Automatic hemorrhage detection from color Doppler ultrasound using a Generative Adversarial Network (GAN)-based anomaly detection method
Author
Mitra, JhimliQiu, Jianwei
MacDonald, Michael
Venugopal, Prem
Wallace, Kirk
Abdou, Hossam
Richmond, Michael
Elansary, Noha
Edwards, Joseph
Patel, Neerav
Morrison, Jonathan
Marinelli, Luca
Date
2022-01-01Journal
IEEE Journal of Translational Engineering in Health and MedicinePublisher
IEEEType
Article
Metadata
Show full item recordAbstract
Hemorrhage control has been identified as a priority focus area both for civilian and military populations in the United States because exsanguination is the most common cause of preventable death in hemorrhagic injury. Non-compressible torso hemorrhage (NCTH) has high mortality rate and there are currently no broadly available therapies for NCTH outside of a surgical room environment. Novel therapies, which include High Intensity Focused Ultrasound (HIFU) have emerged as promising methods for hemorrhage control as they can non-invasively cauterize bleeding tissue deep within the body without injuring uninvolved regions. A major challenge in the application of HIFU with color Doppler US guidance is the interpretation and optimization of the blood flow images in real-time to identify the hemorrhagic focus. Today, this task requires an expert sonographer, limiting the utility of this therapy in non-clinical environments. In this work, we investigated the feasibility of an automated hemorrhage detection method using a Generative Adversarial Network (GAN) for anomaly detection that 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 femoral arteries in an animal model of vascular injury (N = 11 pigs). Velocity information of the blood flow were extracted from the color Doppler images that were used for training and testing the anomaly detection network. Normotensive images from 8 pigs were used for training, and testing was performed on normotensive, immediately after injury, 10 minutes post-injury and 30 minutes post-injury images from 3 other pigs. The residual images or the reconstructed error maps show promise in detecting hemorrhages with an AUC of 0.90, 0.87, 0.62 immediately, 10 minutes post-injury and 30 minutes post-injury respectively with an overall AUC of 0.83.Keyword
Anomaly detectioncolor Doppler ultrasound
deep learning
Doppler effect
generative adversarial network
Generative adversarial networks
hemorrhage detection
Hemorrhaging
Image color analysis
Training
Ultrasonic imaging
unsupervised anomaly detection
Identifier to cite or link to this item
http://hdl.handle.net/10713/19687ae974a485f413a2113503eed53cd6c53
10.1109/JTEHM.2022.3199987
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