Brain Anatomy Prior Modeling to Forecast Clinical Progression of Cognitive Impairment with Structural MRI.
Brain structural MRI has been widely used to assess the future progression of cognitive impairment (CI). Previous learning-based studies usually suffer from the issue of small-sized labeled training data, while a huge amount of structural MRIs exist in large-scale public databases. Intuitively, brain anatomical structures derived from these public MRIs (even without task-specific label information) can boost CI progression trajectory prediction. However, previous studies seldom use such brain anatomy structure information as priors. To this end, this paper proposes a brain anatomy prior modeling (BAPM) framework to forecast the clinical progression of cognitive impairment with small-sized target MRIs by exploring anatomical brain structures. Specifically, the BAPM consists of a pretext model and a downstream model, with a shared brain anatomy-guided encoder to model brain anatomy prior using auxiliary tasks explicitly. Besides the encoder, the pretext model also contains two decoders for two auxiliary tasks (i.e., MRI reconstruction and brain tissue segmentation), while the downstream model relies on a predictor for classification. The brain anatomy-guided encoder is pre-trained with the pretext model on 9,344 auxiliary MRIs without diagnostic labels for anatomy prior modeling. With this encoder frozen, the downstream model is then fine-tuned on limited target MRIs for prediction. We validate BAPM on two CI-related studies with T1-weighted MRIs from 448 subjects. Experimental results suggest the effectiveness of BAPM in (1) four CI progression prediction tasks, (2) MR image reconstruction, and (3) brain tissue segmentation, compared with several state-of-the-art methods.
Duke Scholars
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Related Subject Headings
- Artificial Intelligence & Image Processing
- 4611 Machine learning
- 4605 Data management and data science
- 4603 Computer vision and multimedia computation
- 0906 Electrical and Electronic Engineering
- 0806 Information Systems
- 0801 Artificial Intelligence and Image Processing
Citation
Published In
DOI
ISSN
Publication Date
Volume
Location
Related Subject Headings
- Artificial Intelligence & Image Processing
- 4611 Machine learning
- 4605 Data management and data science
- 4603 Computer vision and multimedia computation
- 0906 Electrical and Electronic Engineering
- 0806 Information Systems
- 0801 Artificial Intelligence and Image Processing