3D segmentation of the ascending and descending aorta from CT data via graph-cuts

Jungwon Cha; Alexander Henn; Marcus Stoddard; Amir Amini

doi:10.1117/12.2295967

12 March 2018 3D segmentation of the ascending and descending aorta from CT data via graph-cuts

Jungwon Cha, Alexander Henn, Marcus Stoddard, Amir Amini

Proceedings Volume 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging; 105781R (2018) https://doi.org/10.1117/12.2295967
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

Segmentation of the aorta from CT and MR data is important in order to quantitatively assess diseases of the aorta including aortic dissection and distention of aortic aneurysm, among others. In this paper, we propose a segmentation method to extract exact the 3D boundary of the aorta via graph-cuts segmentation. The graph-cuts technique is able to avoid local minima with global optimization and can be applied to 3D and higher dimension with fast computation. We performed 3D segmentation using this method for five CT data sets. The user selects seed points for aorta region as 'object' and surrounding tissues as 'background' on an axial slice of the 3D CT data and the algorithm calculates the cost of n-link (neighborhood-link) and t-link (terminal-link), and computes the minimum cut separating the aorta from the background by applying the max-flow/min-cut algorithm. Results were validated against manually traced aorta boundaries. The mean Dice Similarity Coefficient for the five 3D segmentations was 0.9381. The 3D segmentation took less than five minutes for data sets of size 512×512×244 to 512×512×284.

Citation Download Citation

Jungwon Cha, Alexander Henn, Marcus Stoddard, and Amir Amini "3D segmentation of the ascending and descending aorta from CT data via graph-cuts", Proc. SPIE 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 105781R (12 March 2018); https://doi.org/10.1117/12.2295967

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