Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei, China.
School of Computer Science and Technology, Anhui University, Hefei, China.
Int J Comput Assist Radiol Surg. 2020 Apr;15(4):589-600. doi: 10.1007/s11548-020-02128-9. Epub 2020 Feb 26.
Left atrium segmentation and visualization serve as a fundamental and crucial role in clinical analysis and understanding of atrial fibrillation. However, most of the existing methods are directly transmitting information, which may cause redundant information to be passed to affect segmentation performance. Moreover, they did not further consider atrial visualization after segmentation, which leads to a lack of understanding of the essential atrial anatomy.
We propose a novel unified deep learning framework for left atrium segmentation and visualization simultaneously. At first, a novel dual-path module is used to enhance the expressiveness of cardiac image representation. Then a multi-scale context-aware module is designed to effectively handle complex appearance and shape variations of the left atrium and associated pulmonary veins. The generated multi-scale features are feed to gated bidirectional message passing module to remove irrelevant information and extract discriminative features. Finally, the features after message passing are efficiently combined via a deep supervision mechanism to produce the final segmentation result and reconstruct 3D volumes.
Our approach primarily against the 2018 left atrium segmentation challenge dataset, which consists of 100 3D gadolinium-enhanced magnetic resonance images. Our method achieves an average dice of 0.936 in segmenting the left atrium via fivefold cross-validation, which outperforms state-of-the-art methods.
The performance demonstrates the effectiveness and advantages of our network for the left atrium segmentation and visualization. Therefore, our proposed network could potentially improve the clinical diagnosis and treatment of atrial fibrillation.
左心房分割和可视化在房颤的临床分析和理解中起着基础和关键作用。然而，现有的大多数方法都是直接传输信息，这可能会导致冗余信息被传递，从而影响分割性能。此外，它们在分割后没有进一步考虑心房的可视化，这导致对心房基本解剖结构的理解不足。
我们提出了一种新颖的左心房分割和可视化的统一深度学习框架。首先，使用一种新颖的双路径模块来增强心脏图像表示的表现力。然后设计了一个多尺度上下文感知模块，以有效处理左心房及其相关肺静脉的复杂外观和形状变化。生成的多尺度特征被馈送到门控双向消息传递模块中，以去除不相关的信息并提取有区别的特征。最后，通过深度监督机制有效地组合消息传递后的特征，以产生最终的分割结果并重建 3D 体积。
我们的方法主要针对 2018 年的左心房分割挑战数据集，该数据集包含 100 个 3D 钆增强磁共振图像。我们的方法通过五折交叉验证在左心房分割方面的平均骰子系数达到 0.936，优于最先进的方法。
该性能证明了我们的网络在左心房分割和可视化方面的有效性和优势。因此，我们提出的网络有可能改善房颤的临床诊断和治疗。
