Accurate segmentation of cardiac bi-ventricle (CBV) from magnetic resonance (MR) images has a great significance to analyze and evaluate the function of the cardiovascular system. However, the complex structure of CBV image makes fully automatic segmentation as a well-known challenge. In this paper, we propose an improved end-to-end encoder-decoder network for CBV segmentation from the pixel level view (Cardiac-DeepIED). In our framework, we explicitly solve the high variability of complex cardiac structures through an improved encoder-decoder architecture which consists of Fire dilated modules and D-Fire dilated modules. This improved encoder-decoder architecture has the advantages of being capable of obtaining semantic task-aware representation and preserving fine-grained information. In addition, our method can dynamically capture potential spatiotemporal correlations between consecutive cardiac MR images through specially designed convolutional long-term and short-term memory structure; it can simulate spatiotemporal contexts between consecutive frame images. The combination of these modules enables the entire network to get an accurate, robust segmentation result. The proposed method is evaluated on the 145 clinical subjects with leave-one-out cross-validation. The average dice metric (DM) is up to 0.96 (left ventricle), 0.89 (myocardium), and 0.903 (right ventricle). The performance of our method outperforms state-of-the-art methods. These results demonstrate the effectiveness and advantages of our method for CBV regions segmentation at the pixel-level. It also reveals the proposed automated segmentation system can be embedded into the clinical environment to accelerate the quantification of CBV and expanded to volume analysis, regional wall thickness analysis, and three LV dimensions analysis.

中文翻译：

---

Cardiac-DeepIED：使用改进的端到端编码器-解码器网络对心脏双心室进行自动像素级深度分割

磁共振（MR）图像中心脏双心室（CBV）的精确分割对于分析和评估心血管系统的功能具有重要意义。然而，CBV图像的复杂结构使得全自动分割成为众所周知的挑战。在本文中，我们提出了一种改进的端到端编码器-解码器网络，用于从像素级视图（Cardiac-DeepIED）进行CBV分割。在我们的框架中，我们通过改进的编码器-解码器架构明确解决了复杂心脏结构的高变异性，该架构由 Fire 扩张模块和 D-Fire 扩张模块组成。这种改进的编码器-解码器架构的优点是能够获得语义任务感知表示并保留细粒度信息。此外，我们的方法可以通过专门设计的卷积长期和短期记忆结构动态捕获连续心脏MR图像之间潜在的时空相关性；它可以模拟连续帧图像之间的时空上下文。这些模块的组合使整个网络能够获得准确、稳健的分割结果。所提出的方法在 145 名临床受试者上进行了留一交叉验证的评估。平均骰子指标 (DM) 高达 0.96（左心室）、0.89（心肌）和 0.903（右心室）。我们的方法的性能优于最先进的方法。这些结果证明了我们的像素级 CBV 区域分割方法的有效性和优势。它还揭示了所提出的自动分割系统可以嵌入到临床环境中，以加速 CBV 的量化，并扩展到体积分析、区域壁厚分析和 LV 三个维度分析。