Vascular Segmentation
Vascular Segmentation - Related Videos
03:24
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08:18
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Cited by 6 •
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04:41
Laparoscopic Left Liver Sectoriectomy of Caroli's Disease Limited to Segment II and III
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Cited by 5 •
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07:56
A Full Skin Defect Model to Evaluate Vascularization of Biomaterials In Vivo
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Cited by 13 •
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Vascularization is key to approaches in successful tissue engineering. Therefore, reliable technologies are required to evaluate the development of vascular networks in tissue-constructs. Here we present a simple and cost-effective method to visualize and quantify vascularization in vivo.
11:38
Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench
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Cited by 10 •
2017
Segmentation of three-dimensional data from many imaging techniques is a major bottleneck in analysis of complex biological systems. Here, we describe the use of SuRVoS Workbench to semi-automatically segment volumetric data at various length-scales using example datasets from cryo-electron tomography, cryo soft X-ray tomography, and phase contrast X-ray tomography techniques.
FAQs
Preprocessing and contrast enhancement improve the separation between vessel signal and surrounding tissue before segmentation begins. These steps can make vessel structures more distinguishable, allowing later thresholding, region growing, or classification to identify candidate pixels or voxels. Their importance is practical: clearer image information can support more consistent delineation and more reliable downstream vascular measurements.
Thresholding assigns image pixels or voxels to vessel or non-vessel categories according to image values, whereas region growing expands a selected area through connected vessel information. Machine-learning classification instead learns to distinguish vessel structures from surrounding tissue. Choosing among these approaches depends on how vessel appearance is represented in the medical image and may affect the resulting vascular map.
Boundary refinement and connectivity checks address two different sources of segmentation error. Refinement improves the placement of vessel edges, while connectivity preserves links among branching segments and helps prevent breaks in the vascular map. Both matter because measurements and interpretation depend not only on which locations are labeled as vessels, but also on whether the mapped network retains its anatomical structure.
A typical workflow begins with image preprocessing, continues with contrast enhancement and vessel identification, and then applies boundary refinement and connectivity handling. The resulting map can be used to calculate vessel diameter, length, branching, and tortuosity. Keeping these stages distinct helps connect image processing decisions with the quantitative vascular features ultimately used in medical analysis.
Vascular segmentation supports assessment of stenosis, aneurysms, and other vascular abnormalities by providing a delineated structure from which vessel geometry can be quantified. Measurements such as diameter, length, branching, and tortuosity give researchers and clinicians structured information rather than relying only on visual inspection. This makes the method relevant to both diagnosis-oriented analysis and research studies.
In medicine, the resulting vascular maps can inform surgical and endovascular planning by showing the relevant vascular anatomy in a structured form. They also provide quantitative inputs for computational modeling and clinical studies. These uses extend the value of segmentation beyond image display: the same delineated anatomy can support procedural preparation, analysis of vascular structure, and investigation across patient data.