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White Matter Segmentation Algorithm for DTI Images Based on Super-Pixel Full Convolutional Network

Journal of Medical Systems volume 43, Article number: 303 (2019) Cite this article

Abstract

Diffusion tensor imaging (DTI) is a new imaging method that can be used to non-invasively measure the diffusion coefficient of water molecules in biological tissue structures in recent years. Since the DTI data is a tensor space, its segmentation is different from ordinary MRI images. Based on the existing deep learning model, an improved image semantic segmentation method based on super-pixels and conditional random field is proposed. Firstly, this paper uses the existing feature extraction model based on deep learning to obtain rough semantic segmentation results, including high-level semantic information of the image but lacking details of the image. In addition, the super-pixel segmentation algorithm is implemented to obtain super-pixels that carries more low-level information. Secondly, due to the lack of image details in rough segmentation results, the segmentation of the edge of the image is inaccurate. In this paper, a boundary optimization algorithm is proposed to optimize the edge segmentation accuracy of the rough results. Finally, the use of super-pixels for local boundary optimization can improve the segmentation accuracy. Experiments results show that this segment is a practical and effective method.

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Acknowledgments

This work is financially supported by Liaoning Province Natural Science Fund(NO: 201602725) and Shenyang Science and Technology Bureau Fund (NO:1801331).

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  1. Central Hospital Affiliated of Shenyang Medical College, Shenyang, 110024, Liaoning, China

    Yiping Mu, Qi Li & Yang Zhang

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  1. Yiping Mu
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  2. Qi Li
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Correspondence to Yiping Mu.

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Mu, Y., Li, Q. & Zhang, Y. White Matter Segmentation Algorithm for DTI Images Based on Super-Pixel Full Convolutional Network. J Med Syst 43, 303 (2019). https://doi.org/10.1007/s10916-019-1431-1

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  • DOI: https://doi.org/10.1007/s10916-019-1431-1

Keywords

  • Diffusion tensor imaging
  • Rough segmentation
  • Boundary optimization
  • Super-pixel segmentation
  • Full convolutional network
  • Deep learning