Cited by 861Medical Image Segmentation TechniquesBrain Tumor Detection and ClassificationAdvanced Neural Network Applications

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Automatic Segmentation of MR Brain Images With a Convolutional Neural Network

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Moeskops, Pim, Viergever, Max A., Mendrik, Adrienne M., de Vries, Linda S., Benders, Manon J. N. L., Isgum, Ivana·2016·IEEE Transactions on Medical Imaging

DOI 10.1109/tmi.2016.2548501

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Abstract

Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses multiple patch sizes and multiple convolution kernel sizes to acquire multi-scale information about each voxel. The method is not dependent on explicit features, but learns to recognise the information that is important for the classification based on training data. The method requires a single anatomical MR image only. The segmentation method is applied to five different data sets: coronal T2-weighted images of preterm infants acquired at 30 weeks postmenstrual age (PMA) and 40 weeks PMA, axial T2-weighted images of preterm infants acquired at 40 weeks PMA, axial T1-weighted images of ageing adults acquired at an average age of 70 years, and T1-weighted images of young adults acquired at an average age of 23 years. The method obtained the following average Dice coefficients over all segmented tissue classes for each data set, respectively: 0.87, 0.82, 0.84, 0.86, and 0.91. The results demonstrate that the method obtains accurate segmentations in all five sets, and hence demonstrates its robustness to differences in age and acquisition protocol.

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Authors: Moeskops, P., Viergever, M. A., Mendrik, A. M., de Vries, L. S., Benders, M. J. N. L., & Isgum, I.
Affiliations: University Medical Center Utrecht (NL)
ORCID: Moeskops, Pim
Viergever, Max A.
Benders, Manon J. N. L.
Venue: IEEE Transactions on Medical Imaging
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2016-05
Volume: 35
Issue: 5
Pages: 1252-1261
ISSN: 0278-0062 (ISSN-L)
1558-254X
DOI: 10.1109/tmi.2016.2548501
OpenAlex: W2332066482
Cited by: 861
References: 44
Citations by year: 2026: 16 · 2025: 32 · 2024: 57 · 2023: 62 · 2022: 90 · 2021: 122 · 2020: 131 · 2019: 165 · 2018: 102 · 2017: 75 · 2016: 8
FWCI: 79.18
Type: journal article
Language: en
Open access: green
pure.amsterdamumc.nl
Licence: other-oa
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE Transactions on Medical Imaging (publishedVersion)
PubMed (publishedVersion)
TU/e Research Portal (publishedVersion)
Pure Amsterdam UMC (acceptedVersion, other-oa)
arXiv (Cornell University) (submittedVersion)
TU/e Research Portal (submittedVersion)
Data Archiving and Networked Services (DANS) (submittedVersion)
arXiv (Cornell University)
Funders: Nvidia
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Brain Tumor Detection and Classification
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords: brain tissue segmentation, MR brain images, convolutional neural networks, multi-scale convolution, preterm infants, T2-weighted imaging, T1-weighted imaging, older adults, spatial consistency
MeSH terms: Machine Learning, Adult, Aged, Brain, Humans, Image Processing, Computer-Assisted, Infant, Newborn, Infant, Premature, Magnetic Resonance Imaging, Neural Networks, Computer, Young Adult

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Automatic Segmentation of MR Brain Images With a Convolutional Neural Network

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