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

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

PURPOSE: Modern 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.

CONCLUSION: We 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.

Original languageEnglish
Pages (from-to)15266028221105840
JournalJournal of Endovascular Therapy
Early online date9 Jul 2022
DOIs
Publication statusE-pub ahead of print - 9 Jul 2022

Keywords

  • AAA
  • Abdominal Aortic Aneurysm
  • Artificial Intelligence
  • Automatic
  • Deep learning
  • Digital Subtraction Angiography
  • EVAR
  • Endovascular Aneurysm Repair
  • Intraoperative
  • Segmentation

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