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

doi:10.1007/978-3-030-16184-2_55

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

Medical Image Analysis

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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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 language	English
Title of host publication	New Knowledge in Information Systems and Technologies
Editors	Luís Paulo Reis, Sandra Costanzo, Hojjat Adeli, Álvaro Rocha
Place of Publication	Berlin
Publisher	Springer
Pages	577-586
Number of pages	10
Volume	2
ISBN (Print)	9783030161835
DOIs	https://doi.org/10.1007/978-3-030-16184-2_55
Publication status	Published - 30 Mar 2019
Event	World Conference on Information Systems and Technologies, WorldCIST 2019 - Galicia, Spain Duration: 16 Apr 2019 → 19 Apr 2019

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	931
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	World Conference on Information Systems and Technologies, WorldCIST 2019
Country/Territory	Spain
City	Galicia
Period	16/04/19 → 19/04/19

Keywords

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

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10.1007/978-3-030-16184-2_55

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Fernandes, J., Alves, V., Khalili, N., Benders, M. J. N. L., Išgum, I., Pluim, J., & Moeskops, P. (2019). Convolutional neural network-based regression for quantification of brain characteristics using MRI. In L. P. Reis, S. Costanzo, H. Adeli, & Á. Rocha (Eds.), New Knowledge in Information Systems and Technologies (Vol. 2, pp. 577-586). (Advances in Intelligent Systems and Computing; Vol. 931). Springer. https://doi.org/10.1007/978-3-030-16184-2_55

Fernandes, João ; Alves, Victor ; Khalili, Nadieh et al. / Convolutional neural network-based regression for quantification of brain characteristics using MRI. New Knowledge in Information Systems and Technologies. editor / Luís Paulo Reis ; Sandra Costanzo ; Hojjat Adeli ; Álvaro Rocha. Vol. 2 Berlin : Springer, 2019. pp. 577-586 (Advances in Intelligent Systems and Computing).

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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.",

keywords = "Brain quantification, Convolutional neural networks, Deep learning, Magnetic resonance imaging, Preterm infants, Rat brain, Regression",

author = "Jo{\~a}o Fernandes and Victor Alves and Nadieh Khalili and Benders, {Manon J.N.L.} and Ivana I{\v s}gum and Josien Pluim and Pim Moeskops",

year = "2019",

month = mar,

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doi = "10.1007/978-3-030-16184-2_55",

language = "English",

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Fernandes, J, Alves, V, Khalili, N, Benders, MJNL, Išgum, I, Pluim, J & Moeskops, P 2019, Convolutional neural network-based regression for quantification of brain characteristics using MRI. in LP Reis, S Costanzo, H Adeli & Á Rocha (eds), New Knowledge in Information Systems and Technologies. vol. 2, Advances in Intelligent Systems and Computing, vol. 931, Springer, Berlin, pp. 577-586, World Conference on Information Systems and Technologies, WorldCIST 2019, Galicia, Spain, 16/04/19. https://doi.org/10.1007/978-3-030-16184-2_55

Convolutional neural network-based regression for quantification of brain characteristics using MRI. / Fernandes, João; Alves, Victor; Khalili, Nadieh et al.
New Knowledge in Information Systems and Technologies. ed. / Luís Paulo Reis; Sandra Costanzo; Hojjat Adeli; Álvaro Rocha. Vol. 2 Berlin: Springer, 2019. p. 577-586 (Advances in Intelligent Systems and Computing; Vol. 931).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Fernandes, João

AU - Alves, Victor

AU - Khalili, Nadieh

AU - Benders, Manon J.N.L.

AU - Išgum, Ivana

AU - Pluim, Josien

AU - Moeskops, Pim

PY - 2019/3/30

Y1 - 2019/3/30

N2 - 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.

AB - 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.

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KW - Convolutional neural networks

KW - Deep learning

KW - Magnetic resonance imaging

KW - Preterm infants

KW - Rat brain

KW - Regression

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AN - SCOPUS:85065091198

SN - 9783030161835

VL - 2

T3 - Advances in Intelligent Systems and Computing

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EP - 586

BT - New Knowledge in Information Systems and Technologies

A2 - Reis, Luís Paulo

A2 - Costanzo, Sandra

A2 - Adeli, Hojjat

A2 - Rocha, Álvaro

PB - Springer

CY - Berlin

T2 - World Conference on Information Systems and Technologies, WorldCIST 2019

Y2 - 16 April 2019 through 19 April 2019

ER -

Fernandes J, Alves V, Khalili N, Benders MJNL, Išgum I, Pluim J et al. Convolutional neural network-based regression for quantification of brain characteristics using MRI. In Reis LP, Costanzo S, Adeli H, Rocha Á, editors, New Knowledge in Information Systems and Technologies. Vol. 2. Berlin: Springer. 2019. p. 577-586. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-16184-2_55

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

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