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:https://doi.org/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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-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 language	English
Title of host publication	New Knowledge in Information Systems and Technologies - Volume 2
Editors	Álvaro Rocha, Sandra Costanzo, Hojjat Adeli, Luís Paulo Reis
Publisher	Springer Verlag
Pages	577-586
Volume	931
ISBN (Print)	9783030161835
DOIs	https://doi.org/10.1007/978-3-030-16184-2_55
Publication status	Published - 2019
Externally published	Yes
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

Conference

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

Access to Document

https://doi.org/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 Á. Rocha, S. Costanzo, H. Adeli, & L. P. Reis (Eds.), New Knowledge in Information Systems and Technologies - Volume 2 (Vol. 931, pp. 577-586). (Advances in Intelligent Systems and Computing). Springer Verlag. 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 - Volume 2. editor / Álvaro Rocha ; Sandra Costanzo ; Hojjat Adeli ; Luís Paulo Reis. Vol. 931 Springer Verlag, 2019. pp. 577-586 (Advances in Intelligent Systems and Computing).

@inproceedings{b15f622e337d4c50b935b5fc4a4a008a,

title = "Convolutional neural network-based regression for quantification of brain characteristics using MRI",

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.",

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",

doi = "https://doi.org/10.1007/978-3-030-16184-2_55",

language = "English",

isbn = "9783030161835",

volume = "931",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Verlag",

pages = "577--586",

editor = "{\'A}lvaro Rocha and Sandra Costanzo and Hojjat Adeli and Reis, {Lu{\'i}s Paulo}",

booktitle = "New Knowledge in Information Systems and Technologies - Volume 2",

note = "World Conference on Information Systems and Technologies, WorldCIST 2019 ; Conference date: 16-04-2019 Through 19-04-2019",

}

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 Á Rocha, S Costanzo, H Adeli & LP Reis (eds), New Knowledge in Information Systems and Technologies - Volume 2. vol. 931, Advances in Intelligent Systems and Computing, Springer Verlag, pp. 577-586, World Conference on Information Systems and Technologies, WorldCIST 2019, Galicia, Spain, 16/04/2019. 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 - Volume 2. ed. / Álvaro Rocha; Sandra Costanzo; Hojjat Adeli; Luís Paulo Reis. Vol. 931 Springer Verlag, 2019. p. 577-586 (Advances in Intelligent Systems and Computing).

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

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T1 - Convolutional neural network-based regression for quantification of brain characteristics using MRI

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

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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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M3 - Conference contribution

SN - 9783030161835

VL - 931

T3 - Advances in Intelligent Systems and Computing

SP - 577

EP - 586

BT - New Knowledge in Information Systems and Technologies - Volume 2

A2 - Rocha, Álvaro

A2 - Costanzo, Sandra

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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 Rocha Á, Costanzo S, Adeli H, Reis LP, editors, New Knowledge in Information Systems and Technologies - Volume 2. Vol. 931. Springer Verlag. 2019. p. 577-586. (Advances in Intelligent Systems and Computing). doi: https://doi.org/10.1007/978-3-030-16184-2_55

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

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