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Disease or injury may cause a change in the biomechanical properties of the lungs, which can alter lung function. Image registration can be used to measure lung ventilation and quantify volume change, which can be a useful diagnostic aid. However, lung registration is a challenging problem because of the variation in deformation along the lungs, sliding motion of the lungs along the ribs, and change in density.

Landmark correspondences have been used to make deformable image registration robust to large displacements.

To tackle the challenging task of intra‐patient lung computed tomography (CT) registration, we extend the landmark correspondence prediction model deep convolutional neural network‐Match by introducing a soft mask loss term to encourage landmark correspondences in specific regions and avoid the use of a mask during inference. To produce realistic …