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Efficient Segmentation Network for Real-Time Blood Vessel Detection of Ultrasound Image on Mobile Devices

장진태 (Jang, Jin Tae, 서강대학교 일반대학원)

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Needle guide technology is in the spotlight for safer and more accurate injections. Ultrasound imaging systems are widely used in needle guide applications because they can show real-time monitoring and help precise injections. It is important to detect the blood vessels that the needle will inject...
Needle guide technology is in the spotlight for safer and more accurate injections. Ultrasound imaging systems are widely used in needle guide applications because they can show real-time monitoring and help precise injections. It is important to detect the blood vessels that the needle will inject for accurate injection. This paper uses a deep learning method to detect blood vessels. With the advances in deep learning, the need for On-Device Machine Learning which applies to it to embedded systems using has increased. However, the low calculation power and low amount of memory are the limitations for On-Device Machine Learning in mobile devices. This study suggests an efficient segmentation network and implementation optimization for On-Device Machine Learning in mobile devices. Evaluations of human forearm data show that this method can segment blood vessels with real-time processing in mobile devices.