Biomedical subjects

Shruti A Japee

Publications and source records attributed to Shruti A Japee.

3 recordsLinked to original sources

PubMed2005

A new video image analysis system to study red blood cell dynamics and oxygenation in capillary networks.

OBJECTIVE: The authors present a Measurement and Analysis System for Capillary Oxygen Transport (MASCOT) to study red blood cell (RBC) dynamics and oxygenation in capillary networks. The system enables analysis of capillaries to study geometry and morphology and provides values for capillary parameters such as diameter and segment length. It also serves as an analysis tool for capillary RBC flow characteristics, including RBC velocity, lineal density, and supply rate. Furthermore, the system provides a means of determining the oxygen saturation of hemoglobin contained within RBCs, by analysis of synchronized videotapes containing images at two wavelengths, enabling the quantification of the oxygen content of individual RBCs. METHODS: Video recordings of RBC flow at two wavelengths, 420 nm (isosbestic) and 436 nm (oxygen sensitive), are made using a dual camera video microscopy system. The 420-nm recording is used to generate images based on the variance of light intensity fluctuations that help to identify capillaries in a given field of view that are in sharp focus and exhibit flow of individual RBCs separated by plasma gaps. A region of interest enclosing the desired capillary is defined and a fixed number of successive video frames at the two wavelengths are captured. Next a difference image is created, which delineates the RBC column, whose width is used to estimate the internal diameter of the capillary. The 420-nm images are also used to identify the location and centroid of each RBC within the capillary. A space-time image is generated to compute the average RBC velocity. Lineal density is calculated as the number of RBCs per unit length of a capillary segment. The mean optical density (OD) of each RBC is calculated at both wavelengths, and the average SO(2) for each cell is determined from OD(436)/OD(420). RESULTS AND CONCLUSIONS: MASCOT is a robust and flexible system that requires simple hardware, including a SGI workstation fitted with an audio-visual module, a VCR, and an oscilloscope. Since the new system provides information on an individual cell basis from entire capillary segments, the authors believe that results obtained using MASCOT will be more accurate than those obtained from previous systems. Due to its flexibility and ease of extension to other applications, MASCOT has the potential to be applied widely as an analysis tool for capillary oxygen transport measurements.

Animals↗

PubMed2005

Automated method for tracking individual red blood cells within capillaries to compute velocity and oxygen saturation.

OBJECTIVE: The authors present a new method to track individual red blood cells (RBCs) as they move through capillaries. This method uses a recently developed Measurement and Analysis System for Capillary Oxygen Transport (MASCOT) and the concept of space-time images to track RBCs between consecutive frames of video recordings of the microcirculation. METHODS: A space-time image displays in a single static image for a single capillary the location of all RBCs as a function of time. Analysis is performed on video tapes of RBC flow through capillaries to obtain velocity of individual cells as they traverse the capillary of interest. A space-time image is generated to track RBCs from one frame to the next and their velocities are computed. Based on the optical density values of each cell obtained from synchronized videotapes at two wavelengths, the oxygen saturation of a cell can be determined. In this manner, oxygen saturation can be tracked for the same cells as they move through the capillary. RESULTS AND CONCLUSIONS: These measurements, taken together, allow one to determine how much and how fast oxygen is being delivered to the surrounding tissue. This method provides, for the first time, a way to track individual RBCs flowing through capillary networks and study their RBC dynamics and oxygenation.

Algorithms↗

PubMedDate not supplied

Flow visualization tools for image analysis of capillary networks.

OBJECTIVE: Video recordings of red blood cell (RBC) flow through capillary networks contain a considerable amount of information pertaining to oxygen transport through the microcirculation. Image analysis of these video recordings has been widely used to determine RBC dynamics (velocity, lineal density and supply rate) and oxygenation (Brunner et al., 2000; Ellis et al., 1990, 1992; Ellsworth et al., 1987; Klyscz et al., 1997; Pries 1988). However, not all capillaries in a given field of view are suitable for image analysis. Typically, capillary segments that are relatively straight and in sharp focus, and exhibit flow of individual RBCs that are well separated by plasma gaps, are good candidates for analysis. We have developed several image processing tools to aid in the selection of such capillaries for analysis and to obtain quick overviews of RBC flow through the microcirculation. METHODS: Burgess et al. (Microcirc. 2:75, 1995) and Burkell et al. (Annals Biomed. Eng. 24:1, 1996; J. Vasc. Res. 35:2, 1998) have previously introduced mean and variance images to aid in the selection of capillaries for analysis. We have extended their concept and developed similar two dimensional visualization techniques for studies of RBC flow through capillary networks. RESULTS: Five new methods of processing video data were developed. The minimum image highlights all capillaries containing RBCs in a given field of view. The maximum image identifies capillaries that exhibit high lineal density or stopped flow. The range image represents the difference between the maximum and minimum light intensity values that occur at a given pixel over a given time period, and helps to identify capillary segments that are in good focus and are perfused by RBCs and plasma. The difference image represents the cumulative sum of the square of differences in intensity values between consecutive frames and gives an indication of the frequency of passage of RBCs separated by plasma gaps. The transition image represents the number of times the intensity at a given pixel crosses a predefined threshold and indicates the number of RBCs (or trains of RBCs) that passes a given location during the observation period. CONCLUSIONS: The above flow visualization techniques are valuable tools to aid in the study of image focus, network geometry, RBC flow paths and dynamics, that can then be used in identifying capillaries for subsequent (separate) detailed analysis to provide quantitative information about RBC flow.

Algorithms↗