Abstract
Among the model-driven segmentation methods, the Maximum a Posterior (MAP) & Markov Random Field (MRF) is the popular statistical framework. However, there remains a dominating limitation in the existing statistical modeling, i.e., the data imaged by MR scanners with different types and parameters cannot be adaptively processed to lead accurate and robust vessel segmentation, as is well-known to the researchers in this field. Our methodology steps contribute as: (1) a region-histogram standardization strategy is explored to the time-of-flight magnetic resonance angiography data; (2) a Gaussian mixture models (GMM) is constructed with three Gaussian distributions and a knowledge-based expectation-maximization algorithm is explored to obtain the GMM parameters; (3) a probability feature map is captured according the estimated vascular distribution weight in GMM and then is embedded into the Markov high-level process to relieve the label field noise and rich the vascular structure. Our method wins out the other models with better segmentation accuracy and the sensibility to small-sized vessels or large arteriovenous malformation mass, which is validated on three different datasets and obtains satisfying results on visual and quantitative evaluation with Dice similarity coefficient and positive predictive value of 89.12% and 95.66%.
This work was funded by the National Natural Science Foundation of China (No. 81827805), National Key R&D Program of China (No. 2018YFA0704102) and supported by the Key Laboratory of Health Informatics in Chinese Academy of Sciences, and also by Shenzhen Engineering Laboratory for Key Technology on Intervention Diagnosis and Treatment Integration.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhao, F.J., et al.: Semi-supervised cerebrovascular segmentation by hierarchical convolutional neural network. IEEE Access 6, 67841–67852 (2018)
Moccia, S., et al.: Blood vessel segmentation algorithms—review of methods, datasets and evaluation metrics. Comput. Methods Programs Biomed. 158, 71–91 (2018)
Sato, Y., et al.: Tissue classification based on 3D local intensity structures for volume rendering. IEEE Trans. Visual Comput. Graphics 6, 160–180 (2000)
Jerman, T., et al.: Enhancement of vascular structures in 3D and 2D angiographic images. IEEE Trans. Med. Imaging 35, 2107–2118 (2016)
Wilson, D.L., et al.: An adaptive segmentation algorithm for time-of-flight MRA data. IEEE Trans. Med. Imaging 18, 938–945 (1999)
Wen, L., et al.: A novel statistical cerebrovascular segmentation algorithm with particle swarm optimization. Neurocomputing 148, 569–577 (2015)
Hassouna, M.S., et al.: Cerebrovascular segmentation from TOF using stochastic models. Med. Image Anal. 10, 2–18 (2006)
Zhou, S.J., et al.: Segmentation of brain magnetic resonance angiography images based on MAP–MRF with multi-pattern neighborhood system and approximation of regularization coefficient. Med. Image Anal. 17, 1220–1235 (2013)
Lu, P., et al.: A vessel segmentation method for multi-modality angiographic images based on multi-scale filtering and statistical models. Biomed. Eng. Online 15, 120 (2016)
Smith, S.M.: Fast robust automated brain extraction. Hum. Brain Mapp. 17, 143–155 (2002)
Bullitt, E., et al.: Vessel tortuosity and brain tumor malignancy: a blinded study1. Acad. Radiol. 12, 1232–1240 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, B., Wu, Z., Liu, S., Zhou, S., Li, N., Zhao, G. (2019). A Device-Independent Novel Statistical Modeling for Cerebral TOF-MRA Data Segmentation. In: Greenspan, H., et al. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures. CLIP UNSURE 2019 2019. Lecture Notes in Computer Science(), vol 11840. Springer, Cham. https://doi.org/10.1007/978-3-030-32689-0_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-32689-0_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32688-3
Online ISBN: 978-3-030-32689-0
eBook Packages: Computer ScienceComputer Science (R0)