Abstract
Long-term mortality rates after endovascular aneurysm repair (EVAR) remain elevated due to post-EVAR rupture caused by loss of seal in stent graft sealing zones. Structured CT review using centerline measurements improves detection, but current workflows require manual centerline editing and expert operators. We propose CEVAR, a transformer framework for automated, protocol-driven sealing zone assessment that combines 3D centerline tracking with embedding-based geometric prediction. Two state-of-the-art image-to-graph models are evaluated for aorto-iliac centerline extraction from follow-up CT and for measurement of stent position, vessel diameters, and seal lengths according to EVAR4C protocol. Across the full test set and a challenging non-contrast subset, the proposed fully automatic method outperforms the commercial semi-automatic workflow. Code and pretrained models are available at https://github.com/RomStriker/CEVAR.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/MLMI_047.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: https://openreview.net/forum?id=VNK55ux98W
BibTex
@InProceedings{NaeRom_CEVAR_MICCAISAT2026,
author = { Naeem, Roman AND Niiniskorpi, Timo AND Sandström, Charlotte AND Desai, Naman AND Häggström, Ida AND Kahl, Fredrik AND Roos, Håkan AND Alvén, Jennifer},
title = { { CEVAR: Centerline Embedding Extraction for Endovascular Aneurysm Repair } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17272},
month = {pending},
page = {pending}
}