ArticleFrontiers in neuroinformatics2024
Brain MRI sequence and view plane identification using deep learning.
Article in Frontiers in neuroinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Few-shot deployment of pretrained MRI transformers in brain imaging tasks.Frontiers in artificial intelligence · 2026Article
- Few-Shot Deployment of Pretrained MRI Transformers in Brain Imaging Tasks.Research square · 2025Article
- Artificial intelligence in neurodegenerative diseases research: a bibliometric analysis since 2000.Frontiers in neurology · 2025Review
- VISION: View-specific integrated segmentation-classification framework for accurate brain tumor detection in MRI scans.PloS one · 2025Article
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Authors and funding
1 author.
Funding
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Abstract
Brain magnetic resonance imaging (MRI) scans are available in a wide variety of sequences, view planes, and magnet strengths. A necessary preprocessing step for any automated diagnosis is to identify the MRI sequence, view plane, and magnet strength of the acquired image. Automatic identification of the MRI sequence can be useful in labeling massive online datasets used by data scientists in the design and development of computer aided diagnosis (CAD) tools. This paper presents a deep learning (DL) approach for brain MRI sequence and view plane identification using scans of different data types as input. A 12-class classification system is presented for commonly used MRI scans, including T1, T2-weighted, proton density (PD), fluid attenuated inversion recovery (FLAIR) sequences in axial, coronal and sagittal view planes. Multiple online publicly available datasets have been used to train the system, with multiple infrastructures. MobileNet-v2 offers an adequate performance accuracy of 99.76% with unprocessed MRI scans and a comparable accuracy with skull-stripped scans and has been deployed in a tool for public use. The tool has been tested on unseen data from online and hospital sources with a satisfactory performance accuracy of 99.84 and 86.49%, respectively.
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