Online learning in motion modeling for intra-interventional image sequences
This work addresses the need for better image guidance during medical procedures, though it appears incremental as it builds on existing state-space models with specific adaptations.
The paper tackled the problem of low sampling frequency in medical image sequences by developing a probabilistic motion model that estimates and forecasts motion between acquired images, achieving improved forecasting performance through patient-specific online learning.
Image monitoring and guidance during medical examinations can aid both diagnosis and treatment. However, the sampling frequency is often too low, which creates a need to estimate the missing images. We present a probabilistic motion model for sequential medical images, with the ability to both estimate motion between acquired images and forecast the motion ahead of time. The core is a low-dimensional temporal process based on a linear Gaussian state-space model with analytically tractable solutions for forecasting, simulation, and imputation of missing samples. The results, from two experiments on publicly available cardiac datasets, show reliable motion estimates and an improved forecasting performance using patient-specific adaptation by online learning.