Deep Learning-Based Vein Localization on Embedded System

Venipuncture is a common process in medical treatment. In the fight against pandemic like COVID-19, it is often very difficult for medical staff to carry out venipuncture accurately, since the staff have to wear safety glasses and surgical gloves. In this work, we designed an embedded system which implements deep learning algorithm to localize veins from color skin images. The proposed method consists of a fully convolutional neural network (CNN) as encoder and feature extractor, a dilated convolution module, and a transposed convolution module as decoder. A synchronized RGB/Near Infrared (NIR) image database was constructed to provide the mapping information between the two image fields. A combined loss function which includes a per-pixel loss and a perceptual loss was presented to optimize the network parameters. To make the model adaptive to different images, a histogram specification scheme was adopted to transform the color style of an image. The model was then implemented on a NVIDIA Jetson TX2 development kit. Comprehensive experiments were conducted on different databases to evaluate the proposed method and the embedded system. Experimental results showed that the system has satisfactory performance and a promising perspective in daily medical treatment.