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Automatic Segmentation of MR Brain Images With a Convolutional Neural Network
Open accessAbstract
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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“In general, the algorithm has Dice coefficients in the range of 0.82–0.87 and achieved a satisfactory accuracy.”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [303]
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“Moeskops et al. [303] employed three parallel-running CNNs, each of which had a 2D input patch of dissimilar size, for segmenting and classifying MRI brain images. These images, which include 35 adults and 22 pre-term infants, were classified into various tissue categories such as cerebrospinal fluid, grey matter, and white matter.”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [303]
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“The proposed system performs better in classifying organs when applied to multimodal data.”
From Medical image retrieval using deep convolutional neural network (2017) · cites this paper as [43]
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“the system trained in [43] is on a different set of image collection”
From Medical image retrieval using deep convolutional neural network (2017) · cites this paper as [43]
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“Although the system trained in [43] is on a different set of image collection, but the high accuracy achieved by our proposed system demonstrates the efficacy of the method in the classification task.”
From Medical image retrieval using deep convolutional neural network (2017) · cites this paper as [43]
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Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
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Fast robust automated brain extraction
Smith, S. M. (2002) · Human Brain Mapping · doi:10.1002/hbm.10062 · Cited by 10,496
Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., et al. (2002) · Neuron · doi:10.1016/s0896-6273(02)00569-x · Cited by 8,610
Marcus, D. S., Wang, T. H., Parker, J., Csernansky, J. G., Morris, J. C., & Buckner, R. L. (2007) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2007.19.9.1498 · Cited by 2,163
Thinning of the Cerebral Cortex in Aging
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1 of the 44 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- tieleman, 2012, Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude, Neural Netw Mach Learning, 4
- landman, 2012, MICCAI 2012 Workshop on Multi-Atlas Labeling
- roth, 2014, A new 2.5 D representation for lymph node detection using random sets of deep convolutional neural network observations, Proc MICCAI, 520
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Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00444-8 · Cited by 7,906
Deep learning in medical image analysis
Shen, D., Wu, G., & Suk, H.-I. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044442 · Cited by 4,968
Learning a variational network for reconstruction of accelerated MRI data
Hammernik, K., Klatzer, T., Kobler, E., Recht, M. P., Sodickson, D. K., Pock, T., & Knöll, F. (2017) · Magnetic Resonance in Medicine · doi:10.1002/mrm.26977 · Cited by 1,777
Greenspan, H. K., van Ginneken, B., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2553401 · Cited by 1,725
Metadata
- 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)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2016-05
- Volume
- 35
- Issue
- 5
- Pages
- 1252-1261
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- 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
- 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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