The DOI matches its Crossref record; 15 of the 16 references it cites were found in a registry or an index.
Multi-scale structured CNN with label consistency for brain MR image segmentation
Abstract
In this paper, a novel method for brain MR image segmentation has been proposed, with deep learning techniques to obtain preliminary labelling and graphical models to produce the final result. A specific architecture, namely multi-scale structured convolutional neural networks (MS-CNN), is designed to capture discriminative features for each sub-cortical structure and to generate a label probability map for the target image. Due to complex background in brain images and the lack of spatial constraints among testing samples, the initial result obtained with MS-CNN is not smooth. To deal with this problem, dynamic random walker with decayed region of interest is then proposed to enforce label consistency. Comprehensive evaluations have been carried out on two publicly available data-sets and experimental results indicate that the proposed method can obtain better segmentation quality efficiently.
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“They reported overall DSC of 82.2 and 85% for IBSR and LPBA40, respectively.”
From Deep learning for brain MRI segmentation: state of the art and future directions (2017) · cites this paper as [41]
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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
Deep 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
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Dolz, J., Desrosiers, C., & Ayed, I. B. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.04.039 · Cited by 415
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Renard, F., Guedria, S., Palma, N. D., & Vuillerme, N. (2020) · Scientific Reports · doi:10.1038/s41598-020-69920-0 · Cited by 255
Yamanakkanavar, N., Choi, J. Y., & Lee, B. (2020) · Sensors · doi:10.3390/s20113243 · Cited by 235
Metadata
- Authors
- Bao, S., & Chung, A. C. S.
- Affiliations
- Hong Kong University of Science and Technology (HK)
- Corresponding author
- Bao, Siqi
- Venue
- Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization
- Publisher
- Informa UK Limited
- Published
- 2016-06-22
- Volume
- 6
- Issue
- 1
- Pages
- 113-117
- ISSN
- 2168-1163 (ISSN-L)
- 2168-1171
- OpenAlex
- W2460515352
- Cited by
- 101
- References
- 16
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 7 · 2023: 6 · 2022: 15 · 2021: 15 · 2020: 21 · 2019: 15 · 2018: 13 · 2017: 6 · 2016: 1
- FWCI
- 7.93
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Funders
- Research Grants Council of Hong Kong (16203115)
- 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 MRI segmentation, label consistency, probabilistic graphical models, label smoothing
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