Mind the Clot: Automated LVO Detection on CTA Using Deep Learning

Shubham Kumar, Arjun Agarwal, Satish Golla, Swetha Tanamala, Ujjwal Upadhyay, Subhankar Chattoraj, Preetham Putha, Sasank Chilamkurthy

ICCVW 2023 pp. 2495-2504

doi:10.1109/ICCVW60793.2023.00264 /iccvw/2023/kumar2023iccvw-mind/

Abstract

Globally, stroke is a leading cause of death and disability, and accurate and timely diagnosis of large vessel occlusion (LVO) is essential for positive outcomes. We present our robust deep learning-based method for detecting both internal carotid artery (ICA) and middle cerebral artery (MCA) large vessel occlusions (LVO) from computed tomography angiography (CTA) scans. Our proposed two LVO detection models achieved an overall combined accuracy of 90.9% with a sensitivity of 89.8% and specificity of 91.4%. Further, the proposed model is lower in computational complexities and produces the results in less than 40 seconds, validating its adequacy for deployment in the clinical workflow.

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Cite

Text

Kumar et al. "Mind the Clot: Automated LVO Detection on CTA Using Deep Learning." IEEE/CVF International Conference on Computer Vision Workshops, 2023. doi:10.1109/ICCVW60793.2023.00264

Markdown

[Kumar et al. "Mind the Clot: Automated LVO Detection on CTA Using Deep Learning." IEEE/CVF International Conference on Computer Vision Workshops, 2023.](https://mlanthology.org/iccvw/2023/kumar2023iccvw-mind/) doi:10.1109/ICCVW60793.2023.00264

BibTeX

@inproceedings{kumar2023iccvw-mind,
  title     = {{Mind the Clot: Automated LVO Detection on CTA Using Deep Learning}},
  author    = {Kumar, Shubham and Agarwal, Arjun and Golla, Satish and Tanamala, Swetha and Upadhyay, Ujjwal and Chattoraj, Subhankar and Putha, Preetham and Chilamkurthy, Sasank},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2023},
  pages     = {2495-2504},
  doi       = {10.1109/ICCVW60793.2023.00264},
  url       = {https://mlanthology.org/iccvw/2023/kumar2023iccvw-mind/}
}