Segmentation of Radial Nerve in Ultrasound Images Using Convolutional Neural Network

  • 林 晉宇

Student thesis: Doctoral Thesis

Abstract

In recent years deep learning has achieved huge successes in many aspects such as object recognition and semantic analysis Deep learning gets a great development in medical image as well Peripheral Nerve Blocks is a type of regional anesthesia which need to find out the location of nerves and inject anesthetic nearby using ultrasound scanning However images recognition by medical experts is time-consuming We trained a deep learning model which can perform segmentation of nerves in real time processing It can be applied to an anesthesia technique called ‘Ultrasound-Guided Regional Anesthesia (UGRA) UGRA has less side-effect compare to general anesthesia Our training dataset are acquired from two collaborative hospitals Kaohsiung Veterans General Hospital (KVGH) and Kaohsiung Medical University Chung-Ho Memorial Hospital (KMUH) Our frameworks are based on the U-net model Moreover we prune our models using the Net-Trim algorithm which is capable to reduce the parameters from a trained model A simplified model consumes less prediction time and memory space Our proposal achieves 0 59 Dice Coefficient and 0 93 accuracy for nerve segmentation
Date of Award2020
Original languageEnglish
SupervisorMing-Long Wu (Supervisor)

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