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Learning-Based 3T Brain MRI Segmentation with Guidance from 7T MRI Labeling

  • Renping Yu
  • Minghui Deng
  • Pew-Thian Yap
  • Zhihui Wei
  • Li Wang
  • Dinggang ShenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10019)

Abstract

Brain magnetic resonance image segmentation is one of the most important tasks in medical image analysis and has considerable importance to the effective use of medical imagery in clinical and surgical setting. In particular, the tissue segmentation of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is crucial for brain measurement and disease diagnosis. A variety of studies have shown that the learning-based techniques are efficient and effective in brain tissue segmentation. However, the learning-based segmentation methods depend largely on the availability of good training labels. The commonly used 3T magnetic resonance (MR) images have insufficient image quality and often exhibit poor intensity contrast between WM, GM, and CSF, therefore not able to provide good training labels for learning-based methods. The advances in ultra-high field 7T imaging make it possible to acquire images with an increasingly high level of quality. In this study, we propose an algorithm based on random forest for segmenting 3T MR images by introducing the segmentation information from their corresponding 7T MR images (through semi-automatic labeling). Furthermore, our algorithm iteratively refines the probability maps of WM, GM, and CSF via a cascade of random forest classifiers to improve the tissue segmentation. Experimental results on 10 subjects with both 3T and 7T MR images in a leave-one-out validation, show that the proposed algorithm performs much better than the state-of-the-art segmentation methods.

Keywords

Random Forest Random Forest Classifier Tissue Segmentation Deep Convolutional Neural Network Segmentation Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This work was supported by the China Scholarship Council (No. 201506840071) and the Research Fund for the Doctoral Program of Higher Education of China (RFDP) (No. 20133219110029).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Renping Yu
    • 1
    • 2
  • Minghui Deng
    • 3
  • Pew-Thian Yap
    • 2
  • Zhihui Wei
    • 1
  • Li Wang
    • 2
  • Dinggang Shen
    • 2
    Email author
  1. 1.School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjingChina
  2. 2.Department of Radiology and BRICUNC at Chapel HillChapel HillUSA
  3. 3.College of Electrical and InformationNortheast Agricultural UniversityHarbinChina

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