心脏实时MRI中标签效率的稀疏贝叶斯学习
Sparse Bayesian Learning for Label Efficiency in Cardiac Real-Time MRI
- German Aerospace Center (DLR)（德国航空航天中心）
- RWTH Aachen University（亚琛工业大学）
- King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对心脏实时MRI中外部切片分割标签稀缺的问题，提出稀疏贝叶斯学习，利用内部切片识别稀疏频率指导外部切片标注选择，实现少量标注下的准确心室容积预测，并提供不确定性估计。
AI中文摘要：
心脏实时磁共振成像（MRI）是一种新兴技术，能以每秒高达50帧的速度对心脏成像，从而提供呼吸对心跳影响的洞察。然而，该方法显著增加了必须分割的图像数量，以推导关键健康指标。尽管神经网络在内部切片上表现良好，但外部切片上的预测往往不可靠。\n\n本研究提出稀疏贝叶斯学习（SBL），以在最少人工标注的情况下预测外部切片的心室容积，从而应对这一挑战。假设心室容积随时间的变化主要由对应于心脏和呼吸频率的稀疏频率主导。此外，SBL通过类型-II似然优化超参数，在分割良好的内部切片上识别这些稀疏频率，自动剪除不相关的成分。所识别的稀疏频率指导外部切片图像的选择以进行标注，从而最小化后验方差。\n\n本研究为贪心算法提供了性能保证。在患者数据上的测试表明，仅需少量标注图像即可实现准确的容积预测。标注过程有效避免了选择低效图像。此外，贝叶斯方法提供了不确定性估计，突出不可靠的预测（例如，在选择次优标签时）。
英文摘要：
Cardiac real-time magnetic resonance imaging (MRI) is an emerging technology that images the heart at up to 50 frames per second, offering insight into the respiratory effects on the heartbeat. However, this method significantly increases the number of images that must be segmented to derive critical health indicators. Although neural networks perform well on inner slices, predictions on outer slices are often unreliable. This work proposes sparse Bayesian learning (SBL) to predict the ventricular volume on outer slices with minimal manual labeling to address this challenge. The ventricular volume over time is assumed to be dominated by sparse frequencies corresponding to the heart and respiratory rates. Moreover, SBL identifies these sparse frequencies on well-segmented inner slices by optimizing hyperparameters via type -II likelihood, automatically pruning irrelevant components. The identified sparse frequencies guide the selection of outer slice images for labeling, minimizing posterior variance. This work provides performance guarantees for the greedy algorithm. Testing on patient data demonstrates that only a few labeled images are necessary for accurate volume prediction. The labeling procedure effectively avoids selecting inefficient images. Furthermore, the Bayesian approach provides uncertainty estimates, highlighting unreliable predictions (e.g., when choosing suboptimal labels).
