3D Reconstruction of Forearm Veins Using NIR-Based Stereovision and Deep Learning

In this paper, the development of a cost-effective assistance system for venipuncture is presented. The system locates forearm veins through near-infrared imaging, depth estimation, deep learning segmentation, and 3D reconstruction. A single-board computer was integrated with two infrared cameras and two 760 nm near-infrared (NIR) LEDs to capture and process stereo images. The depth estimation was achieved through stereo triangulation. A deep learning model based on the U-Net architecture with an attention mechanism and a training dataset of 900 images from 40 participants was used for vein segmentation. Depth information and segmented veins were combined to enable a 3D visualization of the veins. The results show a Jaccard-Score of 92.80 % for vein segmentation and an average reprojection error of 0.48 pixels for the 3D reconstruction.