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General Surface Energy for Spinal Cord and Aorta Segmentation

H. Gupta, D. Schmitter, V. Uhlmann, M. Unser

Proceedings of the Fourteenth IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI'17), Melbourne, Commonwealth of Australia, April 18-21, 2017, pp. 319-322.



We present a new surface energy potential for the segmentation of cylindrical objects in 3D medical imaging using parametric spline active contours (a.k.a. spline-snakes). Our energy formulation is based on an optimal steerable surface detector. Thus, we combine the concept of steerability with spline-snakes that have open topology for semi-automatic segmentation. We show that the proposed energy yields segmentation results that are more robust to noise compared to classical gradient-based surface energies. We finally validate our model by segmenting the aorta on a cohort of 14 real 3D MRI images, and also provide an example of spinal cord segmentation using the same tool.


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