Unsupervised End-to-end Learning for Deformable Medical Image Registration

By Siyuan Shan, Xiaoqing Guo, Wen Yan, Eric I-Chao Chang, Yubo Fan, Yan Xu. 2017

55 citations recorded by the source corpus.

We propose a registration algorithm for 2D CT/MRI medical images with a new unsupervised end-to-end strategy using convolutional neural networks. The contributions of our algorithm are threefold: (1) We transplant traditional image registration algorithms to an end-to-end convolutional neural network framework, while maintaining the unsupervised nature of image registration problems. The image-to-image integrated framework can simultaneously learn both image features and transformation matrix for registration. (2) Training with additional data without any label can further improve the registration performance by approximately 10 %. (3) The registration speed is 100x faster than traditional methods. The proposed network is easy to implement and can be trained efficiently. Experiments demonstrate that our system achieves state-of-the-art results on 2D brain registration and achieves comparable results on 2D liver registration. It can be extended to register other organs beyond liver and brain such as kidney, lung, and heart.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “For example: deformable image registration [97,239]; model-to-image registration [240,241]; MRI-based attenuation correction for PET [242,243]; PET/MRI dose calculation [244]; unsupervised end-to-end learning for deformable registration of 2D CT/MR images [245]; an unsupervised learning model for de”
TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning (2020, background): “Convolutional neural networks (CNNs) have become the state of the art for many medical imaging tasks including segmentation [1], classification [2], reconstruction [3] and registration [4].”