A number of investigators have demonstrated the potential of preclinical micro-CT in characterizing cardiovascular disease in mouse models. One major hurdle to advancing this approach is the extensive user interaction required to derive quantitative metrics from these 4D image arrays (space + time). In this work, we present: (1) a method for constructing an average anatomic cardiac atlas of the mouse based on 4D micro-CT images, (2) a fully automated approach for segmenting newly acquired cardiac data sets using the atlas, and (3) a quantitative characterization of atlas-based segmentation accuracy and consistency. Employing the deformable registration toolkit, ANTs, the construction of minimal deformation fields, and a novel adaptation of joint bilateral filtration, our atlas construction scheme was used to integrate 6, C57BL/6 cardiac micro-CT data sets, reducing the noise standard deviation from ~70 HU in the individual data sets to ~21 HU in the atlas data set. Using the segmentation tools in Atropos and our atlas-based segmentation, we were able to propagate manual labels to 5, C57BL/6 data sets not used in atlas construction. Average Dice coefficients and volume accuracies (respectively) over phases 1 (ventricular diastole), 3, and 5 (ventricular systole) of these 5 data sets were as follows: left ventricle, 0.96, 0.96; right ventricle, 0.89, 0.92; left atrium, 0.88, 0.89; right atrium, 0.86, 0.92; myocardium, 0.90, 0.94. Once the atlas was constructed and segmented, execution of the proposed automated segmentation scheme took ~6.5 hours per data set, versus more than 50 hours required for a manual segmentation.

Date Published: 13 March 2013
PDF: 12 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86691P (13 March 2013); doi: 10.1117/12.2007043

Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)