Real-Time Deep Pose Estimation With Geodesic Loss for Image-to-Template Rigid Registration.
Real-Time Deep Pose Estimation With Geodesic Loss for Image-to-Template Rigid Registration.
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DOI：
10.1109/tmi.2018.2866442
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发表时间：
2019-03
影响因子：
10.6
通讯作者：
Gholipour A
中科院分区：
文献类型：
--
作者：
Mohseni Salehi SS;Khan S;Erdogmus D;Gholipour A
为了增加捕获范围并加速最先进的主体间和主体到模板 3D 刚性配准的性能，我们提出了基于深度学习的方法，这些方法经过训练，可以根据医学图像的切片或体积在规范空间中找到任意方向的主体或解剖结构的 3D 位置。为此，我们提出了回归卷积神经网络 (CNN)，它学习使用图像特征来预测 3D 旋转和平移的角轴表示。我们使用并比较均方误差和测地线损失来训练回归 CNN，以用于两种不同场景中使用的 3D 姿态估计：切片到体积配准和体积到体积配准。作为示例性应用，我们应用所提出的方法将在宽孕龄范围内扫描的胎儿的任意方向的重建图像配准到标准图集空间。我们的结果表明，在这种可修正学习的配准应用中，所提出的具有测地线损失最小化的深度学习方法实现了宽实时捕获范围（< 100ms）的 3D 姿态估计。我们还测试了经过训练的 CNN 在扩大的年龄范围以及具有相似和不同 MR 图像对比度的新生儿图像上的泛化能力。我们在 T2 加权胎儿大脑 MRI 扫描上训练模型，并使用它们根据 T1 加权 MRI 扫描预测新生儿大脑的 3D 姿势。我们表明，当我们通过条件生成对抗网络执行图像对比度转移时，训练后的模型可以很好地推广到新领域。这表明训练后的深度回归 CNN 的应用领域可以进一步扩展到训练中使用之外的图像模态和对比度。我们提出的方法与基于加速优化的配准算法的结合可以显着提高自动成像设备和未来图像处理方法的性能。
With an aim to increase the capture range and accelerate the performance of state-of-the-art inter-subject and subject-to-template 3D rigid registration, we propose deep learning-based methods that are trained to find the 3D position of arbitrarily oriented subjects or anatomy in a canonical space based on slices or volumes of medical images. For this, we propose regression convolutional neural networks (CNNs) that learn to predict the angle-axis representation of 3D rotations and translations using image features. We use and compare mean square error and geodesic loss to train regression CNNs for 3D pose estimation used in two different scenarios: slice-to-volume registration and volume-to-volume registration. As an exemplary application, we applied the proposed methods to register arbitrarily oriented reconstructed images of fetuses scanned in-utero at a wide gestational age range to a standard atlas space. Our results show that in such registration applications that are amendable to learning, the proposed deep learning methods with geodesic loss minimization achieved 3D pose estimation with a wide capture range in real-time (< 100ms). We also tested the generalization capability of the trained CNNs on an expanded age range and on images of newborn subjects with similar and different MR image contrasts. We trained our models on T2-weighted fetal brain MRI scans and used them to predict the 3D pose of newborn brains based on T1-weighted MRI scans. We showed that the trained models generalized well for the new domain when we performed image contrast transfer through a conditional generative adversarial network. This indicates that the domain of application of the trained deep regression CNNs can be further expanded to image modalities and contrasts other than those used in training. A combination of our proposed methods with accelerated optimization-based registration algorithms can dramatically enhance the performance of automatic imaging devices and image processing methods of the future.