Convolutional neural network-based regression for quantification of brain characteristics using MRI

João Fernandes*, Victor Alves, Nadieh Khalili, Manon J.N.L. Benders, Ivana Išgum, Josien Pluim, Pim Moeskops

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationNew Knowledge in Information Systems and Technologies - Volume 2
EditorsLuís Paulo Reis, Sandra Costanzo, Hojjat Adeli, Álvaro Rocha
PublisherSpringer-Verlag
Pages577-586
Number of pages10
ISBN (Print)9783030161835
DOIs
Publication statusPublished - 1 Jan 2019
EventWorld Conference on Information Systems and Technologies, WorldCIST 2019 - Galicia, Spain
Duration: 16 Apr 201919 Apr 2019

Publication series

NameAdvances in Intelligent Systems and Computing
Volume931
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

ConferenceWorld Conference on Information Systems and Technologies, WorldCIST 2019
Country/TerritorySpain
CityGalicia
Period16/04/1919/04/19

Keywords

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

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