"End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network"

Bob D. de Vos, Floris Berendsen, Max A. Viergever, Marius Staring and Ivana Išgum


In this work we propose a deep learning network for deformable image registration (DIRNet). The DIRNet consists of a convolutional neural network (ConvNet) regressor, a spatial transformer, and a resampler. The ConvNet analyzes a pair of fixed and moving images and outputs parameters for the spatial transformer, which generates the displacement vector field that enables the resampler to warp the moving image to the fixed image. The DIRNet is trained end-to-end by unsupervised optimization of a similarity metric between input image pairs. A trained DIRNet can be applied to perform registration on unseen image pairs in one pass, thus non-iteratively. Evaluation was performed with registration of images of handwritten digits (MNIST) and cardiac cine MR scans (Sunnybrook Cardiac Data). The results demonstrate that registration with DIRNet is as accurate as a conventional deformable image registration method with short execution times.



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BibTeX entry

author = "{Bob D. de Vos and Floris Berendsen and Max A. Viergever and Marius Staring and Ivana Išgum}",
title = "{End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network}",
booktitle = "{Deep Learning in Medical Image Analysis Workshop at MICCAI}",
editor = "{M. Jorge Cardoso and Tal Arbel and Gustavo Carneiro and Tanveer Syeda-Mahmood and João Manuel R.S. Tavares and Mehdi Moradi and Andrew Bradley and Hayit Greenspan and João Paulo Papa and Anant Madabhushi and Jacinto C. Nascimento and Jaime S. Cardoso and Vasileios Belagiannis and Zhi Lu}",
address = "{Quebec,Canada}",
series = "{Lecture Notes in Computer Science}",
volume = "{10553}",
pages = "{204 - 212}",
month = "{September}",
year = "{2017}",

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