Fast and robust segmentation of the striatum using deep convolutional neural networks
Open accessAbstract
Background Automated segmentation of brain structures is an important task in structural and functional image analysis. We developed a fast and accurate method for the striatum segmentation using deep convolutional neural networks (CNN).New method T1 magnetic resonance (MR) images were used for our CNN-based segmentation, which require neither image feature extraction nor nonlinear transformation. We employed two serial CNN, Global and Local CNN: The Global CNN determined approximate locations of the striatum. It performed a regression of input MR images fitted to smoothed segmentation maps of the striatum. From the output volume of Global CNN, cropped MR volumes which included the striatum were extracted. The cropped MR volumes and the output volumes of Global CNN were used for inputs of Local CNN. Local CNN predicted the accurate label of all voxels. Segmentation results were compared with a widely used segmentation method, FreeSurfer.Results Our method showed higher Dice Similarity Coefficient (DSC) (0.893±0.017 vs. 0.786±0.015) and precision score (0.905±0.018 vs. 0.690±0.022) than FreeSurfer-based striatum segmentation (p=0.06). Our approach was also tested using another independent dataset, which showed high DSC (0.826±0.038) comparable with that of FreeSurfer. Comparison with existing method Segmentation performance of our proposed method was comparable with that of FreeSurfer. The running time of our approach was approximately three seconds.Conclusion We suggested a fast and accurate deep CNN-based segmentation for small brain structures which can be widely applied to brain image analysis.
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- Authors
- Choi, H., & Jin, K. H.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- Corresponding author
- Choi, Hongyoon
- Jin, Kyong Hwan
- Venue
- Journal of Neuroscience Methods
- Publisher
- Elsevier BV
- Published
- 2016-12
- Volume
- 274
- Pages
- 146-153
- ISSN
- 0165-0270 (ISSN-L)
- OpenAlex
- W2538673204
- Cited by
- 73
- References
- 29
- Citations by year
- 2026: 1 · 2024: 8 · 2023: 5 · 2022: 5 · 2021: 13 · 2020: 11 · 2019: 17 · 2018: 8 · 2017: 5
- FWCI
- 6.79
- Type
- journal article
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Locations
- Journal of Neuroscience Methods (publishedVersion)
- PubMed (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- 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
- deep convolutional neural networks, T1-weighted imaging, brain structure segmentation, FreeSurfer comparison, Dice coefficient
- MeSH terms
- Adult, Brain Mapping, Corpus Striatum, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Models, Neurological, Nerve Net, Young Adult
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