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Reproducible White Matter Tract Segmentation Using 3D U-Net on a Large-scale DTI Dataset

  • Bo Li
  • Marius de Groot
  • Meike W. Vernooij
  • M. Arfan Ikram
  • Wiro J. Niessen
  • Esther E. Bron
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11046)

Abstract

Tract-specific diffusion measures, as derived from brain diffusion MRI, have been linked to white matter tract structural integrity and neurodegeneration. As a consequence, there is a large interest in the automatic segmentation of white matter tract in diffusion tensor MRI data. Methods based on the tractography are popular for white matter tract segmentation. However, because of the limited consistency and long processing time, such methods may not be suitable for clinical practice. We therefore developed a novel convolutional neural network based method to directly segment white matter tract trained on a low-resolution dataset of 9149 DTI images. The method is optimized on input, loss function and network architecture selections. We evaluated both segmentation accuracy and reproducibility, and reproducibility of determining tract-specific diffusion measures. The reproducibility of the method is higher than that of the reference standard and the determined diffusion measures are consistent. Therefore, we expect our method to be applicable in clinical practice and in longitudinal analysis of white matter microstructure.

Keywords

White Matter Tract Low resolution DTI Diffusion measurements Segmentation Convolution neural network 3D 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Bo Li
    • 1
    • 2
  • Marius de Groot
    • 2
  • Meike W. Vernooij
    • 2
  • M. Arfan Ikram
    • 2
  • Wiro J. Niessen
    • 2
    • 3
  • Esther E. Bron
    • 2
  1. 1.Northeastern UniversityShenyangChina
  2. 2.Erasmus MCRotterdamThe Netherlands
  3. 3.Delft University of TechnologyDelftthe Netherlands

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