2021/11/22 by Antonio Pepe, Pepe, Antonio, Jan Egger +17 · 1 citation
Engineering · #Advanced X-ray and CT Imaging #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Reservoir Engineering and Simulation Methods #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2111.11269
openalex publication_date 2021/11/22 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
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^∘ and 21.4^∘ per angle. Our method showed to decrease static error by 3.57^∘ (40.2%) and 4.11^∘ (32.8%) against state of the art and LOA by 5.4^∘ (49.0%) and 16.0^∘ (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.