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dc.contributor.authorMitra, Jhimli
dc.contributor.authorQiu, Jianwei
dc.contributor.authorMacDonald, Michael
dc.contributor.authorVenugopal, Prem
dc.contributor.authorWallace, Kirk
dc.contributor.authorAbdou, Hossam
dc.contributor.authorRichmond, Michael
dc.contributor.authorElansary, Noha
dc.contributor.authorEdwards, Joseph
dc.contributor.authorPatel, Neerav
dc.contributor.authorMorrison, Jonathan
dc.contributor.authorMarinelli, Luca
dc.date.accessioned2022-09-07T13:57:42Z
dc.date.available2022-09-07T13:57:42Z
dc.date.issued2022-01-01
dc.identifier.urihttp://hdl.handle.net/10713/19687
dc.description.abstractHemorrhage 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.en_US
dc.description.urihttps://doi.org/10.1109/JTEHM.2022.3199987en_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.ispartofIEEE Journal of Translational Engineering in Health and Medicineen_US
dc.subjectAnomaly detectionen_US
dc.subjectcolor Doppler ultrasounden_US
dc.subjectdeep learningen_US
dc.subjectDoppler effecten_US
dc.subjectgenerative adversarial networken_US
dc.subjectGenerative adversarial networksen_US
dc.subjecthemorrhage detectionen_US
dc.subjectHemorrhagingen_US
dc.subjectImage color analysisen_US
dc.subjectTrainingen_US
dc.subjectUltrasonic imagingen_US
dc.subjectunsupervised anomaly detectionen_US
dc.titleAutomatic hemorrhage detection from color Doppler ultrasound using a Generative Adversarial Network (GAN)-based anomaly detection methoden_US
dc.typeArticleen_US
dc.identifier.doi10.1109/JTEHM.2022.3199987
dc.source.journaltitleIEEE Journal of Translational Engineering in Health and Medicine


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