Automated cross-sectional view selection in CT angiography of aortic dissections with uncertainty awareness and retrospective clinical annotations

• URL: http://arxiv.org/abs/2111.11269v1
• Date: Mon, 22 Nov 2021 15:11:36 GMT
• Title: Automated cross-sectional view selection in CT angiography of aortic dissections with uncertainty awareness and retrospective clinical annotations
• Authors: Antonio Pepe and Jan Egger and Marina Codari and Martin J. Willemink and Christina Gsaxner and Jianning Li and Peter M. Roth and Gabriel Mistelbauer and Dieter Schmalstieg and Dominik Fleischmann
• Abstract summary: We show how manual annotations routinely collected in a clinic can be efficiently used to ease this task. Ill-posed but repetitive imaging tasks can be eased or automated by leveraging imperfect, retrospective clinical annotations.
• Score: 11.415942647070796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Objective: Surveillance imaging of chronic aortic diseases, such as dissections, relies on obtaining and comparing cross-sectional diameter measurements at predefined aortic landmarks, over time. Due to a lack of robust tools, the orientation of the cross-sectional planes is defined manually by highly trained operators. We show how manual annotations routinely collected in a clinic can be efficiently used to ease this task, despite the presence of a non-negligible interoperator variability in the measurements. Impact: Ill-posed but repetitive imaging tasks can be eased or automated by leveraging imperfect, retrospective clinical annotations. Methodology: In this work, we combine convolutional neural networks and uncertainty quantification methods to predict the orientation of such cross-sectional planes. We use clinical data randomly processed by 11 operators for training, and test on a smaller set processed by 3 independent operators to assess interoperator variability. Results: Our analysis shows that manual selection of cross-sectional planes is characterized by 95% limits of agreement (LOA) of $10.6^\circ$ and $21.4^\circ$ per angle. Our method showed to decrease static error by $3.57^\circ$ ($40.2$%) and $4.11^\circ$ ($32.8$%) against state of the art and LOA by $5.4^\circ$ ($49.0$%) and $16.0^\circ$ ($74.6$%) against manual processing. Conclusion: This suggests that pre-existing annotations can be an inexpensive resource in clinics to ease ill-posed and repetitive tasks like cross-section extraction for surveillance of aortic dissections.

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