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

Part of the Lecture Notes in Computer Science book series (LNIP,volume 10553)


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.


  • Deep learning
  • Deformable image registration
  • Convolution neural network
  • Spatial transformer
  • Cardiac cine MRI

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  • DOI: 10.1007/978-3-319-67558-9_24
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This study was funded by the Netherlands Organization for Scientific Research (NWO): project 12726.

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Correspondence to Bob D. de Vos .

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de Vos, B.D., Berendsen, F.F., Viergever, M.A., Staring, M., Išgum, I. (2017). End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network. In: , et al. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . DLMIA ML-CDS 2017 2017. Lecture Notes in Computer Science(), vol 10553. Springer, Cham.

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