Deep geometric learning for intracranial aneurysm detection: towards expert rater performance.

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  • Additional Information
    • Source:
      Publisher: BMJ Publishing Group Country of Publication: England NLM ID: 101517079 Publication Model: Electronic Cited Medium: Internet ISSN: 1759-8486 (Electronic) Linking ISSN: 17598478 NLM ISO Abbreviation: J Neurointerv Surg Subsets: MEDLINE
    • Publication Information:
      Original Publication: London : BMJ Publishing Group, c2009-
    • Subject Terms:
      Intracranial Aneurysm*/diagnostic imaging ; Deep Learning* ; Magnetic Resonance Angiography*/methods ; Computed Tomography Angiography*/methods; Humans ; Cerebral Angiography/methods ; Male ; Sensitivity and Specificity ; Female ; Middle Aged
    • Abstract:
      Background: Early detection of intracranial aneurysms (IAs) is crucial for patient outcomes. Typically identified on angiographic scans such as CT angiography (CTA) or MR angiography (MRA), the sensitivity of experts in studies on small IAs (diameter <3 mm) was moderate (64-74.1% for CTAs and 70-92.8% for MRAs), and these figures could be lower in a routine clinical setting. Recent research shows that the expert level of sensitivity might be achieved using deep learning approaches.
      Methods: A large multisite dataset including 1054 MRA and 2174 CTA scans with expert IA annotations was collected. A novel modality-agnostic two-step IA detection approach was proposed. The first step used nnU-Net for segmenting vascular structures, with model training performed separately for each modality. In the second step, segmentations were converted to vascular surface that was parcellated by sampling point clouds and, using a PointNet++ model, each point was labeled as an aneurysm or vessel class.
      Results: Quantitative validation of the test data from different sites than the training data showed that the proposed approach achieved pooled sensitivity of 85% and 90% on 157 MRA scans and 1338 CTA scans, respectively, while the sensitivity for small IAs was 72% and 83%, respectively. The corresponding number of false findings per image was low at 1.54 and 1.57, and 0.4 and 0.83 on healthy subject data.
      Conclusions: The proposed approach achieved a state-of-the-art balance between the sensitivity and the number of false findings, matched the expert-level sensitivity to small (and other) IAs on external data, and therefore seems fit for computer-assisted detection of IAs in a clinical setting.
      Competing Interests: Competing interests: None declared.
      (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)
    • Contributed Indexing:
      Keywords: aneurysm; brain; vascular malformation
    • Publication Date:
      Date Created: 20231013 Date Completed: 20241014 Latest Revision: 20241014
    • Publication Date:
      20241015
    • Accession Number:
      10.1136/jnis-2023-020905
    • Accession Number:
      37833055