Abstract
We introduce a deep learning approach for automated kidney segmentation in autosomal dominant polycystic kidney disease (ADPKD). Our method combines Nyul normalization, resampling, and attention mechanisms to create a generalizable network. We evaluated our approach on two distinct datasets and found that our proposed model outperforms the baseline method with an average improvement of 9.45 % in Dice and 79.90 % in mean surface symmetric distance scores across both the datasets, demonstrating its potential for robust and accurate total kidney volume calculation from T1-w MRI images in ADPKD patients.
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© 2024 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Raj, A. et al. (2024). Generalizable Kidney Segmentation for Total Volume Estimation. In: Maier, A., Deserno, T.M., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2024. BVM 2024. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-44037-4_75
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DOI: https://doi.org/10.1007/978-3-658-44037-4_75
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