3-D quantification and visualization of vascular structures from confocal microscopic images using skeletonization and voxel-coding

Soltanian-Zadeh, Hamid; Shahrokni, Ali; Khalighi, Mohammad-Mehdi; Zhang, Zheng G.; Zoroofi, Reza Aghaeizadeh; Maddah, Mahnaz; Chopp, Michael

doi:10.1016/j.compbiomed.2004.06.009

Cited by 13 publications

(7 citation statements)

References 18 publications

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“…This simple approach provided clean volumes devoid of random noise but other procedures can be used to obtain binary representations of tumor vessel networks [35]. For each binary volume, identified voxels were divided among several stacks in agreement with the approximate cross-sectional area of the microvessel they represented.…”

Section: Resultsmentioning

confidence: 99%

D Quantification of Tumor Vasculature in Lymphoma Xenografts in NOD/SCID Mice Allows to Detect Differences among Vascular-Targeted Therapies

et al. 2013

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Quantitative characterization of the in vivo effects of vascular-targeted therapies on tumor vessels is hampered by the absence of useful 3D vascular network descriptors aside from microvessel density. In this study, we extended the quantification of planar vessel distribution to the analysis of vascular volumes by studying the effects of antiangiogenic (sorafenib and sunitinib) or antivascular (combretastatin A4 phosphate) treatments on the quantity and spatial distributions of thin microvessels. These observations were restricted to perinecrotic areas of treated human multiple myeloma tumors xenografted in immunodeficient mice and to microvessels with an approximate cross-sectional area lower than 75 µm2. Finally, vessel skeletonization minimized artifacts due to possible differential wall staining and allowed a comparison of the various treatment effects. Antiangiogenic drug treatment reduced the number of vessels of every caliber (at least 2-fold fewer vessels vs. controls; p<0.001, n = 8) and caused a heterogeneous distribution of the remaining vessels. In contrast, the effects of combretastatin A4 phosphate mainly appeared to be restricted to a homogeneous reduction in the number of thin microvessels (not more than 2-fold less vs. controls; p<0.001, n = 8) with marginal effects on spatial distribution. Unexpectedly, these results also highlighted a strict relationship between microvessel quantity, distribution and cross-sectional area. Treatment-specific changes in the curves describing this relationship were consistent with the effects ascribed to the different drugs. This finding suggests that our results can highlight differences among vascular-targeted therapies, providing hints on the processes underlying sample vascularization together with the detailed characterization of a pathological vascular tree.

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Section: Resultsmentioning

confidence: 99%

D Quantification of Tumor Vasculature in Lymphoma Xenografts in NOD/SCID Mice Allows to Detect Differences among Vascular-Targeted Therapies

et al. 2013

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“…These include “medialness” measures employing Dijkstra's algorithm, Frangi “vesselness” with Dijkstra, and tubular‐likelihood measures with Dijkstra . Another related approach is voxel‐coding‐based shortest path algorithms, where a boundary‐seeded code is used as the cost metric and a single‐seeded code is used to determine the medial points from the image. A minimum path algorithm can then be used to connect these extracted medial points.…”

Section: Image Processingmentioning

confidence: 99%

Reconstruction of coronary circulation networks: A review of methods

et al. 2019

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Building anatomically accurate models of the coronary vascular system enables potentially deeper understandings of coronary circulation. To achieve this, (a) images at different levels of vascular network-arteries, arterioles, capillaries, venules, and veins-need to be obtained through suitable imaging modalities; and (b) from images, morphological and topological information needs to be extracted using image processing techniques. While there are several modalities that enable the imaging of large vessels, microcirculation imaging-capturing vessels having diameter lesser than 100 μm-has to date been typically confined to small regions of the heart. This spatially limited microcirculatory information has often been used within cardiac models, with the potentially erroneous assumption that it is representative of the whole organ. However, with the recent advancements in imaging and image processing, it is rapidly becoming feasible to acquire, process, and quantify microcirculation data at the scale of whole organ. In this review, we summarize the progress toward this goal followed through a presentation of the current state-of-the-art imaging and image processing techniques in the context of coronary microcirculation extraction, prominently but not exclusively, from small animals. K E Y W O R D Sconfocal imaging, coronary vascular system, deep learning, image processing, vessel extraction

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“…In the field of medical applications, thinning is used for information extraction from 3D images and is usually a preliminary step in a sequence of operations on images: for example, thinning permits the reconstruction of 3D vascular trees [37,38]; skeletons may be considered as paths to guide cameras in Computed Tomography Colonography [39,40]; thinning permits the assessment of osteoporosis density studies [41].…”

Section: Thinning Algorithmsmentioning

confidence: 99%

Two symmetrical thinning algorithms for 3D binary images, based on -simple points

Lohou¹,

Bertrand²

2007

Pattern Recognition

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3-D quantification and visualization of vascular structures from confocal microscopic images using skeletonization and voxel-coding

Cited by 13 publications

References 18 publications

D Quantification of Tumor Vasculature in Lymphoma Xenografts in NOD/SCID Mice Allows to Detect Differences among Vascular-Targeted Therapies

D Quantification of Tumor Vasculature in Lymphoma Xenografts in NOD/SCID Mice Allows to Detect Differences among Vascular-Targeted Therapies

Reconstruction of coronary circulation networks: A review of methods

Two symmetrical thinning algorithms for 3D binary images, based on -simple points

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