Projects per year
Abstract
Accurate localization of the aortic annulus is key to several imaging tasks, like cross-sectional aortic valve plane estimation, aortic root segmentation, and annulus diameter measurements. In this project, we propose an end-to-end trainable deep reinforcement learning (DRL) algorithm aimed at identification of the aortic annulus in patients with aortic dissection. We trained 5 different agents on a dataset of 75 CT scans from 66 patients following a sequential model-upgrading strategy. We evaluated the effect of performing different image preprocessing steps, adding batch normalization and regularization layers, and changing terminal state definition. At each step of this sequential process, the model performance has been evaluated on a validation sample composed of 24 CTA scans from 24 independent patients. Localization accuracy was defined as the Euclidean distance between estimated and target aortic annulus locations. Best model results show a median localization error equal to 2.98 mm with an interquartile range equal to [2.25, 3.81] mm, and a failure rate (i.e., percentage of samples with localization error in validation data. We proved the feasibility of DRL application for aortic annulus localization in CTA images of patients with aortic dissection, which are characterized by a large variability in aortic morphology and image quality. Nevertheless, further improvements are needed to reach expert-human level performance.
Original language | English |
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Title of host publication | Thoracic Image Analysis - Second International Workshop, TIA 2020, Held in Conjunction with MICCAI 2020, Proceedings |
Subtitle of host publication | Second International Workshop, TIA 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings |
Editors | Jens Petersen, Raúl San José Estépar, Alexander Schmidt-Richberg, Sarah Gerard, Bianca Lassen-Schmidt, Colin Jacobs, Reinhard Beichel, Kensaku Mori |
Place of Publication | Cham |
Publisher | Springer |
Pages | 94-105 |
Number of pages | 12 |
ISBN (Electronic) | 978-3-030-62469-9 |
ISBN (Print) | 978-3-030-62468-2 |
DOIs | |
Publication status | Published - 1 Jan 2020 |
Event | 2nd International Workshop on Thoracic Image Analysis - Virtuell, Peru Duration: 8 Oct 2020 → 8 Oct 2020 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 12502 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 2nd International Workshop on Thoracic Image Analysis |
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Abbreviated title | TIA 2020 |
Country/Territory | Peru |
City | Virtuell |
Period | 8/10/20 → 8/10/20 |
Keywords
- Aortic annulus
- Deep reinforcement learning
- Landmark localization
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Projects
- 1 Finished
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Aortic Dissection
Egger, J., Pepe, A., Schmalstieg, D., Schussnig, R., von der Linden, W., Melito, G. M., Holzapfel, G., Ramalho Queiroz Pacheco, D., Jafarinia, A., Brenn, G., Ranftl, S., Müller, T. S., Gupta, I., Steinbach, O., Fries, T., Badeli, V., Hochrainer, T., Schanz, M., Rolf-Pissarczyk, M., Biro, O. & Ellermann, K.
1/01/18 → 31/12/20
Project: Research project
Activities
- 1 Talk at workshop, seminar or course
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Detection, Segmentation, Simulation and Visualization of Aortic Dissections: A Review
Antonio Pepe (Speaker)
21 Oct 2020Activity: Talk or presentation › Talk at workshop, seminar or course › Science to science