Deep convolutional neural 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 health and disease. In the isointense stage (approximately 6-8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, making the tissue segmentation very challenging. Only a small number of existing methods have been designed for tissue segmentation in this isointense stage; however, they only used a single T1 or T2 images, or the combination of T1 and T2 images. In this paper, we propose to use deep convolutional neural networks (CNNs) for segmenting isointense stage brain tissues using multi-modality MR images. CNNs are a type of deep models in which trainable filters and local neighborhood pooling operations are applied alternatingly on the raw input images, resulting in a hierarchy of increasingly complex features. Specifically, we used multi-modality information from T1, T2, and fractional anisotropy (FA) images as inputs and then generated the segmentation maps as outputs. The multiple intermediate layers applied convolution, pooling, normalization, and other operations to capture the highly nonlinear mappings between inputs and outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense stage brain images. Results showed that our proposed model significantly outperformed prior methods on infant brain tissue segmentation. In addition, our results indicated that integration of multi-modality images led to significant performance improvement.
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Zhang et al. CNN approach outperforms prior methods and classical ML algorithms using SVM and RF (overall DSC 85.03±2.27 CNN vs 76.95±3.55 SVM, 83.15±2.52 RF).
“They showed that their CNN approach outperforms prior methods and classical machine learning algorithms using support vector machine (SVM) and random forest (RF) classifiers (overall DSC performance 85.03% ∓ 2.27% (CNN) vs. 76.95% ∓ 3.55% (SVM), 83.15% ∓ 2.52% (RF)).”
From Deep learning for brain MRI segmentation: state of the art and future directions (2017) · cites this paper as [27]
Most current popular CNN architectures use patch-wise approach.
“Most of the current popular architectures [21, 22, 26, 27] use this approach.”
From Deep learning for brain MRI segmentation: state of the art and future directions (2017) · cites this paper as [27]
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 [27]
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- Authors
- Zhang, W., Li, R., Deng, H., Wang, L., Lin, W., Ji, S., & Shen, D.
- Affiliations
- Old Dominion University (US)
- Instacart (United States) (US)
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Ji, Shuiwang
- Shen, Dinggang
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2015-03
- Volume
- 108
- Pages
- 214-224
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC4323729
- OpenAlex
- W2082526668
- Cited by
- 817
- References
- 63
- Citations by year
- 2026: 12 · 2025: 36 · 2024: 45 · 2023: 62 · 2022: 77 · 2021: 110 · 2020: 93 · 2019: 149 · 2018: 90 · 2017: 86 · 2016: 50 · 2015: 5 · 2014: 1
- FWCI
- 66.87
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- UNC Libraries
- Funders
- National Science Foundation (DBI-1147134, DBI-1350258)
- National Institutes of Health (EB006733, EB008374, EB009634, AG041721, MH100217, AG042599)
- 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
- white matter segmentation, gray matter segmentation, cerebrospinal fluid segmentation, multimodal MRI, T1 and T2 MRI, fractional anisotropy, deep convolutional neural networks, brain development
- MeSH terms
- Brain, Brain Mapping, Humans, Image Processing, Computer-Assisted, Infant, Magnetic Resonance Imaging, Neural Networks, Computer, Anisotropy, White Matter, Gray Matter
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