Figure 1: Segmentation results on both the IXI and TubeTK datasets for angiographic and structural MR sequences.
This repository comprises our results and code for the methods for segmentation and registration of cerebral arteries described in the following publications:
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[MIDL2024] Brain Artery Segmentation for Structural MRI
Accepted at Medical Imaging with Deep Learning (MIDL) Conference 2024
Full Reference:Sabrowsky-Hirsch, B., AlShenoudy, A., Thumfart, S., Giretzlehner, M., & Scharinger, J. (2024). Brain Artery Segmentation for Structural MRI. In Medical Imaging with Deep Learning. OpenReview. -
[MIUA2024a] Towards Segmenting Cerebral Arteries from Structural MRI
Accepted at Medical Image Understanding and Analysis (MIUA) Conference 2024
Full Reference:Alshenoudy, A., Sabrowsky-Hirsch, B., Scharinger, J., Thumfart, S., Giretzlehner, M.. (2024). Towards Segmenting Cerebral Arteries from Structural MRI. 28th UK Conference on Medical Image Understanding and Analysis (MIUA 2024). Springer Nature. -
[MIUA2024b] Robust Multi-Modal Registration of Cerebral Vasculature
Accepted at Medical Image Understanding and Analysis (MIUA) Conference 2024
Full Reference:Sabrowsky-Hirsch, B., Alshenoudy, A., Scharinger, J., Gmeiner, M., Thumfart, S., Giretzlehner, M.. (2024). Robust Multi-Modal Registration of Cerebral Vasculature. 28th UK Conference on Medical Image Understanding and Analysis (MIUA 2024). Springer Nature.
Please find more details on the respective subfolder linked for each publication.
For our previous work on the anatomical annotation of cerebral arteries, refer to our previous repository.
Figure 2: Overview of MIDL2024 method and respective submodules employed from our other publications.
If you have any inquiries, please open a GitHub issue.
This project is financed by research subsidies granted by the government of Upper Austria. RISC Software GmbH is Member of UAR (Upper Austrian Research) Innovation Network.