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Objective: Surveillance imaging of chronic aortic diseases, such asdissections, relies on obtaining and comparing cross-sectional diametermeasurements at predefined aortic landmarks, over time. Due to a lack of robusttools, the orientation of the cross-sectional planes is defined manually byhighly trained operators. We show how manual annotations routinely collected ina clinic can be efficiently used to ease this task, despite the presence of anon-negligible interoperator variability in the measurements.  Impact: Ill-posed but repetitive imaging tasks can be eased or automated byleveraging imperfect, retrospective clinical annotations.  Methodology: In this work, we combine convolutional neural networks anduncertainty quantification methods to predict the orientation of suchcross-sectional planes. We use clinical data randomly processed by 11 operatorsfor training, and test on a smaller set processed by 3 independent operators toassess interoperator variability.  Results: Our analysis shows that manual selection of cross-sectional planesis 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 andLOA by $5.4^\circ$ ($49.0$%) and $16.0^\circ$ ($74.6$%) against manualprocessing.  Conclusion: This suggests that pre-existing annotations can be an inexpensiveresource in clinics to ease ill-posed and repetitive tasks like cross-sectionextraction for surveillance of aortic dissections.

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