Spatio-temporal deep learning for automatic detection of intracranial vessel perforation in digital subtraction angiography during endovascular thrombectomy

Ruisheng Su*, Matthijs van der Sluijs, Sandra A.P. Cornelissen, Geert Lycklama, Jeannette Hofmeijer, Charles B.L.M. Majoie, Pieter Jan van Doormaal, Adriaan C.G.M. van Es, Danny Ruijters, Wiro J. Niessen, Aad van der Lugt, Theo van Walsum

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)
54 Downloads (Pure)

Abstract

Intracranial vessel perforation is a peri-procedural complication during endovascular therapy (EVT). Prompt recognition is important as its occurrence is strongly associated with unfavorable treatment outcomes. However, perforations can be hard to detect because they are rare, can be subtle, and the interventionalist is working under time pressure and focused on treatment of vessel occlusions. Automatic detection holds potential to improve rapid identification of intracranial vessel perforation. In this work, we present the first study on automated perforation detection and localization on X-ray digital subtraction angiography (DSA) image series. We adapt several state-of-the-art single-frame detectors and further propose temporal modules to learn the progressive dynamics of contrast extravasation. Application-tailored loss function and post-processing techniques are designed. We train and validate various automated methods using two national multi-center datasets (i.e., MR CLEAN Registry and MR CLEAN-NoIV Trial), and one international multi-trial dataset (i.e., the HERMES collaboration). With ten-fold cross-validation, the proposed methods achieve an area under the curve (AUC) of the receiver operating characteristic of 0.93 in terms of series level perforation classification. Perforation localization precision and recall reach 0.83 and 0.70 respectively. Furthermore, we demonstrate that the proposed automatic solutions perform at similar level as an expert radiologist.

Original languageEnglish
Article number102377
JournalMedical image analysis
Volume77
DOIs
Publication statusPublished - Apr 2022

Keywords

  • Decision making
  • Endovascular procedures
  • Object detection
  • Stroke
  • Treatment outcome
  • Vascular system injuries
  • X-Rays

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