Generating Cerebral Vessel Trees of Acute Ischemic Stroke Patients using Conditional Set-Diffusion

Thijs P. Kuipers, Praneeta R. Konduri, Henk Marquering, Erik J Bekkers
Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning, PMLR 250:782-792, 2024.

Abstract

The advancements in computational modeling and simulations have facilitated the emergence of in-silico clinical trials (ISCTs). ISCTs are valuable in developing and evaluating novel treatments targeting acute ischemic stroke (AIS), a prominent contributor to both mortality and disability rates. However, obtaining large populations of accurate anatomical structures that are required as input to ISCTs is labor-intensive and time-consuming. In this work, we propose and evaluate diffusion-based generative modeling and set transformers to generate a population of synthetic intracranial vessel tree centerlines with associated radii and vessel types. We condition our model on the presence of an occlusion in the middle cerebral artery, a frequently occurring occlusion location in AIS patients. Our analysis of generated synthetic populations shows that our model accurately produces diverse and realistic cerebral vessel trees that represent the geometric characteristics of the real population.

Cite this Paper

BibTeX
@InProceedings{pmlr-v250-kuipers24a, title = {Generating Cerebral Vessel Trees of Acute Ischemic Stroke Patients using Conditional Set-Diffusion}, author = {Kuipers, Thijs P. and Konduri, Praneeta R. and Marquering, Henk and Bekkers, Erik J}, booktitle = {Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning}, pages = {782--792}, year = {2024}, editor = {Burgos, Ninon and Petitjean, Caroline and Vakalopoulou, Maria and Christodoulidis, Stergios and Coupe, Pierrick and Delingette, Hervé and Lartizien, Carole and Mateus, Diana}, volume = {250}, series = {Proceedings of Machine Learning Research}, month = {03--05 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v250/main/assets/kuipers24a/kuipers24a.pdf}, url = {https://proceedings.mlr.press/v250/kuipers24a.html}, abstract = {The advancements in computational modeling and simulations have facilitated the emergence of in-silico clinical trials (ISCTs). ISCTs are valuable in developing and evaluating novel treatments targeting acute ischemic stroke (AIS), a prominent contributor to both mortality and disability rates. However, obtaining large populations of accurate anatomical structures that are required as input to ISCTs is labor-intensive and time-consuming. In this work, we propose and evaluate diffusion-based generative modeling and set transformers to generate a population of synthetic intracranial vessel tree centerlines with associated radii and vessel types. We condition our model on the presence of an occlusion in the middle cerebral artery, a frequently occurring occlusion location in AIS patients. Our analysis of generated synthetic populations shows that our model accurately produces diverse and realistic cerebral vessel trees that represent the geometric characteristics of the real population.} }
Endnote
%0 Conference Paper %T Generating Cerebral Vessel Trees of Acute Ischemic Stroke Patients using Conditional Set-Diffusion %A Thijs P. Kuipers %A Praneeta R. Konduri %A Henk Marquering %A Erik J Bekkers %B Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2024 %E Ninon Burgos %E Caroline Petitjean %E Maria Vakalopoulou %E Stergios Christodoulidis %E Pierrick Coupe %E Hervé Delingette %E Carole Lartizien %E Diana Mateus %F pmlr-v250-kuipers24a %I PMLR %P 782--792 %U https://proceedings.mlr.press/v250/kuipers24a.html %V 250 %X The advancements in computational modeling and simulations have facilitated the emergence of in-silico clinical trials (ISCTs). ISCTs are valuable in developing and evaluating novel treatments targeting acute ischemic stroke (AIS), a prominent contributor to both mortality and disability rates. However, obtaining large populations of accurate anatomical structures that are required as input to ISCTs is labor-intensive and time-consuming. In this work, we propose and evaluate diffusion-based generative modeling and set transformers to generate a population of synthetic intracranial vessel tree centerlines with associated radii and vessel types. We condition our model on the presence of an occlusion in the middle cerebral artery, a frequently occurring occlusion location in AIS patients. Our analysis of generated synthetic populations shows that our model accurately produces diverse and realistic cerebral vessel trees that represent the geometric characteristics of the real population.
APA
Kuipers, T.P., Konduri, P.R., Marquering, H. & Bekkers, E.J.. (2024). Generating Cerebral Vessel Trees of Acute Ischemic Stroke Patients using Conditional Set-Diffusion. Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 250:782-792 Available from https://proceedings.mlr.press/v250/kuipers24a.html.

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