Automated Ultrasound Measurement of the Inferior Vena Cava: An Animal Study

Abstract

Because of continuous movement and variation in diameter of the inferior vena cava (IVC) with respiration, the measurements on IVC are labor-intensive and with considerable inter-operator variations. Some computer-assisted techniques have been developed to track the movement of the IVC semi-automatically. However, existing methods predominantly rely on reference marker selection and require many manual inputs. In this study, we developed a cross-correlation (CC)-based method for automated IVC movement tracking and measurement, which requires minimal manual input and avoids manual selection of reference markers. Based on the CC method, two approaches, named direct and relative approaches, were used to calculate the maximum, minimum, and variation of the IVC diameter, and compared with the manual measurement. Fifty-four ultrasound cine-loops collected from nine pigs were tested. The results reveal that both the proposed approaches were well agreed with the manual measurement. The errors of the direct approach were less than 9%, while those of relative approach were as high as 26.7%. It is concluded that the proposed direct approach is superior for IVC diameter measurements, which can be comparable with manual counterpart, serving as an alternative to traditional IVC measurement.

Automated Pleural Line Detection Based on Radon Transform Using Ultrasound

“…Many smart algorithms have been developed for automated ultrasound measurement for different parameters, for example, blood vessel diameter and wall thickness. 8,16 -23 Biswas et al 17 applied deep learning for automated lumen characterization of carotid artery using ultrasound. Our previous work focused on developing smart algorithms for automated inferior vena cava identifications 22 and measurements.…”

“…Our previous work focused on developing smart algorithms for automated inferior vena cava identifications 22 and measurements. 21 In the field of lung ultrasound, endeavors were focused on the detection of different lung ultrasound artifacts, for example, B-lines and pleural lines. Brusasco et al 8 and Moshavegh et al 24 attempted to provide quantitative measure for the number of B-lines present in lung ultrasound images.…”