用在线学习建模医疗影像运动，填补低采样率下的缺失帧。
Online learning in motion modeling for intra-interventional image sequences
- 基于线性高斯状态空间模型，实现运动估计与前瞻预测
- 在心脏影像数据集上，患者自适应在线学习提升预测精度
- 适合需要实时影像重建的介入手术场景
医学检查中的图像监测有助于诊断与治疗，但采样频率常过低，导致图像缺失。本文提出一种概率运动模型，可估计已获取图像间的运动并提前预测后续运动。核心是基于线性高斯状态空间模型的低维时序过程，具备解析可解的预测、模拟与缺失样本补全能力。在两个公开心脏数据集上的实验表明，通过在线学习进行患者特异性适应，能获得可靠的运动估计，并显著提升预测性能。
原文摘要 · Abstract (English)
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.
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