Evidence map›Paper›PMID 35815701›Full record

ArticleJournal of endovascular therapy : an official journal of the International Society of Endovascular Specialists2023

Deep Learning-Based Intraoperative Stent Graft Segmentation on Completion Digital Subtraction Angiography During Endovascular Aneurysm Repair.

Kaj O Kappe, Stefan P M Smorenburg, Arjan W J Hoksbergen, Jelmer M Wolterink, Kak Khee Yeung

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Article in Journal of endovascular therapy : an official journal of the International Society of Endovascular Specialists, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Kaj O KappeDepartment of Surgery, Amsterdam University Medical Centers location, Vrije Universiteit, Amsterdam, The Netherlands.
Stefan P M SmorenburgDepartment of Surgery, Amsterdam University Medical Centers location, Vrije Universiteit, Amsterdam, The Netherlands.ORCID 0000-0002-4754-3016
Arjan W J HoksbergenDepartment of Surgery, Amsterdam University Medical Centers location, Vrije Universiteit, Amsterdam, The Netherlands.
Jelmer M WolterinkDepartment of Applied Mathematics, Technical Medical Centre, University of Twente, Enschede, The Netherlands.
Kak Khee YeungDepartment of Surgery, Amsterdam University Medical Centers location, Vrije Universiteit, Amsterdam, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeModern endovascular hybrid operating rooms generate large amounts of medical images during a procedure, which are currently mostly assessed by eye. In this paper, we present fully automatic segmentation of the stent graft on the completion digital subtraction angiography during endovascular aneurysm repair, utilizing a deep learning network. TECHNIQUE: Completion digital subtraction angiographies (cDSAs) of 47 patients treated for an infrarenal aortic aneurysm using EVAR were collected retrospectively. A two-dimensional convolutional neural network (CNN) with a U-Net architecture was trained for segmentation of the stent graft from the completion angiographies. The cross-validation resulted in an average Dice similarity score of 0.957 ± 0.041 and median of 0.968 (IQR: 0.950 - 0.976). The mean and median of the average surface distance are 1.266 ± 1.506 mm and 0.870 mm (IQR: 0.490 - 1.430), respectively.

conclusionWe developed a fully automatic stent graft segmentation method based on the completion digital subtraction angiography during EVAR, utilizing a deep learning network. This can provide the platform for the development of intraoperative analytical applications in the endovascular hybrid operating room such as stent graft deployment accuracy, endoleak visualization, and image fusion correction.

Indexed as

Aortic Aneurysm, AbdominalBlood Vessel Prosthesis ImplantationDeep LearningEndovascular ProceduresAngiography, Digital SubtractionBlood Vessel ProsthesisEndoleakEndovascular Aneurysm RepairHumansRetrospective StudiesStentsTreatment OutcomeAAAAbdominal Aortic AneurysmArtificial IntelligenceAutomaticDeep learningDigital Subtraction AngiographyEndovascular Aneurysm RepairEVARIntraoperativeSegmentation

Identifiers

PMID35815701
PMCPMC10637092

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.