Unsupervised learning-based deformable registration of temporal chest radiographs to detect interval change

Temporal subtraction of sequential chest radiographs based on image registration technique has been developed for decades to assist radiologists in the detection of interval changes. Although the performance of current methods is good, the computation cost of these methods is generally high. The high computation cost is mainly due to the iterative optimization problem of non-learning-based deformable registration. In this work we present a fast unsupervised learning-based algorithm for deformable registration of chest radiographs. Based on a convolutional neural network, the proposed model learns to directly estimate spatial transformations from pairs of moving images and fixed images, and uses the transformations to warp the moving images. We apply a regularization term to constrain the model to learn local matching. The model is trained by optimizing a pair-wise similarity metric between the warped moving image and the fixed image, with no need for any supervised information such as ground truth deformation fields. The trained model can be used to predict the warped moving images in one shot, and is thus very fast. The subtraction images of the warped images and the fixed images are able to enhance various interval changes. The preliminary results showed that for approximately 98.55% cases, the learning-based method could obtain improved or comparable registration in comparison with the baseline method.