Influence of learned landmark correspondences on lung CT registration

Abstract

AbstractBackgroundDisease 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.PurposeLandmark correspondences have been used to make deformable image registration robust to large displacements.MethodsTo 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 deformations to train the landmark correspondence model, we use data‐driven synthetic transformations. We study the influence of these learned landmark correspondences on lung CT registration by integrating them into intensity‐based registration as a distance‐based penalty.ResultsOur results on the public thoracic CT dataset COPDgene show that using learned landmark correspondences as a soft constraint can reduce median registration error from approximately 5.46 to 4.08 mm compared to standard intensity‐based registration, in the absence of lung masks.ConclusionsWe show that using landmark correspondences results in minor improvements in local alignment, while significantly improving global alignment.