Qi Xiaoming, He Yuting, Yang Guanyu, Chen Yang, Yang Jian, Liu Wangyag, Zhu Yinsu, Xu Yi, Shu Huazhong, Li Shuo
IEEE J Biomed Health Inform. 2022 May;26(5):2264-2275. doi: 10.1109/JBHI.2021.3122581. Epub 2022 May 5.
The accurate 3D left ventricular (LV) myocardium segmentation in short-axis (SAX) view of cardiac magnetic resonance (CMR) is challenged by the sparse spatial structure of CMR. The strategy of multi-view CMR fusion can provide fine-grained spatial structure for accurate segmentation. However, the large information misalignment and lack of dense 3D CMR as fusion target in multi-view CMR fusion, and the different spatial resolution between the fusion result and the ground truth in segmentation limit the strategy. In this study, we propose a multi-view spatial-aware adversarial network (MVSGAN). It studies the perception of fine-grained cardiac structure for accurate segmentation by the spatialaware multi-view CMR fusion. It consists of three modules: (1) A residual adversarial fusion (RAF) module takes inter-slices deep correlation and anatomical prior to refine the spatial structures by residual supplement and adversarial optimization. (2) A structural perception-aggregation (SPA) module establishes the spatial correlation between the dense cardiac model and sparse label for accurate CMR LV myocardium segmentation. (3) A joint training strategy utilizes the dense SAX volume as explicit and implicit goals to jointly optimize the framework. The experiments are applied on a public dataset and a clinical dataset to evaluate the performance of MVSGAN. The average Dice and Jaccard score of LV myocardium segmentation obtained by MVSGAN are highest among seven existing state-of-the-art methods, which are up to 0.92 and 0.75. It is concluded that the spatial-aware multi-view CMR fusion can provide meaningful spatial correlation for accurate LV myocardium segmentation.
心脏磁共振成像（CMR）短轴（SAX）视图中准确的三维左心室（LV）心肌分割面临着CMR稀疏空间结构的挑战。多视图CMR融合策略可为准确分割提供细粒度的空间结构。然而，多视图CMR融合中存在大量信息错位，且缺乏作为融合目标的密集三维CMR，以及分割中融合结果与真实情况之间不同的空间分辨率限制了该策略。在本研究中，我们提出了一种多视图空间感知对抗网络（MVSGAN）。它通过空间感知多视图CMR融合研究细粒度心脏结构的感知以进行准确分割。它由三个模块组成：（1）一个残差对抗融合（RAF）模块利用切片间深度相关性和解剖学先验，通过残差补充和对抗优化来细化空间结构。（2）一个结构感知聚合（SPA）模块建立密集心脏模型与稀疏标签之间的空间相关性，以实现准确的CMR LV心肌分割。（3）一种联合训练策略利用密集的SAX体积作为显式和隐式目标来联合优化框架。实验应用于一个公共数据集和一个临床数据集，以评估MVSGAN的性能。MVSGAN获得的LV心肌分割的平均Dice和Jaccard分数在七种现有的最先进方法中最高，分别高达0.92和0.75。得出的结论是，空间感知多视图CMR融合可为准确的LV心肌分割提供有意义的空间相关性。
