The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Deep neural networks for anatomical brain segmentation
Abstract
We present a novel approach to automatically segment magnetic resonance (MR) images of the human brain into anatomical regions. Our methodology is based on a deep artificial neural network that assigns each voxel in an MR image of the brain to its corresponding anatomical region. The inputs of the network capture information at different scales around the voxel of interest: 3D and orthogonal 2D intensity patches capture a local spatial context while downscaled large 2D orthogonal patches and distances to the regional centroids enforce global spatial consistency. Contrary to commonly used segmentation methods, our technique does not require any non-linear registration of the MR images. To benchmark our model, we used the dataset provided for the MICCAI 2012 challenge on multi-atlas labelling, which consists of 35 manually segmented MR images of the brain. We obtained competitive results (mean dice coefficient 0.725, error rate 0.163) showing the potential of our approach. To our knowledge, our technique is the first to tackle the anatomical segmentation of the whole brain using deep neural networks.
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“They achieved competitive results (DSC = 72.5% ∓ 16.3%) in MICCAI 2012 challenge on multi-atlas labeling as the first CNN approach applied to the task.”
From Deep learning for brain MRI segmentation: state of the art and future directions (2017) · cites this paper as [40]
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A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
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Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
Kamnitsas, K., Ledig, C., Newcombe, V. F. J., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., & Glocker, B. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.10.004 · Cited by 3,498
“In contrast to the 2D/3D hybrid variants (Roth et al. (2014); Brebisson and Montana (2015)), 3D CNNs can fully exploit dense-inference (LeCun et al. (1998); Ser-manet et al. (2014)), a technique that greatly decreases inference times and which we will further discuss in section 2.1.”
BackgroundInfluentialDeep learning for brain MRI segmentation: state of the art and future directions
Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D. L., & Erickson, B. J. (2017) · Journal of Digital Imaging · doi:10.1007/s10278-017-9983-4 · Cited by 1,129
Automatic Segmentation of MR Brain Images With a Convolutional Neural Network
Moeskops, P., Viergever, M. A., Mendrik, A. M., de Vries, L. S., Benders, M. J. N. L., & Isgum, I. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2548501 · Cited by 861
Metadata
- Authors
- de Brebisson, A., & Montana, G.
- Affiliations
- St Thomas' Hospital (GB)
- ORCID
- Montana, Giovanni
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 20-28
- OpenAlex
- W1910109443
- Cited by
- 330
- References
- 21
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 15 · 2023: 24 · 2022: 37 · 2021: 39 · 2020: 49 · 2019: 55 · 2018: 43 · 2017: 45 · 2016: 12 · 2015: 1
- FWCI
- 13.81
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- Research Portal (King's College London) (submittedVersion)
- 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 › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- brain segmentation, magnetic resonance imaging, deep neural network, spatial context, multi-scale features
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