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.
Interpretation of temporal CT images could help the radiologists to detect some subtle interval changes in the sequential
examinations. The purpose of this study was to develop a fully automated scheme for accurate registration of temporal
CT images for pulmonary nodule detection. Our method consisted of three major registration steps. Firstly, affine
transformation was applied in the segmented lung region to obtain global coarse registration images. Secondly, B-splines
based free-form deformation (FFD) was used to refine the coarse registration images. Thirdly, Demons algorithm was
performed to align the feature points extracted from the registered images in the second step and the reference images.
Our database consisted of 91 temporal CT cases obtained from Beijing 301 Hospital and Shanghai Changzheng Hospital.
The preliminary results showed that approximately 96.7% cases could obtain accurate registration based on subjective
observation. The subtraction images of the reference images and the rigid and non-rigid registered images could
effectively remove the normal structures (i.e. blood vessels) and retain the abnormalities (i.e. pulmonary nodules). This
would be useful for the screening of lung cancer in our future study.
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