Zusammenfassung
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 crosssections, 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 interpatient 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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Literatur
Li B, Christensen GE, Hoffman EA, McLennan G, Reinhardt JM. Establishing a normative atlas of the human lung: intersubject warping and registration of volumetric CT images. Acad Radiol. 2003;10(3):255–65.
Xu K, Gao R, Khan MS, Bao S, Tang Y, Deppen SA et al. Development and characterization of a chest CT atlas. Proc SPIE Int Soc Opt Eng. Vol. 2021. NIH Public Access. 2021.
Feragen A, Owen M, Petersen J, Wille MM, Thomsen LH, Dirksen A et al. Tree-space statistics and approximations for large-scale analysis of anatomical trees. Inf Process Med Imaging. Springer. 2013:74–85.
Hansen L, Heinrich MP. Revisiting iterative highly efficient optimisation schemes in medical image registration. Med Image Comput Comput Assist Interv. Springer. 2021:203–12.
Mok TC, Chung AC. Large deformation diffeomorphic image registration with Laplacian pyramid networks. Med Image Comput Comput Assist Interv. Springer. 2020:211–21.
Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.Nat Methods. 2021;18(2):203–11.
Armato III SG, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP et al. The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys. 2011;38(2):915–31.
Lo P, Van Ginneken B, Reinhardt JM, Yavarna T, De Jong PA, Irving B et al. Extraction of airways from CT (EXACT’09). IEEE Trans Med Imaging. 2012;31(11):2093–107.
Tan Z, Feng J, Zhou J. SGNet: structure-aware graph-based network for airway semantic segmentation. Med Image Comput Comput Assist Interv. Springer. 2021:153–63.
Hansen L, Heinrich MP. Deep learning based geometric registration for medical images: how accurate can we get without visual features? Inf Process Med Imaging. Springer. 2021:18–30.
Heinrich MP, Jenkinson M, Papiez BW, Brady M, Schnabel JA. Towards realtime multimodal fusion for image-guided interventions using self-similarities. Med Image Comput Comput Assist Interv. Springer. 2013:187–94.
Heinrich MP, Papiez BW, Schnabel JA, Handels H. Non-parametric discrete registration with convex optimisation. Biomed Image Registration Proc. Springer. 2014:51–61.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
About this paper
Cite this paper
Falta, F., Hansen, L., Himstedt, M., Heinrich, M.P. (2022). Learning an Airway Atlas from Lung CT Using Semantic Inter-patient Deformable Registration. In: Maier-Hein, K., Deserno, T.M., Handels, H., Maier, A., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2022. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-36932-3_15
Download citation
DOI: https://doi.org/10.1007/978-3-658-36932-3_15
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-36931-6
Online ISBN: 978-3-658-36932-3
eBook Packages: Computer Science and Engineering (German Language)