  Assessing the condition and visibility of veins is a crucial step before
obtaining intravenous access in the antecubital fossa, which is a common
procedure to draw blood or administer intravenous therapies (IV therapies).
Even though medical practitioners are highly skilled at intravenous
cannulation, they usually struggle to perform the procedure in patients with
low visible veins due to fluid retention, age, overweight, dark skin tone, or
diabetes. Recently, several investigations proposed combining Near Infrared
(NIR) imaging and deep learning (DL) techniques for forearm vein segmentation.
Although they have demonstrated compelling results, their use has been rather
limited owing to the portability and precision requirements to perform
venipuncture. In this paper, we aim to contribute to bridging this gap using
three strategies. First, we introduce a new NIR-based forearm vein segmentation
dataset of 2,016 labelled images collected from 1,008 subjects with low visible
veins. Second, we propose a modified U-Net architecture that locates veins
specifically in the antecubital fossa region of the examined patient. Finally,
a compressed version of the proposed architecture was deployed inside a
bespoke, portable vein finder device after testing four common embedded
microcomputers and four common quantization modalities. Experimental results
showed that the model compressed with Dynamic Range Quantization and deployed
on a Raspberry Pi 4B card produced the best execution time and precision
balance, with 5.14 FPS and 0.957 of latency and Intersection over Union (IoU),
respectively. These results show promising performance inside a
resource-restricted low-cost device.
