Department of Radiation Oncology, University of California, San Francisco, CA, United States of America.
Phys Med Biol. 2018 Sep 17;63(18):185017. doi: 10.1088/1361-6560/aada66.
The purpose of the work is to develop a deep unsupervised learning strategy for cone-beam CT (CBCT) to CT deformable image registration (DIR). This technique uses a deep convolutional inverse graphics network (DCIGN) based DIR algorithm implemented on 2 Nvidia 1080 Ti graphics processing units. The model is comprised of an encoding and decoding stage. The fully-convolutional encoding stage learns hierarchical features and simultaneously forms an information bottleneck, while the decoding stage restores the original dimensionality of the input image. Activations from the encoding stage are used as the input channels to a sparse DIR algorithm. DCIGN was trained using a distributive learning-based convolutional neural network architecture and used 285 head and neck patients to train, validate, and test the algorithm. The accuracy of the DCIGN algorithm was evaluated on 100 synthetic cases and 12 hold out test patient cases. The results indicate that DCIGN performed better than rigid registration, intensity corrected Demons, and landmark-guided deformable image registration for all evaluation metrics. DCIGN required ~14 h to train, and ~3.5 s to make a prediction on a 512 × 512 × 120 voxel image. In conclusion, DCIGN is able to maintain high accuracy in the presence of CBCT noise contamination, while simultaneously preserving high computational efficiency.
本工作旨在为锥形束 CT（CBCT）至 CT 变形图像配准（DIR）开发一种深度无监督学习策略。该技术使用基于深度卷积逆图形网络（DCIGN）的 DIR 算法，在 2 个 Nvidia 1080 Ti 图形处理单元上实现。该模型由编码和解码阶段组成。完全卷积编码阶段学习分层特征，同时形成信息瓶颈，而解码阶段恢复输入图像的原始维度。来自编码阶段的激活用作稀疏 DIR 算法的输入通道。DCIGN 采用基于分布式学习的卷积神经网络架构进行训练，并使用 285 个头颈部患者对算法进行训练、验证和测试。DCIGN 算法的准确性在 100 个合成病例和 12 个保留测试患者病例上进行了评估。结果表明，对于所有评估指标，DCIGN 的性能均优于刚性配准、强度校正 Demons 和基于标记的变形图像配准。DCIGN 训练需要约 14 小时，对 512 × 512 × 120 体素图像进行预测需要约 3.5 秒。总之，DCIGN 能够在存在 CBCT 噪声污染的情况下保持高精度，同时保持高计算效率。
