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.

翻译：心脏磁共振（CMR）电影图像的精确双心室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）的分割仍然更具挑战性且可重复性较低。性能退化经常发生在右心室基底，该处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发生变化。在本工作中，我们提出通过两种策略来解决当前CMR分割中尚未解决的问题，特别是在右心室基底：首先，我们在心脏病学专家的指导下，通过重新标注ACDC数据集中的右心室基底，细化描绘右心室流出道（RVOT），从而补充了公共资源。其次，我们提出了一种新颖的双编码器U-Net架构，该架构利用时间非相干性在发生平面间运动时指导分割。平面间运动通过运动追踪模型的贝叶斯不确定性，以追踪丢失为特征进行表征。我们的实验表明，考虑到时间非相干性，我们的方法显著改善了右心室基底的分割。此外，我们研究了基于深度学习的分割的可重复性，结果表明，一致的标注与追踪丢失相结合可以增强右心室分割的可重复性，这可能有助于大量专注于右心室的临床研究。