Medical image registration is of vital importance for clinical diagnosis and treatment. The registration performance based on deep learning algorithms has been found to be more accurate when compared to that of conventional registration methods. In order to apply deep learning algorithms to the registration of serial chest radiographs, the current research conducted preprocessing on original chest radiographs by using a mask, then the mask was used for ResUnet registration network training, and finally the evaluation of the registration model was performed. Results showed that the model based on a deep learning mask and deep learning registration was able to approach good registration performance on chest radiographs, and the model can be used as a potential tool on temporal subtraction of sequential chest radiographs
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.