Convolutional Neural Network-Based Regression for Quantification of Brain Characteristics Using MRI

  • João FernandesEmail author
  • Victor Alves
  • Nadieh Khalili
  • Manon J. N. L. Benders
  • Ivana Išgum
  • Josien Pluim
  • Pim Moeskops
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 931)


Preterm birth is connected to impairments and altered brain growth. Compared to their term born peers, preterm infants have a higher risk of behavioral and cognitive problems since most part of their brain development is in extra-uterine conditions. This paper presents different deep learning approaches with the objective of quantifying the volumes of 8 brain tissues and 5 other image-based descriptors that quantify the state of brain development. Two datasets were used: one with 86 MR brain images of patients around 30 weeks PMA and the other with 153 patients around 40 weeks PMA. Two approaches were evaluated: (1) using the full image as 3D input and (2) using multiple image slices as 3D input, both achieving promising results. A second study, using a dataset of MR brain images of rats, was also performed to assess the performance of this method with other brains. A 2D approach was used to estimate the volumes of 3 rat brain tissues.


Preterm infants Rat brain Brain quantification Deep learning Convolutional neural networks Regression Magnetic resonance imaging 



This work was supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013. We gratefully acknowledge the support of the NVIDIA Corporation with their donation of a Quadro P6000 board used in this research.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Informatics, School of EngineeringUniversity of MinhoBragaPortugal
  2. 2.Algoritmi CentreUniversity of MinhoBragaPortugal
  3. 3.Images Sciences InstituteUniversity Medical Center UtrechtUtrechtThe Netherlands
  4. 4.Department of NeonatologyUniversity Medical Center UtrechtUtrechtThe Netherlands
  5. 5.Eindhoven University of TechnologyEindhovenThe Netherlands

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