Zusammenfassung
The segmentation of vascular systems is a challenging task since their sizes and structures vary greatly so that the spatial context becomes highly important. For further clinical analysis of the vascular system it is important to create a connected vascular tree starting from the main trunk, following the tree structure up to small branches. To address these issues, we propose a new iterative segmentation model that recursively evolves a segmentation of a vasculature by following its tree structure. Our iterative CNN alternates between three steps: First, a 3D segmentation of a sub-region is performed. Second, branches that are not part of the currently analyzed branch are removed and third, subsequent sub-regions are detected. These steps are repeated until the entire vascular system is segmented. We trained, validated and tested our model on 82 CT images. We showed that, in comparison to state of the art methods, our new model generates a more accurate segmentation, resulting in an improvement of the Dice score of 7 % and a reduction of the Hausdorff distance of approximately 20 %.
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
Selle D, Preim B, Schenk A, Peitgen HO. Analysis of vasculature for liver surgical planning. IEEE Trans Med Imaging. 2002;21(11):1344–57.
Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention. Springer. 2015:234–41.
Livne M, Rieger J, Aydin OU, Taha AA, Akay EM, Kossen T et al. A U-Net deep learning framework for high performance vessel segmentation in patients with cerebrovascular disease. Front Neurosci. 2019;13.
Kitrungrotsakul T, Han XH, Iwamoto Y, Lin L, Foruzan AH, XiongWet al.VesselNet: a deep convolutional neural network with multi pathways for robust hepatic vessel segmentation. Comput Med Imaging Graph. 2019;75:74–83.
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): 203–211.
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
Schumacher, M., Bade, R., Genz, A., Heinrich, M. (2022). Iterative 3D CNN Based Segmentation of Vascular Trees in Liver CT. 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_2
Download citation
DOI: https://doi.org/10.1007/978-3-658-36932-3_2
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)