A Multiple Decoder CNN for Inverse Consistent 3D Image Registration

• URL: http://arxiv.org/abs/2002.06468v1
• Date: Sat, 15 Feb 2020 23:23:09 GMT
• Title: A Multiple Decoder CNN for Inverse Consistent 3D Image Registration
• Authors: Abdullah Nazib, Clinton Fookes, Olivier Salvado, Dimitri Perrin
• Abstract summary: Deep learning technologies have drastically decreased the registration time and increased registration accuracy. We propose a registration framework with inverse consistency. We perform training and testing of the method on the publicly available LPBA40 MRI dataset and demonstrate strong performance.
• Score: 18.017296651822857
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The recent application of deep learning technologies in medical image registration has exponentially decreased the registration time and gradually increased registration accuracy when compared to their traditional counterparts. Most of the learning-based registration approaches considers this task as a one directional problem. As a result, only correspondence from the moving image to the target image is considered. However, in some medical procedures bidirectional registration is required to be performed. Unlike other learning-based registration, we propose a registration framework with inverse consistency. The proposed method simultaneously learns forward transformation and backward transformation in an unsupervised manner. We perform training and testing of the method on the publicly available LPBA40 MRI dataset and demonstrate strong performance than baseline registration methods.

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