关键词: 超声治疗, 全卷积网络, 深度聚合, 小数据集, 迁移学习

Abstract:

In recent years, with the application of ultrasonic cavitation in medical treatment, ultrasonic cavitation therapy has returned to people’s field of vision, and its extensive advantages in dealing with vascular occlusion have caused extensive discussion and research. In order to achieve accurate real-time treatment, a method of ultrasonic vessel segmentation based on Fully Convolutional Networks（FCN） is proposed. The Full Convolutional deep Aggregation Migration Network（AMFCN） uses a symmetric network connection to the full convolutional network, deeply extracts image features in depth aggregation mode, optimizes the data enhancement mode, adds a migration learning model, and selects a suitable loss function. The method effectively utilizes existing data for data expansion and alleviates the influence of too little medical image data. The experimental results show that the research method can achieve better segmentation performance on the ultrasound blood vessel image and accurately segment the blood vessel region.

Key words: ultrasonic therapy, Full Convolutional Network（FCN）, deep aggregation, small data set, migration learning