SU-C-18A-06: Tracking Fuzzy Border Using Geodesic Curve and Its Application to Liver Segmentation On Planning CT

Authors


Abstract

Purpose:

To investigate the feasibility of using geodesic curves to track fuzzy borders between liver and chest wall and to evaluate how it could improve the performance of automatic liver segmentation on treatment planning CT images.

Methods:

The performance of automatic liver segmentation usually suffers from the fuzzy borders between liver and the adjacent chest wall due to similar HU values on non-contrast-enhanced planning CT images. To address this issue, geodesic curves were used to track these fuzzy borders. We first constructed a horizontal gradient map on the coronal-view images. After automatically identifying one starting and one ending point, a minimal distance map (MDM) was constructed by evolving a front starting from an infinitesimal circle around the ending point to every pixel on the image. The value of each point in MDM represents the minimal distance from the point to the ending point, which combines both arc length and gradient magnitude along the path. The front evolution was numerically solved by fast-marching method. We coupled this method with our automatic liver segmentation method, in which an initial contour was firstly estimated by adaptive thresholding and then a distance-regularized geodesic active contour model was used to further refine the contour. Besides visual assessment, dice similarity coefficient (DSC) was calculated to quantitatively compare the computer-generated contours with manual outlines.

Results:

This study included five patients with 597 CT slices who received SBRT liver treatment. We observed clear separation between liver and chest wall after delineation. The results of automatic segmentation were in excellent agreement with manual outlines, yielding all DSCs higher than 0.9 with mean of 0.93.

Conclusion:

The preliminary results demonstrate that geodesic curve can be used to track fuzzy borders between liver and chest wall, and thus to improve the performance of automatic liver segmentation on planning CT images.

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