Abstract
Magnetic resonance imaging (MRI), a non-ionizing radiation imaging method, is widely utilized in diagnosing Alzheimer’s disease (AD) due to its excellent imaging ability for brain soft tissues and high-resolution slice imaging. However, the enormous size of 3D MRI makes it difficult to process and analyze it. Therefore, a challenge is to accurately mine encephalatrophy patches from 3D MRI and build a patch-based diagnostic model. The existing patch-based methods mainly extract and fuse features of each patch in isolation, ignoring mutual information between patches, which makes the diagnosis performance unsatisfactory. We propose a novel dual-branch AD diagnostic model based on distinguishing atrophic patch localization. (1) We propose a Distinguishing Atrophic Patch Localization (DAPL) algorithm based on Distinguishing Index (DI) and Spatial Contact Ratio (SCR) to extract lesion areas that have significant impacts on diagnosis from 3D MRI. Meanwhile, we proposed a Discontinuity-voxel-based Dynamic Voxel Wrapping algorithm (DV\(^2\)W) to calculate DI for each patch. (2) A dual-branch diagnostic network (DBDN) is constructed to obtain intra-patch and inter-patch features synchronously. The intra-feature extraction branch extracts feature from each patch through a parallel multi-channel network and fuse them. The Inter-feature extraction branch defines the Spatial Context Mixing matrix (SCM) and performs feature extraction on SCM to obtain mutual information. The performance evaluation demonstrates that our DBDN model has adequate diagnostic performance compared to state-of-the-art methods. In addition, the distinguishing pathological locations identified by our DAPL algorithm can effectively guide inexperienced clinical doctors to identify lesion areas and guide doctors in diagnosis quickly.
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Discover the latest articles, news and stories from top researchers in related subjects.Data Availability
The dataset used in this study was obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI). More information regarding ADNI can be obtained from the following link:http://adni.loni.usc.edu/ (accessed on 16 July 2023).
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Acknowledgements
This research was funded by Science and Technology Development Project of Liaoning Province of China under Grant number 2021JH6/10500127 and Science Research Project of Liaoning Department of Education of China under Grant number LJKZ0008.
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School of Computer Science and Engineering, Northeastern University, Shenyang, China
Yue Tu, Shukuan Lin, Jianzhong Qiao, Kuankuan Hao & Yilin Zhuang
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Contributions
Yue Tu: Conceptualization, Methodology, Software, Validation, Formal Analysis, Writing - Original Draft, Visualization.Shukuan Lin: Resources, Supervision, Writing - Review & Editing.Jianzhong Qiao: Resources, Supervision.Kuankuan Hao: Writing - Review & Editing, Investigation, Validation, Response to Reviewers.Yilin Zhuang: Data Curation, Software. All authors have read and agreed to the published version of the manuscript.
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Correspondence toShukuan Lin.
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A benchmark dataset, Alzheimer’s Disease Neuroimaging Initiative(ADNI), which was used in our work, has obtained the informed consent from the participants. We have obtained permission to use this dataset. More information can be found in the following link:http://adni.loni.usc.edu/study-design/ (accessed on 16 July 2023).
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Tu, Y., Lin, S., Qiao, J.et al. A novel dual-branch Alzheimer’s disease diagnostic model based on distinguishing atrophic patch localization.Appl Intell54, 9067–9087 (2024). https://doi.org/10.1007/s10489-024-05663-z
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