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Non-parametric Brain Tissues Segmentation via a Parallel Architecture of CNNs

  • Dante Mújica-VargasEmail author
  • Alicia Martínez
  • Manuel Matuz-Cruz
  • Antonio Luna-Alvarez
  • Mildred Morales-Xicohtencatl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11524)

Abstract

A fully automatic brain tissue segmentation framework is introduced in current paper, it is based on a parallel architecture of a specialized convolutional deep neuronal network designed to develop binary segmentation. The main contributions of this proposal imply its ability to segment brain RMI images of different acquisition modes, it does not require the initialization of any parameter; apart from the foregoing, it does not require any preprocessing stage to improve the quality of each slice. Experimental tests were developed considering BrainWeb and BraTS 2017 databases. The robustness and effectiveness of this proposal is verified by quantitative and qualitative results.

Keywords

Brain RMI segmentation Parallel architecture Convolutional deep neuronal network 

Notes

Acknowledgement

The authors are grateful to CONACYT, as well as Tecnológico Nacional de México/CENIDET for their support trough the project 5628.19-P so-called “Sistema embebido para asistencia de conducción basado en Lógica Difusa Tipo-2”.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Dante Mújica-Vargas
    • 1
    Email author
  • Alicia Martínez
    • 1
  • Manuel Matuz-Cruz
    • 2
  • Antonio Luna-Alvarez
    • 1
  • Mildred Morales-Xicohtencatl
    • 1
  1. 1.Tecnológico Nacional de México/CENIDETCuernavacaMéxico
  2. 2.Tecnológico Nacional de México/ITTapachulaTapachulaMéxico

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