Automatic Recognition of Standard Liver Sections Based on Vision-Transformer

Abstract

Acquisition of ultrasound standard views is a prerequisite for performing ultrasound diagnosis. With the aim of solving the clinical imaging challenge that adult liver standard sections have long been constrained by physicians' subjective experience, this paper collects 12 common liver ultrasound standard sections from the Second Hospital of Fujian Medical University and investigates and discusses the adaptability of the Vision-Transformer(ViT)-based deep learning automatic recognition method in liver ultrasound standard sections. Using a regional pixel set segmentation operation on liver ultrasound images, we found that the ViT model achieved recognition accuracy of 92.9% in the available ultrasound dataset when the basic segmentation module was 16*16 and the depth was 12. We also compared other mainstream deep learning frameworks based on convolutional neural networks, and the ViT model outperformed all other methods, guided by the features of the visual attention mechanism. The work in this paper provides a rich research base for deep learning of liver ultrasound based on the visual attention mechanism, and to a certain extent standardises the medical examination of the liver in adults by ultrasound-based means.