A study investigating automated quantitative analyses of coronary multidetector computed tomography images

Mark Eric Olszewski; Andreas Wahle; Divya Khullar; Krishna Subramanyan; Milan Sonka

doi:10.1117/12.599774

14 April 2005 A study investigating automated quantitative analyses of coronary multidetector computed tomography images

Mark Eric Olszewski, Andreas Wahle, Divya Khullar, Krishna Subramanyan, Milan Sonka

Author Affiliations +

Proceedings Volume 5746, Medical Imaging 2005: Physiology, Function, and Structure from Medical Images; (2005) https://doi.org/10.1117/12.599774
Event: Medical Imaging, 2005, San Diego, California, United States

Abstract

With the recent, rapid development of multidetector computed tomography (MDCT), excitement has built around the possibility of noninvasively imaging the coronary arteries. While the development of hardware and reconstruction technologies have advanced significantly, current image analysis techniques are dominated by manual interpretation using maximum intensity projections and volume rendering. If MDCT is to become the tool that it aims to be, objective, quantitative methods of image analysis will be necessary - not only to facilitate the study of atherosclerosis and coronary heart disease, but also for the accurate and timely interpretation of clinical data. This study focuses on the interobserver variability associated with the analysis of coronary MDCT images and a method for automatic segmentation of the same images. In the study of interobserver variability, six independent experts manually traced the luminal border in 60 randomly selected vascular cross sections (5 cross section each from: 4 LAD, 4 LCX, and 4 RCA). The images were acquired with an Mx8000 IDT 16-slice MDCT scanner. The mean unsigned difference for all observers was 0.38 ± 0.26 mm, with an average maximum difference of 1.32 mm. Using the expertly identified luminal borders, an independent standard was created by averaging the six sets of contours. This standard was then used to validate a prototypical automated segmentation system that uses dynamic programming and a knowledge-based cost function to optimally segment the luminal border. The resulting border positioning error was 0.17 ± 0.12 mm.

Citation Download Citation

Mark Eric Olszewski, Andreas Wahle, Divya Khullar, Krishna Subramanyan, and Milan Sonka "A study investigating automated quantitative analyses of coronary multidetector computed tomography images", Proc. SPIE 5746, Medical Imaging 2005: Physiology, Function, and Structure from Medical Images, (14 April 2005); https://doi.org/10.1117/12.599774

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