Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
565-05-001 ISMRM Abstract

Are MRI-based deep learning algorithms for kidney volume assessment in polycystic kidney disease ready for deployment?

Accepted
Enrique Almar-Munoz 1, Emil Colliander2, Sebastian Tupper3, Agnes Mayr1, Rebeca Miron Mombiela2,4
1Medical University of Innsbruck, Innsbruck, Austria
2Radiology, Herlev and Gentofte Hospital, Herlev, Denmark
3Herlev and Gentofte Hospital, Herlev, Denmark
4Institute for Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
Presenting Author: Enrique Almar-Munoz

Synopsis

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References

1. Davies, F., Coles, G. A., Harper, P. S., Williams, A. J., Evans, C., & Cochlin, D. (1991). Polycystic kidney disease re-evaluated: a population-based study. QJM: An International Journal of Medicine, 79(3), 477-485. https://doi.org/10.1093/oxfordjournals.qjmed.a068568 [doi]
2. Irazabal, M. V., Rangel, L. J., Bergstralh, E. J., Osborn, S. L., Harmon, A. J., Sundsbak, J. L., ... & Crisp Investigators. (2015). Imaging classification of autosomal dominant polycystic kidney disease: a simple model for selecting patients for clinical trials. Journal of the American Society of Nephrology, 26(1), 160-172. doi: 10.1681/ASN.2013101138 [doi]
3. Alan, S. L., Shen, C., Landsittel, D. P., Harris, P. C., Torres, V. E., Mrug, M., ... & Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP. (2018). Baseline total kidney volume and the rate of kidney growth are associated with chronic kidney disease progression in autosomal dominant polycystic kidney disease. Kidney International, 93(3), 691-699. https://doi.org/10.1016/j.kint.2017.09.027 [doi]
4. Kistler, A. D., Poster, D., Krauer, F., Weishaupt, D., Raina, S., Senn, O., ... & Serra, A. L. (2009). Increases in kidney volume in autosomal dominant polycystic kidney disease can be detected within 6 months. Kidney International, 75(2), 235-241. https://doi.org/10.1038/ki.2008.558 [doi]
5. He, X., Hu, Z., Dev, H., Romano, D. J., Sharbatdaran, A., Raza, S. I., ... & Prince, M. R. (2024). Test retest reproducibility of organ volume measurements in ADPKD using 3D multimodality deep learning. Academic radiology, 31(3), 889-899. https://doi.org/10.1016/j.acra.2023.09.009 [doi]
6. Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., ... & Prince, M. R. (2022). Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence, 4(2), e210205. https://doi.org/10.1148/ryai.210205 [doi]
7. Sharbatdaran, A., Romano, D., Teichman, K., Dev, H., Raza, S. I., Goel, A., ... & Prince, M. R. (2022). Deep learning automation of kidney, liver, and spleen segmentation for organ volume measurements in autosomal dominant polycystic kidney disease. Tomography, 8(4), 1804-1819. https://doi.org/10.3390/tomography8040152 [doi]
8. van Gastel, M. D., Edwards, M. E., Torres, V. E., Erickson, B. J., Gansevoort, R. T., & Kline, T. L. (2019). Automatic measurement of kidney and liver volumes from MR images of patients affected by autosomal dominant polycystic kidney disease. Journal of the American Society of Nephrology, 30(8), 1514-1522. doi:10.1681/ASN.2018090902 [doi]
9. Kim, Y., Tao, C., Kim, H., Oh, G. Y., Ko, J., & Bae, K. T. (2022). A deep learning approach for automated segmentation of kidneys and exophytic cysts in individuals with autosomal dominant polycystic kidney disease. Journal of the American Society of Nephrology, 33(8), 1581-1589. doi:10.1681/ASN.2021111400 [doi]
10. Krishnan, C., Schmidt, E., Onuoha, E., Mrug, M., Cardenas, C. E., & Kim, H. (2024). nnUNet for automatic kidney and cyst segmentation in autosomal dominant polycystic kidney disease. Current Medical Imaging, 20(1), e15734056272767. https://doi.org/10.2174/0115734056272767231130110017 [doi]
11. Kline, T. L., Korfiatis, P., Edwards, M. E., Blais, J. D., Czerwiec, F. S., Harris, P. C., ... & Erickson, B. J. (2017). Performance of an artificial multi-observer deep neural network for fully automated segmentation of polycystic kidneys. Journal of digital imaging, 30(4), 442-448. https://doi.org/10.1007/s10278-017-9978-1 [doi]
12. Taylor, J., Thomas, R., Metherall, P., van Gastel, M., Cornec-Le Gall, E., Caroli, A., ... & Ong, A. C. (2024). An artificial intelligence generated automated algorithm to measure total kidney volume in ADPKD. Kidney international reports, 9(2), 249-256. https://doi.org/10.1016/j.ekir.2023.10.029 [doi]
13. Potretzke, T. A., Korfiatis, P., Blezek, D. J., Edwards, M. E., Klug, J. R., Cook, C. J., ... & Kline, T. L. (2023, May). Clinical implementation of an artificial intelligence algorithm for magnetic resonance–derived measurement of total kidney volume. In Mayo Clinic Proceedings (Vol. 98, No. 5, pp. 689-700). Elsevier. https://doi.org/10.1016/j.mayocp.2022.12.019 [doi]
14. Woznicki, P., Siedek, F., van Gastel, M. D., Dos Santos, D. P., Arjune, S., Karner, L. A., ... & Müller, R. U. (2022). Automated kidney and liver segmentation in MR images in patients with autosomal dominant polycystic kidney disease: a multicenter study. Kidney360, 3(12), 2048-2058. doi:10.34067/KID.0003192022 [doi]
15. Dev, H., Zhu, C., Sharbatdaran, A., Raza, S. I., Wang, S. J., Romano, D. J., ... & Prince, M. R. (2023). Effect of averaging measurements from multiple MRI pulse sequences on kidney volume reproducibility in autosomal dominant polycystic kidney disease. Journal of Magnetic Resonance Imaging, 58(4), 1153-1160. https://doi.org/10.1002/jmri.28593 [doi]
16. Conze, P. H., Andrade-Miranda, G., Le Meur, Y., Cornec-Le Gall, E., & Rousseau, F. (2024). Dual-task kidney MR segmentation with transformers in autosomal-dominant polycystic kidney disease. Computerized Medical Imaging and Graphics, 113, 102349. https://doi.org/10.1016/j.compmedimag.2024.102349 [doi]
17. Schmidt, E. K., Krishnan, C., Onuoha, E., Gregory, A. V., Kline, T. L., Mrug, M., ... & Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) Investigators. (2024). Deep learning-based automated kidney and cyst segmentation of autosomal dominant polycystic kidney disease using single vs. multi-institutional data. Clinical Imaging, 106, 110068. https://doi.org/10.1016/j.clinimag.2023.110068 [doi]
18. Bevilacqua, V., Brunetti, A., Cascarano, G. D., Guerriero, A., Pesce, F., Moschetta, M., & Gesualdo, L. (2019). A comparison between two semantic deep learning frameworks for the autosomal dominant polycystic kidney disease segmentation based on magnetic resonance images. BMC medical informatics and decision making, 19(Suppl 9), 244. https://doi.org/10.1186/s12911-019-0988-4 [doi]
19. Raj, A., Tollens, F., Caroli, A., Nörenberg, D., & Zöllner, F. G. (2023). Automated prognosis of renal function decline in ADPKD patients using deep learning. Zeitschrift für Medizinische Physik, 34(2), 330. doi:10.1016/j.zemedi.2023.08.001 [doi]

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