At present, deep learning is widely used and has achieved excellent results in many fields except in the field of image registration, the reasons are two-fold: Firstly all the steps of deep learning should be derivable; nevertheless, the nonlinear deformation which is usually used in registration algorithms is hard to be depicted by explicit function. Secondly, success of deep learning is based on a large amount of labeled data, this is problematic for the application in real scenes. To address these concerns, we propose an unsupervised network for image registration. In order to integrate registration process into deep learning, image deformation is achieved by resampling, which can make deformation step derivable. The network optimizes its parameters directly by minimizing the loss between registered image and reference image without ground truth. To further improve algorithm's accuracy and speed, we incorporate coarse-to-fine multi-scale iterative scheme. We apply our method to register microscopic section images of neuron tissue. Compared with highly fine-tuning method sift flow, our method achieves similar accuracy with much less time.

Date Published: 6 March 2018
PDF: 8 pages
Proc. SPIE 10581, Medical Imaging 2018: Digital Pathology, 105811D (6 March 2018); doi: 10.1117/12.2293264

Published in SPIE Proceedings Vol. 10581:
Medical Imaging 2018: Digital Pathology
John E. Tomaszewski; Metin N. Gurcan, Editor(s)