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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
  • pre-AMC

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
PublisherSpringer Verlag
Pages577-586
Volume931
ISBN (Print)9783030161835
DOIs
Publication statusPublished - 2019
Externally publishedYes
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
ISSN (Print)2194-5357

Conference

ConferenceWorld Conference on Information Systems and Technologies, WorldCIST 2019
Country/TerritorySpain
CityGalicia
Period16/04/201919/04/2019

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