[论文解读] VoxelAtlasGAN: 3D Left Ventricle Segmentation on Echocardiography with Atlas Guided Generation and Voxel-to-voxel Discrimination

Suyu Dong, Gongning Luo+6 more|arXiv (Cornell University)|Jun 10, 2018

Advanced Neural Network Applications参考文献 16被引用 7

一句话总结

本文提出VoxelAtlasGAN，一种新颖的3D左心室分割框架，用于超声心动图，采用基于图谱引导生成和体素级判别机制的体素到体素条件生成对抗网络（cGAN）。该方法将3D左心室图谱作为先验知识，并引入体积一致性约束，实现了高精度（Dice = 0.953）和实时推理（每体积0.1秒），展现出在3D超声心动图分割中强大的临床潜力。

ABSTRACT

3D left ventricle (LV) segmentation on echocardiography is very important for diagnosis and treatment of cardiac disease. It is not only because of that echocardiography is a real-time imaging technology and widespread in clinical application, but also because of that LV segmentation on 3D echocardiography can provide more full volume information of heart than LV segmentation on 2D echocardiography. However, 3D LV segmentation on echocardiography is still an open and challenging task owing to the lower contrast, higher noise and data dimensionality, limited annotation of 3D echocardiography. In this paper, we proposed a novel real-time framework, i.e., VoxelAtlasGAN, for 3D LV segmentation on 3D echocardiography. This framework has three contributions: 1) It is based on voxel-to-voxel conditional generative adversarial nets (cGAN). For the first time, cGAN is used for 3D LV segmentation on echocardiography. And cGAN advantageously fuses substantial 3D spatial context information from 3D echocardiography by self-learning structured loss; 2) For the first time, it embeds the atlas into an end-to-end optimization framework, which uses 3D LV atlas as a powerful prior knowledge to improve the inference speed, address the lower contrast and the limited annotation problems of 3D echocardiography; 3) It combines traditional discrimination loss and the new proposed consistent constraint, which further improves the generalization of the proposed framework. VoxelAtlasGAN was validated on 60 subjects on 3D echocardiography and it achieved satisfactory segmentation results and high inference speed. The mean surface distance is 1.85 mm, the mean hausdorff surface distance is 7.26 mm, mean dice is 0.953, the correlation of EF is 0.918, and the mean inference speed is 0.1s. These results have demonstrated that our proposed method has great potential for clinical application

研究动机与目标

• 解决3D超声心动图中对比度低、噪声高及标注有限带来的左心室精准分割挑战。
• 通过将3D左心室图谱作为先验知识整合进深度学习框架，提升分割精度和推理速度。
• 通过结合传统对抗损失与基于归一化互信息的新型体积一致性约束，增强模型泛化能力。
• 开发一种实时、端到端的框架，实现高精度3D左心室分割，适用于临床部署。

提出的方法

• 该框架采用体素到体素的条件生成对抗网络（cGAN），以建模3D空间上下文并生成精确的分割图。
• 在生成器中嵌入3D左心室图谱作为先验知识，以指导分割并加速推理。
• 提出一种基于归一化互信息的新型体积一致性约束，以强制输入与生成体积之间的结构相似性。
• 损失函数结合标准对抗判别损失与体积一致性约束，同时优化生成结果的真实感与解剖学保真度。
• 使用随机梯度下降进行端到端训练，学习率为0.0002，动量为0.5。
• 推理仅使用生成器，在单张GPU上实现每体积0.1秒的实时性能。

实验结果

研究问题

• RQ1 体素到体素的cGAN框架是否能在低对比度、高噪声的3D超声心动图上实现高精度的3D左心室分割？
• RQ2 将3D左心室图谱作为先验知识的整合，在提升分割精度和推理速度方面效果如何？
• RQ3 与标准对抗训练相比，所提出的体积一致性约束是否能增强模型泛化能力？
• RQ4 该框架是否能在保持高分割精度的同时实现实时推理，适用于临床3D超声心动图数据？

主要发现

• 所提出的VoxelAtlasGAN在60名受试者上实现了0.953的平均Dice系数，表明分割精度高。
• 平均表面距离为1.85 mm，平均豪斯多夫表面距离为7.26 mm，表明边界定位精确。
• 分割结果与真实值的射血分数（EF）值相关系数为0.918，表明临床一致性强。
• 该框架实现了每体积0.1秒的推理速度，支持实时临床应用。
• 消融实验证实，图谱先验和体积一致性约束均显著提升了分割性能。
• 即使不使用图谱或一致性约束，体素到体素的cGAN仍优于3D U-Net基线模型，显示出内在鲁棒性。