Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis.

Area of Science:

• Medical Computer Vision
• Artificial Intelligence in Healthcare
• Biomedical Image Analysis

Background:

• Self-supervised learning (SSL) in medical imaging leverages anatomical self-similarity for pretraining, but current methods assume independent and identically distributed (i.i.d.) data, unsuitable for longitudinal studies.
• Existing SSL techniques for image-to-image architectures often focus on spatial or temporal similarity independently, failing to effectively utilize multi-scale spatiotemporal information in longitudinal data.
• Naive extensions of single-scale methods to multi-scale spatiotemporal analysis can lead to degenerate solutions, limiting their applicability to complex medical imaging scenarios.

Purpose of the Study:

• To develop a novel local and multi-scale spatiotemporal representation learning method for image-to-image architectures trained on longitudinal medical images.
• To introduce a self-supervised segmentation consistency regularization technique for finetuning, effectively utilizing intra-subject correlations in longitudinal data.
• To enhance the performance and longitudinal consistency of medical image segmentation tasks using self-supervised pretraining on longitudinal datasets.

Main Methods:

• Proposed a spatiotemporal representation learning method that exploits multi-scale intra-subject image feature similarity for pretraining on longitudinal datasets.
• Developed feature-wise regularizations to prevent degenerate representations during the pretraining phase.
• Introduced a self-supervised segmentation consistency regularization during finetuning to leverage intra-subject correlations.

Main Results:

• The proposed framework significantly outperformed well-tuned randomly-initialized baselines and existing self-supervised techniques on various segmentation tasks.
• Demonstrated superior performance and improved longitudinal consistency on both adult neurodegenerative MRI and infant brain MRI datasets.
• The method effectively captures local and multi-scale spatiotemporal features, addressing limitations of i.i.d. assumptions in medical imaging.

Conclusions:

• The developed method provides a robust approach for self-supervised learning on longitudinal medical images, enhancing segmentation accuracy and consistency.
• This work advances the field of medical computer vision by enabling effective utilization of temporal information in clinical study designs.
• The proposed techniques offer a promising direction for improving automated analysis of dynamic biological processes and disease progression using MRI.