Filling Large Discontinuities in 3D Vascular Networks Using Skeleton- and Intensity-Based Information

Abstract

Abstract. 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.

Functional Parameters Derived from Magnetic Resonance Imaging Reflect Vascular Morphology in Preclinical Tumors and in Human Liver Metastases

“…Briefly, pre- and post-contrast scans were cropped to exclude non-tumor regions in the analysis, intensity normalized, co-registered using non-rigid registration, and then subtracted (Figure 1). Vessel structures were then segmented using a modified vesselness filter (27), and skeletonized using an iterative thinning algorithm (28) and an intensity-based gap filling model previously developed in our group (23). Because many cancer therapies are thought to normalize vascular structure in tumors, structural parameters such as vessel volume, branching points, diameter, length, and tortuosity were chosen to characterize morphological changes (1).…”

“…In this study, we attempt to tackle this shortcoming by measuring the 3D relationship between functional imaging parameters and structural vascular features in vivo to facilitate clinical utility. To achieve this aim, we used improved imaging (22) and 3D segmentation techniques (23) to extract functional parameters derived from DCE-MRI and structural parameters derived from contrast-enhanced CT performed in vivo . Relationships were first assessed in untreated tumors from two different preclinical models with high and low levels of vascularization.…”

Improved centerline tree detection of diseased peripheral arteries with a cascading algorithm for vascular segmentation

“…An iterative tensor voting strategy for identifying curvilinear structures in two-dimensional (2-D) images, primarily from microscopy, was introduced by Loss et al 16 However, it only applies to relatively small gaps. Bates et al 17 presented a strategy where they combine both skeleton-and intensity-based information to fill large discontinuities. Since the main goal of this method is to fill gaps originating from thresholding the vesselness likelihood map, the method may not be suitable for detecting collateral arteries.…”

Multiscale Topology Characterises Dynamic Tumour Vascular Networks

“…Topological data analysis offers great potential for relating the form and function of vascular networks, and proposing novel biomarkers for tumour progression and treatment.Introduction. The advent of high resolution imaging techniques has driven the development of reconstruction algorithms, which generate exquisitely detailed 3D renderings of biological tissues, such as tumour vascular networks 10,11 . Analyses of these images have quantified structural features and shape, including vessel density, number of vessels and branch points 7 , fractal dimension 12 , and lacunarity 13 , and highlighted their relevance for monitoring disease progression 14,15 and treatment 6 .…”