images of clots formed in mice with normal and limited levels of factor VII.

Segmentation, reconstruction, and analysis of blood thrombi in 2-photon microscopy images

In this paper, we study the problem of segmenting, reconstructing, and analyzing the structure and growth of thrombi (clots) in vivo in blood vessels based on 2-photon microscopic image data. First, we develop an algorithm for segmenting clots in 3-D microscopic images which incorporates the density-based clustering algorithm and other methods for dealing with imaging artifacts. Next, we apply the union-of-balls (or alpha-shape) algorithm to reconstruct the boundary of clots in 3-D. Finally, we perform experimental analysis on the reconstructed clots and obtain quantitative data of thrombus growth and structures. The experiments are conducted on laser-induced injuries in vessels of two types of mice (the wild type and the type with low levels of coagulation factor VII). By analyzing and comparing the developing clot structures based on their reconstruction from image data, we obtain results of biomedical significance. Our quantitative analysis of the clot composition leads to better understanding of the thrombus development, which is also valuable to the modeling and verification of computational simulation of thrombogenesis.

Automatic segmentation and analysis of fibrin networks in 3D confocal microscopy images

Fibrin networks are a major component of blood clots that provides structural support to the formation of growing clots. Abnormal fibrin networks that are too rigid or too unstable can promote cardiovascular problems and/or bleeding. However, current biological studies of fibrin networks rarely perform quantitative analysis of their structural properties (e.g., the density of branch points) due to the massive branching structures of the networks. In this paper, we present a new approach for segmenting and analyzing fibrin networks in 3D confocal microscopy images. We first identify the target fibrin network by applying the 3D region growing method with global thresholding. We then produce a one-voxel wide centerline for each fiber segment along which the branch points and other structural information of the network can be obtained. Branch points are identified by a novel approach based on the outer medial axis. Cells within the fibrin network are segmented by a new algorithm that combines cluster detection and surface reconstruction based on the α-shape approach. Our algorithm has been evaluated on computer phantom images of fibrin networks for identifying branch points. Experiments on z-stack images of different types of fibrin networks yielded results that are consistent with biological observations.

Segmentation of knee joints in x-ray images using decomposition-based sweeping and graph search

Plain radiography (i.e., X-ray imaging) provides an effective and economical imaging modality for diagnosing knee illnesses and injuries. Automatically segmenting and analyzing knee radiographs is a challenging problem. In this paper, we present a new approach for accurately segmenting the knee joint in X-ray images. We first use the Gaussian high-pass filter to remove homogeneous regions which are unlikely to appear on bone contours. We then presegment the bones and develop a novel decomposition-based sweeping algorithm for extracting bone contour topology from the filtered skeletonized images. Our sweeping algorithm decomposes the bone structures into several relatively simple components and deals with each component separately based on its geometric characteristics using a sweeping strategy. Utilizing the presegmentation, we construct a graph to model the bone topology and apply an optimal graph search algorithm to optimize the segmentation results (with respect to our cost function defined on the bone boundaries). Our segmented results match well with the manual tracing results by radiologists. Our segmentation approach can be a valuable tool for assisting radiologists and X-ray technologists in clinical practice and training.

An optimization-based approach for restoring missing structures and textures in images

In this paper, we present a new automated algorithm for image completion, i.e., reconstructing the missing, damaged, or occluded parts in images in a visually non-detectable fashion. Our algorithm is capable of recovering both structural and textural information on the damaged parts, by solving several key subproblems such as determining the connections and shapes of the occluded region boundary curves, synthesizing textures, etc. Our algorithm combines structure-based and texture-based approaches and is hinged on optimization techniques. In particular, we formulate a set of key subproblems as optimization problems in graph theory, and solve them optimally in polynomial time. Previous methods for these problems either cannot ensure the topological correctness of the restored structures or rely only on heuristics.

Segmentation of vascular structures and hematopoietic cells in 3D microscopy images and quantitative analysis

In this paper, we present image processing methods for quantitative study of how the bone marrow microenvironment changes (characterized by altered vascular structure and hematopoietic cell distribution) caused by diseases or various factors. We develop algorithms that automatically segment vascular structures and hematopoietic cells in 3-D microscopy images, perform quantitative analysis of the properties of the segmented vascular structures and cells, and examine how such properties change. In processing images, we apply local thresholding to segment vessels, and add post-processing steps to deal with imaging artifacts. We propose an improved watershed algorithm that relies on both intensity and shape information and can separate multiple overlapping cells better than common watershed methods. We then quantitatively compute various features of the vascular structures and hematopoietic cells, such as the branches and sizes of vessels and the distribution of cells. In analyzing vascular properties, we provide algorithms for pruning fake vessel segments and branches based on vessel skeletons. Our algorithms can segment vascular structures and hematopoietic cells with good quality. We use our methods to quantitatively examine the changes in the bone marrow microenvironment caused by the deletion of Notch pathway. Our quantitative analysis reveals property changes in samples with deleted Notch pathway. Our tool is useful for biologists to quantitatively measure changes in the bone marrow microenvironment, for developing possible therapeutic strategies to help the bone marrow microenvironment recovery.