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Proceedings Paper

Unsupervised learning-based deformable registration of temporal chest radiographs to detect interval change
Author(s): Qiming Fang; Jichao Yan; Xiaomeng Gu; Jun Zhao; Qiang Li
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Paper Abstract

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.

Paper Details

Date Published: 10 March 2020
PDF: 7 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113132X (10 March 2020); doi: 10.1117/12.2549211
Show Author Affiliations
Qiming Fang, Shanghai Jiao Tong Univ. (China)
Shanghai United Imaging Healthcare Co., Ltd. (China)
Jichao Yan, United Imaging Healthcare Co., Ltd. (China)
Xiaomeng Gu, Shanghai Jiao Tong Univ. (China)
Shanghai United Imaging Healthcare Co., Ltd. (China)
Jun Zhao, Shanghai Jiao Tong Univ. (China)
Qiang Li, Huazhong Univ. of Science and Technology (China)

Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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