论文标题
使用无监督的深度学习的3D纵向腹部CT图像的可变形登记
3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning
论文作者
论文摘要
这项研究研究了无监督的深度学习框架素摩尔普的使用，用于在乳腺癌骨转移患者中获得的腹腹部腹腔CT图像的可变形登记。 CT图像在注册之前通过自动删除CT表和所有其他体外组件进行了完善。为了提高VoxelMorph的学习能力时，只有有限的培训数据可用，基于连续的CT图像的模拟变形，提出了一种新颖的增量训练策略。在4倍的交叉验证方案中，与单个体积的训练相比，增量训练策略的注册性能明显更好。尽管我们的图像注册方法在注册质量方面使用NiftyReg（被认为是基准）并未胜过迭代注册，但注册的速度约为300倍。这项研究表明，基于模拟变形的新型增量训练策略，基于深度学习的纵向腹腔CT图像的可行性可行性。
This study investigates the use of the unsupervised deep learning framework VoxelMorph for deformable registration of longitudinal abdominopelvic CT images acquired in patients with bone metastases from breast cancer. The CT images were refined prior to registration by automatically removing the CT table and all other extra-corporeal components. To improve the learning capabilities of VoxelMorph when only a limited amount of training data is available, a novel incremental training strategy is proposed based on simulated deformations of consecutive CT images. In a 4-fold cross-validation scheme, the incremental training strategy achieved significantly better registration performance compared to training on a single volume. Although our deformable image registration method did not outperform iterative registration using NiftyReg (considered as a benchmark) in terms of registration quality, the registrations were approximately 300 times faster. This study showed the feasibility of deep learning based deformable registration of longitudinal abdominopelvic CT images via a novel incremental training strategy based on simulated deformations.
