Learning an airway atlas from lung CT using semantic inter-patient deformable registration

Abstract

Pulmonary image analysis for diagnostic and interventions often relies on a canonical geometric representation of lung anatomy across a patient cohort. Bronchoscopy can benefit from simulating an appearance atlas of airway cross- sections, intra-patient deformable image registration could be initialised using a shared lung atlas. The diagnosis of pneumonia, COPD and other respiratory diseases can benefit from a well defined anatomical reference space. Previous work to create lung atlases either relied on tedious and often ambiguous manual landmark correspondences and/or image features to perform deformable inter- patient registration. In this work, we overcome these limitations by guiding the registration with semantic airway features that can be obtained straightforwardly with an nnUNet and dilated training labels. We demonstrate that accurate and robust registration results across patients can be achieved in few seconds leading to high agreement of small airways of later generations. Incorporating the semantic cost function improves segmentation overlap and landmark accuracy.

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Reproductions