Neural networks have been proposed for medical image registration by learning, with a substantial amount of training data, the optimal transformations between image pairs. These trained networks can further be optimized on a single pair of test images - known as test-time optimization. This work formulates image registration as a meta-learning algorithm. Such networks can be trained by aligning the training image pairs while simultaneously improving test-time optimization efficacy; tasks which were previously considered two independent training and optimization processes. The proposed meta-registration is hypothesized to maximize the efficiency and effectiveness of the test-time optimization in the "outer" meta-optimization of the networks. For image guidance applications that often are time-critical yet limited in training data, the potentially gained speed and accuracy are compared with classical registration algorithms, registration networks without meta-learning, and single-pair optimization without test-time optimization data. Experiments are presented in this paper using clinical transrectal ultrasound image data from 108 prostate cancer patients. These experiments demonstrate the effectiveness of a meta-registration protocol, which yields significantly improved performance relative to existing learning-based methods. Furthermore, the meta-registration achieves comparable results to classical iterative methods in a fraction of the time, owing to its rapid test-time optimization process.
翻译:提议建立神经网络,以便通过学习进行医学图像登记; 提议通过学习进行医学图像登记,提供大量培训数据,使图像配对之间实现最佳转换; 这些经过培训的网络可以进一步优化,使用单一一对测试图像 -- -- 称为测试时间优化; 这项工作将图像登记作为一种元学习算法; 可以通过对培训图像配对,同时提高测试时间优化效果来培训这些网络; 以前认为是两个独立的培训和优化过程的任务; 拟议的元登记是假设的,以最大限度地提高网络“出”元优化中测试时间优化的效率和有效性; 对于往往时间紧迫但培训数据有限的图像指导应用,可能增加的速度和准确性与古典登记算法、不学习元的登记网络和不使用测试时间优化数据的单面优化相比较。 本文中将使用108个前列腺癌症病人的临床转基因超声波图像数据进行实验。 这些实验表明元登记协议的效力,与现有学习方法相比,其业绩得到显著改善。 此外,元更新的升级速度和精确度将达到可比较的复制方法。