Abstract
An automated pipeline is developed for the serial assessment of renal calculi using computed tomography (CT) scans obtained at multiple time points. This retrospective study included 722 scans from 330 patients chosen from 8544 asymptomatic patients who underwent two or more CTC (CT colonography) or non-enhanced abdominal CT scans between 2004 and 2016 at a single medical center. A pre-trained deep learning (DL) model was used to segment the kidneys and the calculi on the CT scans at each time point. Based on the output of the DL, 330 patients were identified as having a stone candidate on at least one time point. Then, for every patient in this group, the kidneys from different time points were registered to each other, and the calculi present at multiple time points were matched to each other using proximity on the registered scans. The automated pipeline was validated by having a blinded radiologist assess the changes manually. New graph-based metrics are introduced in order to evaluate the performance of our pipeline. Our method shows high fidelity in tracking changes in renal calculi over multiple time points.
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Acknowledgements
This research was supported by the Intramural Research Program of the National Institutes of Health, Clinical Center, and we utilized the computational resources of the National Institutes of Health high-performance computing Biowulf cluster.
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Mukherjee, P., Lee, S., Pickhardt, P.J., Summers, R.M. (2022). Automated Assessment of Renal Calculi in Serial Computed Tomography Scans. In: Wu, S., Shabestari, B., Xing, L. (eds) Applications of Medical Artificial Intelligence. AMAI 2022. Lecture Notes in Computer Science, vol 13540. Springer, Cham. https://doi.org/10.1007/978-3-031-17721-7_5
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DOI: https://doi.org/10.1007/978-3-031-17721-7_5
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