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Original research
Automatic radiomic feature extraction using deep learning for angiographic parametric imaging of intracranial aneurysms
  1. Alexander R Podgorsak1,2,3,
  2. Ryan A Rava2,3,
  3. Mohammad Mahdi Shiraz Bhurwani2,3,
  4. Anusha R Chandra2,3,
  5. Jason M Davies3,4,5,
  6. Adnan H Siddiqui3,4,
  7. Ciprian N Ionita1,2,3
  1. 1 Department of Medical Physics, University at Buffalo, State University of New York, Buffalo, New York, USA
  2. 2 Department of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, New York, USA
  3. 3 Canon Stroke and Vascular Research Center, Buffalo, New York, USA
  4. 4 Department of Neurosurgery, University at Buffalo, State University of New York, Buffalo, New York, USA
  5. 5 Department of Biomedical Informatics, University at Buffalo, State University of New York, Buffalo, New York, USA
  1. Correspondence to Dr Ciprian N Ionita, Department of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, NY 14260, USA; cnionita{at}buffalo.edu

Abstract

Background Angiographic parametric imaging (API) is an imaging method that uses digital subtraction angiography (DSA) to characterize contrast media dynamics throughout the vasculature. This requires manual placement of a region of interest over a lesion (eg, an aneurysm sac) by an operator.

Objective The purpose of our work was to determine if a convolutional neural network (CNN) was able to identify and segment the intracranial aneurysm (IA) sac in a DSA and extract API radiomic features with minimal errors compared with human user results.

Methods Three hundred and fifty angiographic images of IAs were retrospectively collected. The IAs and surrounding vasculature were manually contoured and the masks put to a CNN tasked with semantic segmentation. The CNN segmentations were assessed for accuracy using the Dice similarity coefficient (DSC) and Jaccard index (JI). Area under the receiver operating characteristic curve (AUROC) was computed. API features based on the CNN segmentation were compared with the human user results.

Results The mean JI was 0.823 (95% CI 0.783 to 0.863) for the IA and 0.737 (95% CI 0.682 to 0.792) for the vasculature. The mean DSC was 0.903 (95% CI 0.867 to 0.937) for the IA and 0.849 (95% CI 0.811 to 0.887) for the vasculature. The mean AUROC was 0.791 (95% CI 0.740 to 0.817) for the IA and 0.715 (95% CI 0.678 to 0.733) for the vasculature. All five API features measured inside the predicted masks were within 18% of those measured inside manually contoured masks.

Conclusions CNN segmentation of IAs and surrounding vasculature from DSA images is non-inferior to manual contours of aneurysms and can be used in parametric imaging procedures.

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https://doi.org/10.1136/neurintsurg-2019-015214

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    Footnotes

    • Contributors ARP, MSB, and CNI conceived and designed the research. AHS, JMD, ARC, RAR, and ARP collected and reviewed the data. ARP analyzed the data and performed statistical analysis. CNI handled funding and supervision. ARP drafted the manuscript. All authors made critical revisions of the manuscript and reviewed the final version.

    • Funding This project was partially supported by Canon Medical Systems and the Cummings Foundation.

    • Competing interests CNI: Equipment grant from Canon Medical Systems, support from the Cummings Foundation. JMD: Research grant: National Center for Advancing Translational Sciences of the National Institutes of Health under award number KL2TR001413 to the University at Buffalo; Speakers’ bureau: Penumbra; Honoraria: Neurotrauma Science; shareholder/ownership interests: RIST Neurovascular. AHS: Research grant: NIH/NINDS 1R01NS091075 as a co-investigator for ’Virtual Intervention of Intracranial Aneurysms'; financial interest/investor/stock options/ownership: Amnis Therapeutics, Apama Medical, Blink TBI, Buffalo Technology Partners, Cardinal Consultants, Cerebrotech Medical Systems, Cognition Medical, Endostream Medical, Imperative Care, International Medical Distribution Partners, Neurovascular Diagnostics, Q’Apel Medical, Rebound Therapeutics Corp, Rist Neurovascular, Serenity Medical, Silk Road Medical, StimMed, Synchron, Three Rivers Medical, Viseon Spine; Consultant/advisory board: Amnis Therapeutics, Boston Scientific, Canon Medical Systems USA, Cerebrotech Medical Systems, Cerenovus, Corindus, Endostream Medical, Guidepoint Global Consulting, Imperative Care, Integra LifeSciences Corp, Medtronic, MicroVention, Northwest University–DSMB Chair for HEAT Trial, Penumbra, Q’Apel Medical, Rapid Medical, Rebound Therapeutics Corp, Serenity Medical, Silk Road Medical, StimMed, Stryker, Three Rivers Medical, VasSol, W L Gore & Associates; Principal investigator/steering committee of the following trials: Cerenovus LARGE and ARISE II; Medtronic SWIFT PRIME and SWIFT DIRECT; MicroVention FRED & CONFIDENCE; MUSC POSITIVE; and Penumbra 3D Separator, COMPASS, and INVEST.

    • Ethics approval Institutional review board approval was obtained and informed consent was waived for this Health Insurance Portability and Accountability Act-compliant retrospective study. IRB Study Number: MOD00005495.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Patient consent for publication Not required.