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Proceedings Paper

Automatic estimation of heart boundaries and cardiothoracic ratio from chest x-ray images
Author(s): Ahmed H. Dallal; Chirag Agarwal; Mohammad R. Arbabshirani; Aalpen Patel; Gregory Moore
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Paper Abstract

Cardiothoracic ratio (CTR) is a widely used radiographic index to assess heart size on chest X-rays (CXRs). Recent studies have suggested that also two-dimensional CTR might contain clinical information about the heart function. However, manual measurement of such indices is both subjective and time consuming. This study proposes a fast algorithm to automatically estimate CTR indices based on CXRs. The algorithm has three main steps: 1) model based lung segmentation, 2) estimation of heart boundaries from lung contours, and 3) computation of cardiothoracic indices from the estimated boundaries. We extended a previously employed lung detection algorithm to automatically estimate heart boundaries without using ground truth heart markings. We used two datasets: a publicly available dataset with 247 images as well as clinical dataset with 167 studies from Geisinger Health System. The models of lung fields are learned from both datasets. The lung regions in a given test image are estimated by registering the learned models to patient CXRs. Then, heart region is estimated by applying Harris operator on segmented lung fields to detect the corner points corresponding to the heart boundaries. The algorithm calculates three indices, CTR1D, CTR2D, and cardiothoracic area ratio (CTAR). The method was tested on 103 clinical CXRs and average error rates of 7.9%, 25.5%, and 26.4% (for CTR1D, CTR2D, and CTAR respectively) were achieved. The proposed method outperforms previous CTR estimation methods without using any heart templates. This method can have important clinical implications as it can provide fast and accurate estimate of cardiothoracic indices.

Paper Details

Date Published: 3 March 2017
PDF: 10 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101340K (3 March 2017); doi: 10.1117/12.2254136
Show Author Affiliations
Ahmed H. Dallal, Geisinger Health System (United States)
Univ. of Pittsburgh (United States)
Chirag Agarwal, Geisinger Health System (United States)
Univ. of Illinois at Chicago (United States)
Mohammad R. Arbabshirani, Geisinger Health System (United States)
Aalpen Patel, Geisinger Health System (United States)
Gregory Moore, Geisinger Health System (United States)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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