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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
Published on: September 25, 2019
Statistical modeling and knowledge-based segmentation of cerebral artery based on TOF-MRA and MR-T1
Na Li1, Shoujun Zhou2, Zonghan Wu1
1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Shenzhen Colleges of Advanced Technology, University of Chinese Academy of Sciences, Beijing, China.
Insights
This study presents a novel method for accurate cerebrovascular segmentation and cerebral artery and vein (CA/CV) separation using time-of-flight magnetic resonance angiography (TOF-MRA). The approach achieves high accuracy and efficient separation, paving the way for improved computer-assisted interventions.
Area of Science:
- Medical Imaging
- Neuroscience
- Computer Vision
Background:
- Cerebrovascular segmentation from TOF-MRA faces challenges in accuracy, coverage, and artery/vein separation.
- Existing methods lack a complete solution for precise cerebral artery segmentation.
Purpose of the Study:
- To develop a novel method for accurate cerebrovascular segmentation from TOF-MRA.
- To achieve efficient separation of cerebral arteries and veins (CA/CV).
- To enhance computer-assisted cerebrovascular interventions.
Main Methods:
- Skull-stripping and Hessian-based feature extraction for vascular prior knowledge.
- Intensity- and shape-based Markov statistical modeling with Gaussian mixture models.
- Automatic Markov regularization parameter estimation using machine learning.
- CA/CV separation via morphological logic operations based on topological relationships.
Main Results:
- Achieved an average Dice similarity coefficient of 0.933 for cerebrovascular segmentation.
- Reported average CA/CV separation agreement of 0.976.
- Demonstrated superior performance in segmenting small vessels in low-contrast regions.
- Quantitative metrics: FNR 0.158%, FPR 0.091% for segmentation; FNR 0.041%, FPR 0.022% for CA/CV separation.
Conclusions:
- The proposed method yields satisfying visual and quantitative results.
- Accurate cerebrovascular segmentation and efficient CA/CV separation were achieved.
- The method supports valuable clinical applications in computer-assisted cerebrovascular intervention.
Background And Objective:
For cerebrovascular segmentation from time-of-flight (TOF) magnetic resonance angiography (MRA), the focused issues are segmentation accuracy, vascular network coverage ratio, and cerebral artery and vein (CA/CV) separation. Therefore, cerebral artery segmentation is a challenging work, while a complete solution is lacking so far.
Methods:
The preprocessing of skull-stripping and Hessian-based feature extraction is first implemented to acquire an indirect prior knowledge of vascular distribution and shape. Then, a novel intensity- and shape-based Markov statistical modeling is proposed for complete cerebrovascular segmentation, where our low-level process employs a Gaussian mixture model to fit the intensity histogram of the skull-stripped TOF-MRA data, while our high-level process employs the vascular shape prior to construct the energy function. To regularize the individual data processes, Markov regularization parameter is automatically estimated by using a machine-learning algorithm. Further, cerebral artery and vein (CA/CV) separation is explored with a series of morphological logic operations, which are based on a direct priori knowledge on the relationship of arteriovenous topology and brain tissues in between TOF-MRA and MR-T1.
Results:
We employed 109 sets of public datasets from MIDAS for qualitative and quantitative assessment. The Dice similarity coefficient, false negative rate (FNR), and false positive rate (FPR) of 0.933, 0.158, and 0.091% on average, as well as CA/CV separation results with the agreement, FNR, and FPR of 0.976, 0.041, and 0.022 on average. For clinical visual assessment, our methods can segment various sizes of the vessel in different contrast region, especially performs better on vessels of small size in low contrast region.
Conclusion:
Our methods obtained satisfying results in visual and quantitative evaluation. The proposed method is capable of accurate cerebrovascular segmentation and efficient CA/CV separation. Further, it can stimulate valuable clinical applications on the computer-assisted cerebrovascular intervention according to the neurosurgeon's recommendation.
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