Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
Open accessAbstract
Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of oncological patients. Magnetic resonance imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. So, automatic and reliable segmentation methods are required; however, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. In this paper, we propose an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3 ×3 kernels. The use of small kernels allows designing a deeper architecture, besides having a positive effect against overfitting, given the fewer number of weights in the network. We also investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, proved together with data augmentation to be very effective for brain tumor segmentation in MRI images. Our proposal was validated in the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013), obtaining simultaneously the first position for the complete, core, and enhancing regions in Dice Similarity Coefficient metric (0.88, 0.83, 0.77) for the Challenge data set. Also, it obtained the overall first position by the online evaluation platform. We also participated in the on-site BRATS 2015 Challenge using the same model, obtaining the second place, with Dice Similarity Coefficient metric of 0.78, 0.65, and 0.75 for the complete, core, and enhancing regions, respectively.
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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 [22]
With advent of deep learning, some preprocessing steps became less critical for final segmentation performance.
“With advent of deep learning techniques, some of the preprocessing steps became less critical for the final segmentation performance.”
From Deep learning for brain MRI segmentation: state of the art and future directions (2017) · cites this paper as [22]
Applying normalization prior to deep learning based segmentation improves results in another work.
“however, another work shows improvement when applying normalization prior to deep learning based segmentation procedure [22].”
From Deep learning for brain MRI segmentation: state of the art and future directions (2017) · cites this paper as [22]
Pereira et al. [22] method showed the best performance on the Brats 2013 data, with DSC values of 0.88, 0.83, and 0.77 for complete, core, and enhancing regions, respectively.
“Their method showed the best performance on the Brats 2013 data – DSC values of 0.88, 083, 0.77 for complete, core, and enhancing regions, respectively.”
From Deep learning for brain MRI segmentation: state of the art and future directions (2017) · cites this paper as [22]
Multiple studies have reported data augmentation to be very useful in their studies.
“Multiple studies have reported the data augmentation to be very useful in their studies [9, 22, 29].”
From Deep learning for brain MRI segmentation: state of the art and future directions (2017) · cites this paper as [22]
Pereira et al. [22] were ranked as second place in Brats 2015 data.
“They were also ranked as second place in Brats 2015 data.”
From Deep learning for brain MRI segmentation: state of the art and future directions (2017) · cites this paper as [22]
Data augmentation helps increase the size of training examples and reduce overfitting by introducing random variations to the original data.
“Data augmentation helps increase the size of training examples and reduce overfitting by introducing random variations to the original data.”
From Deep learning for brain MRI segmentation: state of the art and future directions (2017) · cites this paper as [22]
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An unsupervised domain adaptation brain CT segmentation method across image modalities and diseases
Dong, D., Fu, G., Li, J., Pei, Y., & Chen, Y. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118016 · Cited by 30 · see citing works
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- Authors
- Pereira, S., Pinto, A., Alves, V., & Silva, C. A.
- Affiliations
- University of Minho (PT)
- Centro ALGORITMI (PT)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2016-05
- Volume
- 35
- Issue
- 5
- Pages
- 1240-1251
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2310992461
- Cited by
- 2,735
- References
- 44
- Citations by year
- 2026: 176 · 2025: 261 · 2024: 262 · 2023: 296 · 2022: 363 · 2021: 372 · 2020: 343 · 2019: 303 · 2018: 234 · 2017: 111 · 2016: 12 · 2015: 2
- FWCI
- 120.53
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, cc-by-nc-nd)
- Topics
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- brain tumor segmentation, glioma, magnetic resonance imaging, convolutional neural networks, intensity normalization, data augmentation, BRATS 2015, automated image segmentation, treatment planning
- MeSH terms
- Machine Learning, Brain Neoplasms, Glioma, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Neural Networks, Computer
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