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16 March 2020 Blood flow anomaly detection via generative adversarial networks: a preliminary study
Asha Singanamalli, Jhimli Mitra, Kirk Wallace, Prem Venugopal, Scott Smith, Larry Mo, Lai Yee Leung, Jonathan Morrison, Todd Rasmussen, Luca Marinelli
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Proceedings Volume 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; 1131522 (2020) https://doi.org/10.1117/12.2549094
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States
Abstract
This work explores a Generative Adversarial Network (GAN) based approach for hemorrhage detection on color Doppler ultrasound images of blood vessels. Given the challenges of collecting hemorrhage data and the inherent pathology variability, we investigate an unsupervised anomaly detection network which learns a manifold of normal blood flow variability and subsequently identifies anomalous flow patterns that fall outside the learned manifold. As an initial, feasibility study, we collected ultrasound color Doppler images of brachial arteries from 11 healthy volunteers. The images were pre-processed to mask out velocities in surrounding tissues and were subsequently cropped, resized, augmented and normalized. The network was trained on 1530 images from 8 healthy volunteers and tested on 70 images from 2 healthy volunteers. In addition, the network was tested on 6 synthetic images generated to simulate blood flow velocity patterns at the site of hemorrhage. Results show significant (p<0.05) differences in anomaly scores of normal arteries and simulated injured arteries. The residual images, or the reconstruction error maps, show promise in localizing anomalies at pixel level.
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Asha Singanamalli, Jhimli Mitra, Kirk Wallace, Prem Venugopal, Scott Smith, Larry Mo, Lai Yee Leung, Jonathan Morrison, Todd Rasmussen, and Luca Marinelli "Blood flow anomaly detection via generative adversarial networks: a preliminary study", Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 1131522 (16 March 2020); https://doi.org/10.1117/12.2549094
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KEYWORDS
Arteries
Ultrasonography
Data modeling
Network architectures
Blood vessels
Artificial intelligence
Artificial neural networks
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