Fully convolutional networks for multi-modality isointense infant brain image segmentation
Open accessAbstract
The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development. In the isointense phase (approximately 6-8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, resulting in extremely low tissue contrast and thus making the tissue segmentation very challenging. The existing methods for tissue segmentation in this isointense phase usually employ patch-based sparse labeling on single T1, T2 or fractional anisotropy (FA) modality or their simply-stacked combinations without fully exploring the multi-modality information. To address the challenge, in this paper, we propose to use fully convolutional networks (FCNs) for the segmentation of isointense phase brain MR images. Instead of simply stacking the three modalities, we train one network for each modality image, and then fuse their high-layer features together for final segmentation. Specifically, we conduct a convolution-pooling stream for multimodality information from T1, T2, and FA images separately, and then combine them in high-layer for finally generating the segmentation maps as the outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense phase brain images. Results showed that our proposed model significantly outperformed previous methods in terms of accuracy. In addition, our results also indicated a better way of integrating multi-modality images, which leads to performance improvement.
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Nie et al. semantic-wise FCNs obtained improved results compared to Zhang et al. (overall DSC 85.5 CSF, 87.3 GM, 88.7 WM vs 83.5, 85.2, 86.4).
“They obtained improved results compared to [27]. Their overall DSC were 85.5% (CSF), 87.3% (GM), and 88.7% (WM) vs. 83.5% (CSF), 85.2 (GM), and 86.4 (WM) by [27].”
From Deep learning for brain MRI segmentation: state of the art and future directions (2017) · cites this paper as [30]
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- Authors
- Nie, D., Wang, L., Gao, Y., & Shen, D.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Venue
- 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)
- Publisher
- IEEE
- Published
- 2016-04
- Volume
- 2016
- Pages
- 1342-1345
- PMC
- PMC5031138
- OpenAlex
- W2441649867
- Cited by
- 304
- References
- 20
- Citations by year
- 2026: 12 · 2025: 19 · 2024: 25 · 2023: 29 · 2022: 44 · 2021: 43 · 2020: 42 · 2019: 44 · 2018: 28 · 2017: 13 · 2016: 4
- FWCI
- 10.15
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- IEEE International Symposium on Biomedical Imaging (ISBI) (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Mental Health (R01MH100217, U01MH110274)
- 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
- multimodal MRI, white matter segmentation, gray matter segmentation, cerebrospinal fluid segmentation, fully convolutional network, feature fusion, T1 and T2 MRI, fractional anisotropy, early brain development
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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