超音波診断画像の自動獲得のための小型超音波ロボットの開発

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  • Estimation of Organ State utilizing Deep Learning Tiny-YOLOv3 and Fixed Lag Smoothing Motion Filter

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<p>In this research, we proposed a robotic motion control framework that can detect kidney in real-time by utilizing deep learning, and evaluate the accuracy of automatically acquiring and maintaining ultrasound diagnostic images of kidney. Furthermore, we performed object detection experiments using a model that was generated by a kidney phantom, estimated the state of renal phantom motion.</p><p>The novelty of our method is the framework of the combination of the deep learning tiny-YOLOv3 model and filtering considering the influence of speckle noise in an ultrasound image. In our method, the filtering is determined to incorporate the center position of where the object is detected, and two types of filters are adopted.</p>

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