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.

翻译：评估肘前窝静脉状况和可见度是获取静脉通路前关键步骤，该部位常用于抽血或静脉治疗给药。尽管医疗从业者具备娴熟的静脉穿刺技能，但在面对因体液潴留、年龄增长、超重、深色皮肤或糖尿病导致静脉可见度低的患者时，操作仍常遇困难。近期多项研究提出采用近红外成像与深度学习技术进行前臂静脉分割，虽已取得显著成果，但受限于静脉穿刺所需的便携性和精度要求，实际应用仍较为局限。本文通过三项策略助力弥合这一差距：首先，我们建立包含来自1008名低可见度静脉受试者的2016张标注图像的新型近红外前臂静脉分割数据集；其次，提出改进型U-Net架构，可精确定位患者肘前窝区域静脉；最后，在测试四种常见嵌入式微型计算机和四种常用量化模式后，将压缩版架构部署于定制便携式静脉探测器设备。实验结果表明，采用动态范围量化压缩并部署于Raspberry Pi 4B开发板的模型在执行速度与精度间取得最佳平衡，分别实现5.14 FPS帧率、0.957延迟和交并比指标。这些结果展现了该模型在资源受限低成本设备中的优异性能潜力。