Anatomical Predictions using Subject-Specific Medical Data

• URL: http://arxiv.org/abs/2006.00090v1
• Date: Fri, 29 May 2020 21:30:46 GMT
• Title: Anatomical Predictions using Subject-Specific Medical Data
• Authors: Marianne Rakic, John Guttag and Adrian V. Dalca
• Abstract summary: We present a method that predicts how a brain MRI for an individual will change over time. Given a predicted deformation field, a baseline scan can be warped to give a prediction of the brain scan at a future time.
• Score: 7.635279671482444
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Changes over time in brain anatomy can provide important insight for treatment design or scientific analyses. We present a method that predicts how a brain MRI for an individual will change over time. We model changes using a diffeomorphic deformation field that we predict using function using convolutional neural networks. Given a predicted deformation field, a baseline scan can be warped to give a prediction of the brain scan at a future time. We demonstrate the method using the ADNI cohort, and analyze how performance is affected by model variants and the subject-specific information provided. We show that the model provides good predictions and that external clinical data can improve predictions.

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