追踪迷失：右心室基底部心脏电影 MRI 分割的不确定性引导方法
Lost in Tracking: Uncertainty-guided Cardiac Cine MRI Segmentation at Right Ventricle Base
AI总结：
本文针对心脏 MRI 中右心室基底部因平面间运动导致分割困难的问题，提出结合专家重新标注 ACDC 数据集和利用运动追踪不确定性引导的双编码器 U-Net 架构，显著提升了右心室基底部分割性能与可重复性。
摘要：
心脏磁共振（CMR）电影图像的双心室精确分割对于心脏功能的临床评估至关重要。然而，与左心室（LV）相比，右心室（RV）分割仍然更具挑战性且可重复性更低。性能退化经常发生在右心室基底部，该处的平面内解剖结构复杂（包含心房、瓣膜和主动脉），并且由于强烈的平面间运动而变化。在本工作中，我们提出用两种策略来解决 CMR 分割中目前尚未解决的问题，特别是右心室基底部的问题：首先，我们在心脏病专家的指导下，通过重新标注 ACDC 数据集中的右心室基底部，并细化右心室流出道（RVOT）的勾画，补充了公共资源。其次，我们提出了一种新颖的双编码器 U-Net 架构，该架构利用时间不连贯性在平面间运动发生时指导分割。平面间运动通过运动追踪模型的贝叶斯不确定性，以追踪丢失来表征。我们的实验表明，在考虑时间不连贯性的情况下，我们的方法显著改善了右心室基底部分割。此外，我们研究了基于深度学习的分割的可重复性，并表明一致标注与追踪丢失的结合可以提高右心室分割的可重复性，从而可能促进大量以右心室为重点的临床研究。
英文摘要：
Accurate biventricular segmentation of cardiac magnetic resonance (CMR) cine images is essential for the clinical evaluation of heart function. However, compared to left ventricle (LV), right ventricle (RV) segmentation is still more challenging and less reproducible. Degenerate performance frequently occurs at the RV base, where the in-plane anatomical structures are complex (with atria, valve, and aorta) and vary due to the strong interplanar motion. In this work, we propose to address the currently unsolved issues in CMR segmentation, specifically at the RV base, with two strategies: first, we complemented the public resource by reannotating the RV base in the ACDC dataset, with refined delineation of the right ventricle outflow tract (RVOT), under the guidance of an expert cardiologist. Second, we proposed a novel dual encoder U-Net architecture that leverages temporal incoherence to inform the segmentation when interplanar motions occur. The inter-planar motion is characterized by loss-of-tracking, via Bayesian uncertainty of a motion-tracking model. Our experiments showed that our method significantly improved RV base segmentation taking into account temporal incoherence. Furthermore, we investigated the reproducibility of deep learning-based segmentation and showed that the combination of consistent annotation and loss of tracking could enhance the reproducibility of RV segmentation, potentially facilitating a large number of clinical studies focusing on RV.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
