A pinboard by

Postdoc Research Scientist, Columbia University


Using artificial intelligence techniques to identify critical disease pattern from heart data

My research focuses on pushing the boundary of data science and technology development to bring to surface the key disease patterns in heart images with the aim of helping clinicians and patients.

Heart disease is the leading cause of human death worldwide. Among various heart disease, atrial fibrillation is one of the most common arrhythmias (irregular heart beating) afflicting approximately 6 million people in the United States. Radio frequency ablation is a standard treatment for cardiac arrhythmias, however ~40 % of atrial fibrillation patients require two procedures to be chronically terminated of AF. The delivery of safe and effective ablation therapy requires precise identification of regions to target and regions to avoid. In addition, the variation of heart architecture among patients highlights the needs for accurate substrate delineation.

I have acquired TBs of human cardiac image data and built a human cardiac atlas based on a high resolution imaging technique, optical coherence tomography. I have employed the artificial intelligence technique, particularly deep learning, to develop a machine learning algorithm that can pore over the large database and automate the classification of diseased tissue regions which are related to irregular heart beating. The developed image analysis tool can potentially facilitate the treatment of irregular heart beating.


A continuous fiber distribution material model for human cervical tissue.

Abstract: The uterine cervix during pregnancy is the vital mechanical barrier which resists compressive and tensile loads generated from a growing fetus. Premature cervical remodeling and softening is hypothesized to result in the shortening of the cervix, which is known to increase a woman׳s risk of preterm birth. To understand the role of cervical material properties in preventing preterm birth, we derive a cervical material model based on previous mechanical, biochemical and histological experiments conducted on nonpregnant and pregnant human hysterectomy cervical tissue samples. In this study we present a three-dimensional fiber composite model that captures the equilibrium material behavior of the tissue in tension and compression. Cervical tissue is modeled as a fibrous composite material, where a single family of preferentially aligned and continuously distributed collagen fibers are embedded in a compressible neo-Hookean ground substance. The total stress in the collagen solid network is calculated by integrating the fiber stresses. The shape of the fiber distribution is described by an ellipsoid where semi-principal axis lengths are fit to optical coherence tomography measurements. The composite material model is fit to averaged mechanical testing data from uni-axial compression and tension experiments, and averaged material parameters are reported for nonpregnant and term pregnant human cervical tissue. The model is then evaluated by investigating the stress and strain state of a uniform thick-walled cylinder under a compressive stress with collagen fibers preferentially aligned in the circumferential direction. This material modeling framework for the equilibrium behavior of human cervical tissue serves as a basis to determine the role of preferentially-aligned cervical collagen fibers in preventing cervical deformation during pregnancy.

Pub.: 31 Mar '15, Pinned: 31 Aug '17

Collagen Fiber Orientation and Dispersion in the Upper Cervix of Non-Pregnant and Pregnant Women.

Abstract: The structural integrity of the cervix in pregnancy is necessary for carrying a pregnancy until term, and the organization of human cervical tissue collagen likely plays an important role in the tissue's structural function. Collagen fibers in the cervical extracellular matrix exhibit preferential directionality, and this collagen network ultrastructure is hypothesized to reorient and remodel during cervical softening and dilation at time of parturition. Within the cervix, the upper half is substantially loaded during pregnancy and is where the premature funneling starts to happen. To characterize the cervical collagen ultrastructure for the upper half of the human cervix, we imaged whole axial tissue slices from non-pregnant and pregnant women undergoing hysterectomy or cesarean hysterectomy respectively using optical coherence tomography (OCT) and implemented a pixel-wise fiber orientation tracking method to measure the distribution of fiber orientation. The collagen fiber orientation maps show that there are two radial zones and the preferential fiber direction is circumferential in a dominant outer radial zone. The OCT data also reveal that there are two anatomic regions with distinct fiber orientation and dispersion properties. These regions are labeled: Region 1-the posterior and anterior quadrants in the outer radial zone and Region 2-the left and right quadrants in the outer radial zone and all quadrants in the inner radial zone. When comparing samples from nulliparous vs multiparous women, no differences in these fiber properties were noted. Pregnant tissue samples exhibit an overall higher fiber dispersion and more heterogeneous fiber properties within the sample than non-pregnant tissue. Collectively, these OCT data suggest that collagen fiber dispersion and directionality may play a role in cervical remodeling during pregnancy, where distinct remodeling properties exist according to anatomical quadrant.

Pub.: 30 Nov '16, Pinned: 31 Aug '17

Analyzing three-dimensional ultrastructure of human cervical tissue using optical coherence tomography.

Abstract: During pregnancy, the uterine cervix is the mechanical barrier that prevents delivery of a fetus. The underlying cervical collagen ultrastructure, which influences the overall mechanical properties of the cervix, plays a role in maintaining a successful pregnancy until term. Yet, not much is known about this collagen ultrastructure in pregnant and nonpregnant human tissue. We used optical coherence tomography to investigate the directionality and dispersion of collagen fiber bundles in the human cervix. An image analysis tool has been developed, combining a stitching method with a fiber orientation measurement, to study axially sliced cervix samples. This tool was used to analyze the ultrastructure of ex-vivo pregnant and non-pregnant hysterectomy tissue samples taken at the internal os, which is the region of the cervix adjacent to the uterus. With this tool, directionality maps of collagen fiber bundles and dispersion of collagen fiber orientation were analyzed. It was found that that the overall preferred directionality of the collagen fibers for both the nonpregnant and pregnant samples were circling around the inner cervical canal. Pregnant samples showed greater dispersion than non-pregnant samples. Lastly, we observed regional differences in collagen fiber dispersion. Fibers closer to the inner canal showed more dispersion than the fibers on the radial edges.

Pub.: 25 Apr '15, Pinned: 31 Aug '17