Retinal vascular tree morphology: a semi-automatic quantification

An automated method for analysis of flow characteristics of circulating particles from in vivo video microscopy

The behavior of white and red blood cells, platelets, and circulating injected particles is one of the most studied areas of physiology. Most methods used to analyze the circulatory patterns of cells are time consuming. We describe a system named CellTrack, designed for fully automated tracking of circulating cells and micro-particles and retrieval of their behavioral characteristics. The task of automated blood cell tracking in vessels from in vivo video is particularly challenging because of the blood cells' nonrigid shapes, the instability inherent in in vivo videos, the abundance of moving objects and their frequent superposition. To tackle this, the CellTrack system operates on two levels: first, a global processing module extracts vessel borders and center lines based on color and temporal patterns. This enables the computation of the approximate direction of the blood flow in each vessel. Second, a local processing module extracts the locations and velocities of circulating cells. This is performed by artificial neural network classifiers that are designed to detect specific types of blood cells and micro-particles. The motion correspondence problem is then resolved by a novel algorithm that incorporates both the local and the global information. The system has been tested on a series of in vivo color video recordings of rat mesentery. Our results show that the synergy between the global and local information enables CellTrack to overcome many of the difficulties inherent in tracking methods that rely solely on local information. A comparison was made between manual measurements and the automatically extracted measurements of leukocytes and fluorescent microspheres circulatory velocities. This comparison revealed an accuracy of 97%. CellTrack also enabled a much larger volume of sampling in a fraction of time compared to the manual measurements. All these results suggest that our method can in fact constitute a reliable replacement for manual extraction of blood flow characteristics from in vivo videos.

Semiautomated computer analysis of vessel growth in preterm infants without and with ROP

AIMS

To measure characteristics of the retinal blood vessels close to the optic disc in full term and preterm infants, with and without retinopathy of prematurity (ROP), using digital imaging. To determine whether these measures are indicative of the presence or severity of ROP in the retinal periphery.

METHODS

52 digital fundus images from 42 babies were analysed with a semiautomated analysis program developed at Imperial College London. Analysis was limited to the principal temporal vessels close to the optic disc: recording venular diameter and arteriolar diameter and tortuosity.

RESULTS

Each result was categorised by the gestational age of the infant ("very premature" 24-27 weeks, "moderately premature" 28-31 weeks, and "near term" > or =32 weeks) and by the highest stage of ROP present ("no ROP," "mild ROP" stage 1 or 2, and "severe ROP" stage 3). Arteriolar tortuosity was found to vary significantly (Kruskal-Wallis p=0.002) with ROP severity. Although venular and arteriolar diameters increased monotonically with ROP severity the differences were not significant. Venular diameter, arteriolar diameter, and arterial tortuosity did not vary significantly between gestational age groups.

CONCLUSIONS

This study confirms it is possible to quantify the size and tortuosity of retinal blood vessels in term and preterm babies using digital image analysis software. This method detected significant increases in arteriolar tortuosity with increasing ROP severity.