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.
%0 Conference Paper
%1 10178834
%A Liu, Jan
%A Bajraktari, Flakë
%A Rausch, Regine
%A Pott, Peter P.
%B 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS)
%D 2023
%K imt myown
%P 57-60
%R 10.1109/CBMS58004.2023.00192
%T 3D Reconstruction of Forearm Veins Using NIR-Based Stereovision and Deep Learning
%X 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.
@inproceedings{10178834,
abstract = {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.},
added-at = {2023-07-18T09:56:50.000+0200},
author = {Liu, Jan and Bajraktari, Flakë and Rausch, Regine and Pott, Peter P.},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/21abe24cfc379c0e2a65bfc62d52897d8/janliu},
booktitle = {2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS)},
doi = {10.1109/CBMS58004.2023.00192},
interhash = {5e48348f0813e2b56a7f1417ce47aa44},
intrahash = {1abe24cfc379c0e2a65bfc62d52897d8},
issn = {2372-9198},
keywords = {imt myown},
month = {June},
pages = {57-60},
timestamp = {2023-07-18T09:57:20.000+0200},
title = {3D Reconstruction of Forearm Veins Using NIR-Based Stereovision and Deep Learning},
year = 2023
}
