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Automatic 3D segmentation and quantification of lenticulostriate arteries from high-resolution 7 Tesla MRA images

We propose a novel hybrid approach for automatic 3D segmentation and quantification of high-resolution 7 Tesla magnetic resonance angiography (MRA) images of the human cerebral vasculature. Our approach consists of two main steps. First, a 3D model-based approach is used to segment and quantify thic...

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Hauptverfasser: Liao, Wei (VerfasserIn) , Rohr, Karl (VerfasserIn) , Kang, Chang-Ki (VerfasserIn) , Cho, Zang-Hee (VerfasserIn) , Wörz, Stefan (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: February 2016
In: IEEE transactions on image processing
Year: 2016, Jahrgang: 25, Heft: 1, Pages: 400-413
ISSN:1941-0042
DOI:10.1109/TIP.2015.2499085
Online-Zugang:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1109/TIP.2015.2499085
Volltext
Verfasserangaben:Wei Liao, Karl Rohr, Chang-Ki Kang, Zang-Hee Cho, and Stefan Wörz

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520 |a We propose a novel hybrid approach for automatic 3D segmentation and quantification of high-resolution 7 Tesla magnetic resonance angiography (MRA) images of the human cerebral vasculature. Our approach consists of two main steps. First, a 3D model-based approach is used to segment and quantify thick vessels and most parts of thin vessels. Second, remaining vessel gaps of the first step in low-contrast and noisy regions are completed using a 3D minimal path approach, which exploits directional information. We present two novel minimal path approaches. The first is an explicit approach based on energy minimization using probabilistic sampling, and the second is an implicit approach based on fast marching with anisotropic directional prior. We conducted an extensive evaluation with over 2300 3D synthetic images and 40 real 3D 7 Tesla MRA images. Quantitative and qualitative evaluation shows that our approach achieves superior results compared with a previous minimal path approach. Furthermore, our approach was successfully used in two clinical studies on stroke and vascular dementia. 
650 4 |a 3D minimal path approach 
650 4 |a 3D model-based approach 
650 4 |a 3D synthetic imaging 
650 4 |a 3D vessel segmentation 
650 4 |a 7T MRA data 
650 4 |a automatic 3D segmentation 
650 4 |a biomedical MRI 
650 4 |a blood vessels 
650 4 |a Cerebral Cortex 
650 4 |a cerebral vasculature 
650 4 |a Data models 
650 4 |a Dementia, Vascular 
650 4 |a directional speed function 
650 4 |a energy minimization 
650 4 |a fast marching 
650 4 |a high-resolution 7 tesla magnetic resonance angiography imaging 
650 4 |a high-resolution 7 tesla MRA imaging 
650 4 |a human cerebral vasculature 
650 4 |a Humans 
650 4 |a image denoising 
650 4 |a image resolution 
650 4 |a image segmentation 
650 4 |a Image segmentation 
650 4 |a Imaging, Three-Dimensional 
650 4 |a lenticulostriate arteries 
650 4 |a low-contrast noisy regions 
650 4 |a magnetic flux density 7 tesla 
650 4 |a Magnetic Resonance Angiography 
650 4 |a medical disorders 
650 4 |a medical image processing 
650 4 |a Middle Cerebral Artery 
650 4 |a minimal path 
650 4 |a minimal path approach 
650 4 |a minimal path approaches 
650 4 |a Noise measurement 
650 4 |a parametric intensity model 
650 4 |a Probabilistic logic 
650 4 |a probabilistic sampling 
650 4 |a probability 
650 4 |a Shape 
650 4 |a Solid modeling 
650 4 |a Stroke 
650 4 |a stroke dementia 
650 4 |a thin vessels 
650 4 |a Three-dimensional displays 
650 4 |a vascular dementia 
650 4 |a vessel gaps 
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