MOSAICING OF DYNAMIC MESENTERY VIDEO WITH GRADIENT BLENDING.

In biomedical imaging using video microscopy, understanding large tissue structures at cellular and finer resolution poses many image acquisition challenges including limited field-of-view and tissue dynamics during imaging. Automated mosaicing or stitching of live tissue video microscopy enables the visualization and analysis of subtle morphological structures and large scale vessel network architecture in tissues like the mesentery. But mosacing can be challenging if there are deformable, motion-blurred, textureless, feature-poor frames. Feature-based methods perform poorly in such cases for the lack of distinctive keypoints. Standard single block correlation matching strategies might not provide robust registration due to deformable content. In addition, the panorama suffers if there is motion blur present in a sequence. To handle these challenges, we propose a novel algorithm, Deformable Normalized Cross Correlation (DNCC) image matching with RANSAC to establish robust registration. Besides, to produce seamless panorama from motion-blurred frames we present gradient blending method based on image edge information. The DNCC algorithm is applied on Frog Mesentery sequences. Our result is compared with PSS/AutoStitch [1, 2] to establish the efficiency and robustness of the proposed DNCC method.

Development of a novel in vivo corneal fibrosis model in the dog.

The aim of this study was to develop a novel in vivo corneal model of fibrosis in dogs utilizing alkali burn and determine the ability of suberanilohydroxamic acid (SAHA) to inhibit corneal fibrosis using this large animal model. To accomplish this, we used seven research Beagle dogs. An axial corneal alkali burn in dogs was created using 1 N NaOH topically. Six dogs were randomly and equally assigned into 2 groups: A) vehicle (DMSO, 2 µL/mL); B) anti-fibrotic treatment (50 µM SAHA). The degree of corneal opacity, ocular health, and anti-fibrotic effects of SAHA were determined utilizing the Fantes grading scale, modified McDonald-Shadduck (mMS) scoring system, optical coherence tomography (OCT), corneal histopathology, immunohistochemistry (IHC), and transmission electron microscopy (TEM). The used alkali burn dose to produce corneal fibrosis was well tolerated as no significant difference in mMS scores between control and treatment groups (p = 0.89) were detected. The corneas of alkali burned dogs showed significantly greater levels of α-smooth muscle actin, the fibrotic marker, than the controls (p = 0.018). Total corneal thickness of all dogs post-burn was significantly greater than baseline OCT images irrespective of treatment (p = 0.004); TEM showed that alkali burned corneas had significantly greater minimum and maximum interfibrillar distances than the controls (p = 0.026, p = 0.018). The tested topical corneal alkali burn dose generated significant opacity and fibrosis in dog corneas without damaging the limbus as evidenced by histopathology, IHC, TEM, and OCT findings, and represents a viable large animal corneal fibrosis in vivo model. Additional in vivo SAHA dosing studies with larger sample size are warranted.

Cell spreading analysis with directed edge profile-guided level set active contours.

Cell adhesion and spreading within the extracellular matrix (ECM) plays an important role in cell motility, cell growth and tissue organization. Measuring cell spreading dynamics enables the investigation of cell mechanosensitivity to external mechanical stimuli, such as substrate rigidity. A common approach to measure cell spreading dynamics is to take time lapse images and quantify cell size and perimeter as a function of time. In our experiments, differences in cell characteristics between different treatments are subtle and require accurate measurements of cell parameters across a large population of cells to ensure an adequate sample size for statistical hypothesis testing. This paper presents a new approach to estimate accurate cell boundaries with complex shapes by applying a modified geodesic active contour level set method that directly utilizes the halo effect typically seen in phase contrast microscopy. Contour evolution is guided by edge profiles in a perpendicular direction to ensure convergence to the correct cell boundary. The proposed approach is tested on bovine aortic endothelial cell images under different treatments, and demonstrates accurate segmentation for a wide range of cell sizes and shapes compared to manual ground truth.

CELL SEGMENTATION USING HESSIAN-BASED DETECTION AND CONTOUR EVOLUTION WITH DIRECTIONAL DERIVATIVES.

The large amount of data produced by biological live cell imaging studies of cell behavior requires accurate automated cell segmentation algorithms for rapid, unbiased and reproducible scientific analysis. This paper presents a new approach to obtain precise boundaries of cells with complex shapes using ridge measures for initial detection and a modified geodesic active contour for curve evolution that exploits the halo effect present in phase-contrast microscopy. The level set contour evolution is controlled by a novel spatially adaptive stopping function based on the intensity profile perpendicular to the evolving front. The proposed approach is tested on human cancer cell images from LSDCAS and achieves high accuracy even in complex environments.

Epifluorescence-based quantitative microvasculature remodeling using geodesic level-sets and shape-based evolution.

Accurate vessel segmentation is the first step in analysis of microvascular networks for reliable feature extraction and quantitative characterization. Segmentation of epifluorescent imagery of microvasculature presents a unique set of challenges and opportunities compared to traditional angiogram-based vessel imagery. This paper presents a novel system that combines methods from mathematical morphology, differential geometry, and active contours to reliably detect and segment microvasculature under varying background fluorescence conditions. The system consists of three main modules: vessel enhancement, shape-based initialization, and level-set based segmentation. Vessel enhancement deals with image noise and uneven background fluorescence using anisotropic diffusion and mathematical morphology techniques. Shape-based initialization uses features from the second-order derivatives of the enhanced vessel image and produces a coarse ridge (vessel) mask. Geodesic level-set based active contours refine the coarse ridge map and fix possible discontinuities or leakage of the level set contours that may arise from complex topology or high background fluorescence. The proposed system is tested on epifluorescence-based high resolution images of porcine dura mater microvasculature. Preliminary experiments show promising results.