Automatic Vessel Segmentation from Pulsatile Radial Distension
Identification of vascular structures from medical images is integral to many clinical procedures. Most vessel segmentation techniques ignore the characteristic pulsatile motion of vessels in that formulation. In a recent effort to automatically segment vessels that are hidden under fat, we motivated the use of the magnitude of local pulsatile motion extracted from surgical endoscopic video. In this paper we propose a new approach that leverages the local orientation, in addition to magnitude of motion, and demonstrate that the extended computation of motion vectors can improve the segmentation of vascular structures. We implement our approach using two alternatives to magnitude-only motion estimation by using traditional optical flow and by exploiting the monogenic signal for fast flow estimation. Our evaluations are conducted on both synthetic phantoms as well as real ultrasound data showing improved segmentation results (0.36 increase in DSC and 0.11 increase in AUC) with negligible change in computational performance.
KeywordsOptical Coherence Tomography Motion Vector Local Orientation Vessel Segmentation Monogenic Signal
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- 10.Amir-Khalili, A., Hamarneh, G., Peyrat, J.M., Abinahed, J., Al-Alao, O., Al-Ansari, A., Abugharbieh, R.: Automatic segmentation of occluded vasculature via pulsatile motion analysis in endoscopic robot-assisted partial nephrectomy video. Medical Image Analysis (2015)Google Scholar
- 12.Felsberg, M.: Optical flow estimation from monogenic phase. In: Complex Motion, pp. 1–13 (2007)Google Scholar
- 13.Sun, D., Roth, S., Black, M.J.: Secrets of optical flow estimation and their principles. In: IEEE Computer Vision and Pattern Recognition, pp. 2432–2439 (2010)Google Scholar
- 15.Wachinger, C., Klein, T., Navab, N.: The 2D analytic signal on RF and B-mode ultrasound images. In: Information Processing in Medical Imaging, pp. 359–370 (2011)Google Scholar
- 16.Chenouard, N., Unser, M.: 3D steerable wavelets and monogenic analysis for bioimaging. In: IEEE International Symposium on Biomedical Imaging, pp. 2132–2135 (2011)Google Scholar