Structured Learning for 3-D Perivascular Space Segmentation Using Vascular Features

IEEE Trans Biomed Eng. 2017 Dec;64(12):2803-2812. doi: 10.1109/TBME.2016.2638918. Epub 2017 Mar 1.

Abstract

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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Brain / diagnostic imaging*
  • Decision Trees
  • Humans
  • Imaging, Three-Dimensional / methods*
  • Machine Learning
  • Magnetic Resonance Imaging / methods*