Echo Image Segmentation Model for Acupuncture

Abstract

Acupuncture is a type of medical treatment in which needles are inserted into a patient's body. Although this treatment has many therapeutic benefits, the procedure is dangerous to some extents. There are no clear evidence have been identified. To tackle this problem, visual information such as CT are effective. But current systems using echo images for acupuncture face challenges in accuracy and execution time due to a lack of training data and not yet implemented the latest models. It is difficult to obtain the data, we implemented a DNN (Deep Neural Network) which conducts segmentation of blood vessels and nerves that can be easily identified with a small data set. Current system runs on a CNN (Convolutional Neural Network)-based U-net model, this study implemented different image segmentation method, ViT (Vision Transformer), to compare their performance. When a DNN was constructed with the same dataset and using Dice and IoU as evaluation metrics, it showed 87% accuracy for vessel segmentation, an 8% improvement in accuracy compared to existing systems. In conclusion, ViT showed a practical level of performance for vascular recognition, but existing methods were superior for neural recognition. A deep learning model combining CNN and ViT is expected to greatly improve accuracy. Transitional learning is effective for this purpose, and we plan to incorporate it in the future.