Automatic Vessel Segmentation from Pulsatile Radial Distension

作者:Amir Khalili Alborz*; Hamarneh Ghassan; Abugharbieh Rafeef
来源:18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2015-10-05 to 2015-10-09.
DOI:10.1007/978-3-319-24574-4_48

摘要

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.

  • 出版日期2015

全文