Filling Large Discontinuities in 3D Vascular Networks Using Skeleton- and Intensity-Based Information
Segmentation of vasculature is a common task in many areas of medical imaging, but complex morphology and weak signal often lead to incomplete segmentations. In this paper, we present a new gap filling strategy for 3D vascular networks. The novelty of our approach is to combine both skeleton- and intensity-based information to fill large discontinuities. Our approach also does not make any hypothesis on the network topology, which is particularly important for tumour vasculature due to the chaotic arrangement of vessels within tumours. Synthetic results show that using intensity-based information, in addition to skeleton-based information, can make the detection of large discontinuities more robust. Our strategy is also shown to outperform a classic gap filling strategy on 3D Micro-CT images of preclinical tumour models.
KeywordsTumour Vasculature Path Search Intensity Information Tensor Model Medical Image Analysis
Unable to display preview. Download preview PDF.
- 9.Pock, T., Janko, C., Beichel, R., Bischof, H.: Multiscale medialness for robust segmentation of 3-d tubular structures. In: Proc. CVW Workshop (2005)Google Scholar
- 12.Schneider, M., Hirsch, S., Weber, B., Székely, G., Menze, B.H.: TGIF: Topological Gap In-Fill for Vascular Networks. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part II. LNCS, vol. 8674, pp. 89–96. Springer, Heidelberg (2014)Google Scholar
- 13.Szymczak, A., Tannenbaum, A., Mischaikow, K.: Coronary vessel cores from 3-d imagery: A topological approach. In: Proc. SPIE Med. Imag. (2005)Google Scholar