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Abstract

Similarity-based deformable registration of medical images that contain tumor regions often undesirably leads to disproportionate volume changes to tumor regions. An image-registration framework for preserving tumor volumes while promoting similarity in non-tumor part is provided. The framework involves a two-stage process. In the first stage, similarity-based registration is used to identify potential tumor regions by their volume change, generating a soft tumor mask accordingly. In the second stage, a volume-preserving registration network is used. The network uses a novel adaptive volume-preserving loss that penalizes the change in size adaptively based on the masks calculated from the previous stage. The framework balances image similarity and volume preservation in different regions, i.e. non-tumor and tumor regions, by using soft tumor masks to adjust the imposition of volume-preserving loss on each one. It ensures that the tumor volumes are preserved during the registration process.

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