Deep Atlas Network for Efficient 3D Left Ventricle Segmentation on Echocardiography
Deep Atlas Network for Efficient 3D Left Ventricle Segmentation on Echocardiography
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DOI：
10.1016/j.media.2020.101638
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发表时间：
2020-04-01
影响因子：
10.9
通讯作者：
Li, Shuo
中科院分区：
文献类型：
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作者：
Dong, Suyu;Luo, Gongning;Li, Shuo
我们提出了一种在超声心动图上进行 3D 左心室 (LV) 分割的新颖有效方法，这对于心脏病的诊断非常重要。该方法有效克服了3D超声心动图的挑战：高维数据、复杂的解剖环境和有限的注释数据。首先，我们提出了一种深度图谱网络，将左心室图集集成到深度学习框架中，首次解决超声心动图上的3D左心室分割问题，并在有限的注释数据的基础上提高了性能。其次，我们提出了一种新颖的信息一致性约束，从不同层面同时增强模型的性能，最终实现了复杂解剖环境下3D LV分割的有效优化。最后，该方法以端到端反向传播的方式进行优化，即使在高维数据下也能实现较高的推理效率，满足临床实践的效率要求。实验证明，与最先进的方法相比，该方法取得了更好的分割结果和更高的推理速度。平均表面距离、平均豪斯多夫表面距离和平均骰子指数分别为1.52毫米、5.6毫米和0.97。此外，该方法效率高，推理时间为0.02s。实验结果证明该方法在超声心动图3D LV分割方面具有潜在的临床应用价值。 (C) 2020 Elsevier B.V. 保留所有权利。
We proposed a novel efficient method for 3D left ventricle (LV) segmentation on echocardiography, which is important for cardiac disease diagnosis. The proposed method effectively overcame the 3D echocardiography's challenges: high dimensional data, complex anatomical environments, and limited annotation data. First, we proposed a deep atlas network, which integrated LV atlas into the deep learning framework to address the 3D LV segmentation problem on echocardiography for the first time, and improved the performance based on limited annotation data. Second, we proposed a novel information consistency constraint to enhance the model's performance from different levels simultaneously, and finally achieved effective optimization for 3D LV segmentation on complex anatomical environments. Finally, the proposed method was optimized in an end-to-end back propagation manner and it achieved high inference efficiency even with high dimensional data, which satisfies the efficiency requirement of clinical practice. The experiments proved that the proposed method achieved better segmentation results and a higher inference speed compared with state-of-the-art methods. The mean surface distance, mean hausdorff surface distance, and mean dice index were 1.52 mm, 5.6 mm and 0.97 respectively. What's more, the method is efficient and its inference time is 0.02s. The experimental results proved that the proposed method has a potential clinical application for 3D LV segmentation on echocardiography. (C) 2020 Elsevier B.V. All rights reserved.