Non-rigid registration is a necessary but challenging task in medical imaging studies. Recently, unsupervised registration models have shown good performance, but they often require a large-scale training dataset and long training times. Therefore, in real world application where only dozens to hundreds of image pairs are available, existing models cannot be practically used. To address these limitations, we propose a novel unsupervised registration model which is integrated with a gradient-based meta learning framework. In particular, we train a meta learner which finds an initialization point of parameters by utilizing a variety of existing registration datasets. To quickly adapt to various tasks, the meta learner was updated to get close to the center of parameters which are fine-tuned for each registration task. Thereby, our model can adapt to unseen domain tasks via a short fine-tuning process and perform accurate registration. To verify the superiority of our model, we train the model for various 2D medical image registration tasks such as retinal choroid Optical Coherence Tomography Angiography (OCTA), CT organs, and brain MRI scans and test on registration of retinal OCTA Superficial Capillary Plexus (SCP). In our experiments, the proposed model obtained significantly improved performance in terms of accuracy and training time compared to other registration models.
翻译:在医学成像研究中,非硬性登记是一项必要但具有挑战性的任务。最近,未经监督的登记模式表现良好,但往往需要大规模的培训数据集和长时间的培训时间。因此,在只有数十至数百对图像配对的实实在在的应用中,现有模式无法实际使用。为了解决这些局限性,我们提议了一种新的未经监督的登记模式,该模式与基于梯度的元学习框架相结合。特别是,我们培训了一位元学习者,该学习者通过利用现有的各种登记数据集,发现一个初始参数点。为了迅速适应各种任务,对元学习者进行了更新,以接近为每项登记任务进行精确调整的参数中心。因此,我们的模型可以通过短期微调程序适应隐蔽的域任务,并进行准确的登记。为了核实我们模型的优越性,我们为各种2D医学图像登记任务培训模型,例如,通过使用各种现有的登记数据集,发现初始光调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调成成成成成成成成成成成成成成成成型的成型的成型系统。CT器官,对元学习机的学习机进行了升级校校校校校校校校校校校校校校校校,对和测测测测制成成成成成成型模型模模模模模模模模模模模模模模模模模模模模模模的校的校的校模模模模模模模模模模模模模模模模模模模模模模模模模模模模模模模模模模模模模模模模模机,并比。