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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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Samenvatting

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.

Originele taal-2Engels
TitelNew Knowledge in Information Systems and Technologies
RedacteurenLuís Paulo Reis, Sandra Costanzo, Hojjat Adeli, Álvaro Rocha
Plaats van productieBerlin
UitgeverijSpringer
Pagina's577-586
Aantal pagina's10
Volume2
ISBN van geprinte versie9783030161835
DOI's
StatusGepubliceerd - 30 mrt 2019
EvenementWorld Conference on Information Systems and Technologies, WorldCIST 2019 - Galicia, Spanje
Duur: 16 apr 201919 apr 2019

Publicatie series

NaamAdvances in Intelligent Systems and Computing
Volume931
ISSN van geprinte versie2194-5357
ISSN van elektronische versie2194-5365

Congres

CongresWorld Conference on Information Systems and Technologies, WorldCIST 2019
LandSpanje
StadGalicia
Periode16/04/1919/04/19

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