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Bates, R.* ; Risser, L.* ; Irving, B.* ; Papiez, B.W.* ; Kannan, P.* ; Kersemans, V.* ; Schnabel, J.A.*

Filling large discontinuities in 3D vascular networks using skeleton- and intensity-based information.

In: (International Conference on Medical Image Computing and Computer-Assisted Intervention). 2015. 157-164 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 9351)
DOI
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.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel International Conference on Medical Image Computing and Computer-Assisted Intervention
Quellenangaben Band: 9351, Heft: , Seiten: 157-164 Artikelnummer: , Supplement: ,
Nichtpatentliteratur Publikationen
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)