TY - JOUR
T1 - Structured Learning for 3-D Perivascular Space Segmentation Using Vascular Features
AU - Zhang, Jun
AU - Gao, Yaozong
AU - Park, Sang Hyun
AU - Zong, Xiaopeng
AU - Lin, Weili
AU - Shen, Dinggang
N1 - Funding Information:
Manuscript received July 21, 2016; revised October 28, 2016; accepted December 9, 2016. Date of publication March 1, 2017; date of current version November 20, 2017. This work was supported by the National Institutes of Health under Grant EB006733, Grant EB008374, Grant EB009634, Grant MH100217, Grant AG041721, Grant AG049371, Grant AG042599, and Grant NS095027. (Corresponding author: Dinggang Shen.) J. Zhang, S. H. Park, X. Zong, and W. Lin are with the Department of Radiology and BRIC, University of North Carolina at Chapel Hill.
Publisher Copyright:
© 1964-2012 IEEE.
PY - 2017/12
Y1 - 2017/12
N2 - Objective: The goal of this paper is to automatically segment perivascular spaces (PVSs) in brain from high-resolution 7T magnetic resonance (MR) images. Methods: We propose a structured-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 two categories, i.e., PVS and background. In addition, we propose a novel entropy-based sampling strategy to extract informative samples in the background for training an explicit classification model. Since the vascular filters can extract various vascular features, even thin and low-contrast structures can be effectively extracted from noisy backgrounds. Moreover, continuous and smooth segmentation results can be obtained by utilizing patch-based structured labels. Results: The performance of our proposed method is evaluated on 19 subjects with 7T MR images, with the Dice similarity coefficient reaching 66%. Conclusion: The joint use of entropy-based sampling strategy, vascular features, and structured learning can improve the segmentation accuracy. Significance: Instead of manual annotation, our method provides an automatic way for PVS segmentation. Moreover, our method can be potentially used for other vascular structure segmentation because of its data-driven property.
AB - Objective: The goal of this paper is to automatically segment perivascular spaces (PVSs) in brain from high-resolution 7T magnetic resonance (MR) images. Methods: We propose a structured-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 two categories, i.e., PVS and background. In addition, we propose a novel entropy-based sampling strategy to extract informative samples in the background for training an explicit classification model. Since the vascular filters can extract various vascular features, even thin and low-contrast structures can be effectively extracted from noisy backgrounds. Moreover, continuous and smooth segmentation results can be obtained by utilizing patch-based structured labels. Results: The performance of our proposed method is evaluated on 19 subjects with 7T MR images, with the Dice similarity coefficient reaching 66%. Conclusion: The joint use of entropy-based sampling strategy, vascular features, and structured learning can improve the segmentation accuracy. Significance: Instead of manual annotation, our method provides an automatic way for PVS segmentation. Moreover, our method can be potentially used for other vascular structure segmentation because of its data-driven property.
KW - 7T magnetic resonance (MR) images
KW - Perivascular spaces (PVSs)
KW - segmentation
KW - structured random forest (SRF)
KW - vascular features
UR - http://www.scopus.com/inward/record.url?scp=85040444235&partnerID=8YFLogxK
U2 - 10.1109/TBME.2016.2638918
DO - 10.1109/TBME.2016.2638918
M3 - Article
C2 - 28362579
AN - SCOPUS:85040444235
VL - 64
SP - 2803
EP - 2812
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
SN - 0018-9294
IS - 12
M1 - 7865910
ER -