Cited by 330Medical Image Segmentation TechniquesBrain Tumor Detection and ClassificationMedical Imaging and Analysis

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Deep neural networks for anatomical brain segmentation

de Brebisson, Alexandre, Montana, Giovanni·2015·2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

DOI 10.1109/cvprw.2015.7301312

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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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Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Learning Deep Architectures for AI
Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,330
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Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults
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
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Untitled work
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glorot, 2011, Deep sparse rectifier networks, Proceedings of the 14th International Conference on Artificial Intelligence and Statistics JMLR W&CP, 15, 315
bergstra, 0, Theano: a CPU and GPU Math Expression Compiler, Proceedings of the Python for Scientific Computing Conference (SciPy) year = 2010 location = Austin TX note = Oral Presentation

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Yang, N., Wang, D., Tan, T., & Zhang, C. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385896 · Cited by 1
Challenges in Accurately Using Artificial Intelligence and Machine Learning in Biomedical Imaging
Sharma, M., Goswami, B., Goswami, N., Mahanta, S., & Singh, Y. J. (2024) · Biological and medical physics, biomedical engineering · doi:10.1007/978-981-97-5345-1_3 · Cited by 2
PIRNet: Two-Step Deep Neural Network for Segmentation of Brain MRI with Efficient Loss Functions
Datta, A., Purkayastha, K., Ghosal, P., & Ahmed, R. A. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-86296-0_6 · Cited by 1
Eyeing the Human Brain’s Segmentation Methods
al., L. C. K. E. A. L. C. K. E., & TJPRC (2019) · International Journal of Electrical and Electronics Engineering Research · doi:10.24247/ijeeerjun20195 · Cited by 1
Visualization of MRI Datasets for Anatomical Brain Segmentation by Pixel-level Analysis
Manoharan, H., Pang, G. K. H., & Wu, H. (2019) · IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) · doi:10.1109/iemcon.2019.8936302 · Cited by 1
Метод автоматического трекинга стволовых клеток по данным магнитно-резонансной томографии
Veniaminovna, S. M., & В., Ш. М. (2020) · Международный журнал Программные продукты и системы · doi:10.15827/0236-235x.132.706-713 · Cited by 1
Connectivity-Driven Parcellation Methods for the Human Cerebral CortexPreprint
Arslan, S. (2017) · arXiv (Cornell University) · doi:10.25560/54760 · Cited by 1
3D Quantum-inspired Self-supervised Tensor Network for Volumetric Segmentation of Brain MR ImagesPreprint
Konar, D., Bhattacharyya, S., Gandhi, T. K., Panigrahi, B. K., & Jiang, R. (2020) · doi:10.36227/techrxiv.12909860.v1 · Cited by 1
3D Quantum-inspired Self-supervised Tensor Network for Volumetric Segmentation of Medical ImagesPreprint
Konar, D., Bhattacharyya, S., Gandhi, T. K., Panigrahi, B. K., & Jiang, R. (2023) · doi:10.36227/techrxiv.12909860.v4 · Cited by 1
Deep neural networks for segmentation of basal ganglia sub-structures in brain MR images
Sethi, A., Sinha, A., Agarwal, A., Arora, C., & Gupta, A. (2016) · Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1145/3009977.3010048
Anatomical labeling of brain CT scan anomalies using multi-context nearest neighbor relation networksPreprint
Varadarajan, S., Srivastava, M. M., Grewal, M., & Kumar, P. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.09180
Convolutional Neural Network Based Segmentation of Demyelinating Plaques in MRI
Stasiak, B., Tarasiuk, P., Michalska, I., Tomczyk, A., & Szczepaniak, P. S. (2018) · Communications in computer and information science · doi:10.1007/978-3-319-94806-5_10
Intelligent magnetic resonance imaging : application in fetal MRI
Monfared, M. S. (2017) · doi:10.17760/d20290481
The Generation and Application of Medical Image Grid Based on Differential Geometric Features.Preprint
Zhu, Y., Zhou, Z., Liao, G., Yang, Q., & Yuan, K. (2019) · arXiv (Cornell University)
Self-learning shape recognition in medical images
Ma, J. (2019) · doi:10.32657/10220/47802
INVESTIGATIONS OF THE INFLUENCES OF A CNN'S RECEPTIVE FIELD ON SEGMENTATION OF SUBNUCLEI OF BILATERAL AMYGDALAEPreprint
Bao, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.02761
Surface loss for medical image segmentation
Bouchtiba, J. (2019) · Espace ÉTS (ETS)

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
DOI: 10.1109/cvprw.2015.7301312
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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Deep neural networks for anatomical brain segmentation

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