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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Bao et al. reported overall DSC 82.2 and 85% for IBSR and LPBA40.
“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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Applying Artificial Intelligence to Medical Data
Rahman, S. S. (2020)
Subcortical Brain Segmentation in MR image based on Residual Fully Convolutional Networks
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- 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
- Life Sciences › Neuroscience › Neurology › 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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