Internal bleeding detection algorithm based on determination of organ boundary by low-brightness set analysis

Ito, Keiichiro; Sugano, Shigeki; Iwata, Hiroyasu

doi:10.1109/iros.2012.6385745

Cited by 6 publications

(6 citation statements)

References 10 publications

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“…To the best of our knowledge, this is the first study to attempt detecting hemorrhagic focus automatically in ultrasound color Doppler images for immediate attention and possible cauterization of the artery to prevent blood loss. Previous studies [19] , [20] have attempted automatic detection of blood pool from ultrasound images for emergency diagnosis in the event of blunt traumatic injury using a machine learning framework. There have been several studies [40] , [41] attempting to detect and localize cranial hemorrhages using deep learning from CT images after traumatic injury.…”

Section: Discussionmentioning

confidence: 99%

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Automatic Hemorrhage Detection From Color Doppler Ultrasound Using a Generative Adversarial Network (GAN)-Based Anomaly Detection Method

Mitra

Qiu

MacDonald

et al. 2022

IEEE J. Transl. Eng. Health Med.

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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.

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Section: Discussionmentioning

confidence: 99%

“…There are a few unsupervised methods to detect blood pool in blunt abdominal trauma from Focused Assessment with Sonography in Trauma (FAST) ultrasound. The methods involve image pre-processing, and analysis of local intensity features using K-Means clustering and levelsets [19] , [20] , [21] , [22] .…”

Section: Introductionmentioning

confidence: 99%

Automatic Hemorrhage Detection From Color Doppler Ultrasound Using a Generative Adversarial Network (GAN)-Based Anomaly Detection Method

Mitra

Qiu

MacDonald

et al. 2022

IEEE J. Transl. Eng. Health Med.

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“…27 An automatic internal bleeding detection robot system based on ultrasound (US) image processing was constructed to improve the sensitivity. 28,29 The boundary of the organ (liver and kidney) and internal bleeding were determined using low-brightness set analysis. However, failure of segmentation and detection might occur when the extracted organ area is too small in the image.…”

Section: Field Diagnosis Robotsmentioning

confidence: 99%

Rescue Robots for the Urban Earthquake Environment

Hou

et al. 2022

Disaster med. public health prep.

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Robotics have important applications in the field of disaster medical rescue. The deployment of urban rescue robots at the earthquake site can help shorten response time, improve rescue efficiency and keep rescue personnel away from danger. This discussion introduces the performance of some robots in actual rescue scenarios, focuses on the current research status of robots that can provide medical assistance, and analyzes the merits and shortcomings of each system. Based on existing studies, the limitations and development directions of urban rescue robots are also discussed.

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“…Nadeau et al proposed ultrasound intensity-based visual servoing improvement using 2D bi-plane probe for tracking and positioning task framework [9]. Ito et al proposed a system that utilizes ultrasound sensor to detect internal bleeding [10].…”

Section: Figure 1 Intelligent Ultrasound Systemmentioning

confidence: 99%