Deep Feature Learning with Discrimination Mechanism for Brain Tumor Segmentation and Diagnosis
Abstract
Brain tumor segmentation is one of the main challenging problems in computer vision and its early diagnosis is critical to clinics. Segmentation needs to be accurate, efficient and robust to avoid influences caused by various large and complex biases added to images. This paper proposes a multiple convolutional neural network (CNNs) framework with discrimination mechanism which is effective to achieve these goals. First of all, this paper proposes to construct different triplanar 2D CNNs architecture for 3D voxel classification, greatly reducing segmentation time. Experiment is conducted on images provided by Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized by MICCAI 2013 for both training and testing. As T1, T1-enhanced, T2 and FLAIR MRI images are utilized, multimodal features are combined. As a result, accuracy, sensitivity and specificity are comparable in comparison with manual gold standard images and better than state-of-the-art segmentation methods.
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Zhao and Jia [53] obtained comparable results to state-of-art machine learning algorithms on Brats 2013 data.
“They have obtained comparable results to state-of-art machine learning algorithms on Brats 2013 data.”
From Deep learning for brain MRI segmentation: state of the art and future directions (2017) · cites this paper as [53]
Cites
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Deep learning for brain MRI segmentation: state of the art and future directions
Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D. L., & Erickson, B. J. (2017) · Journal of Digital Imaging · doi:10.1007/s10278-017-9983-4 · Cited by 1,129 · see citing works
Gumaei, A., Hassan, M. M., Hassan, M. R., Alelaiwi, A., & Fortino, G. (2019) · IEEE Access · doi:10.1109/access.2019.2904145 · Cited by 484 · see citing works
- Authors
- Zhao, L., & Jia, K.
- Affiliations
- Beijing University of Technology (CN)
- ORCID
- Venue
- 2015 International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP)
- Publisher
- IEEE
- Published
- 2015-09
- Pages
- 306-309
- OpenAlex
- W2291411649
- Cited by
- 67
- References
- 11
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 4 · 2023: 6 · 2022: 15 · 2021: 14 · 2020: 6 · 2019: 9 · 2018: 4 · 2017: 3 · 2016: 1 · 2015: 1
- FWCI
- 3.93
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP) (publishedVersion)
- 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, brain tumor diagnosis, multimodal MRI, MRI classification
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