Chapter title |
Segmentation of Perivascular Spaces Using Vascular Features and Structured Random Forest from 7T MR Image
|
---|---|
Chapter number | 8 |
Book title |
Machine Learning in Medical Imaging
|
Published in |
Lecture notes in computer science, October 2016
|
DOI | 10.1007/978-3-319-47157-0_8 |
Pubmed ID | |
Book ISBNs |
978-3-31-947156-3, 978-3-31-947157-0
|
Authors |
Jun Zhang, Yaozong Gao, Sang Hyun Park, Xiaopeng Zong, Weili Lin, Dinggang Shen, Zhang, Jun, Gao, Yaozong, Park, Sang Hyun, Zong, Xiaopeng, Lin, Weili, Shen, Dinggang |
Editors |
Li Wang, Ehsan Adeli, Qian Wang, Yinghuan Shi, Heung-Il Suk |
Abstract |
Quantitative analysis of perivascular spaces (PVSs) is important to reveal the correlations between cerebrovascular lesions and neurodegenerative diseases. In this study, we propose a learning-based segmentation framework to extract the PVSs from high-resolution 7T MR images. Specifically, we integrate three types of vascular filter responses into a structured random forest for classifying voxels into PVS and background. In addition, we also propose a novel entropy-based sampling strategy to extract informative samples in the background for training the classification model. Since various vascular features can be extracted by the three vascular filters, even thin and low-contrast structures can be effectively extracted from the noisy background. Moreover, continuous and smooth segmentation results can be obtained by utilizing the patch-based structured labels. The segmentation performance is evaluated on 19 subjects with 7T MR images, and the experimental results demonstrate that the joint use of entropy-based sampling strategy, vascular features and structured learning improves the segmentation accuracy, with the Dice similarity coefficient reaching 66 %. |
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Geographical breakdown
Country | Count | As % |
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United Kingdom | 1 | 8% |
Unknown | 11 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Master | 3 | 25% |
Student > Ph. D. Student | 3 | 25% |
Researcher | 3 | 25% |
Unspecified | 1 | 8% |
Student > Bachelor | 1 | 8% |
Other | 0 | 0% |
Unknown | 1 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 3 | 25% |
Computer Science | 2 | 17% |
Neuroscience | 1 | 8% |
Unspecified | 1 | 8% |
Unknown | 5 | 42% |