Bioprinting is a rapidly advancing field that offers a transformative approach to fabricating tissue and organ models through the precise deposition of cell-laden bioinks. Ensuring the fidelity and consistency of printed structures in real-time remains a core challenge, particularly under constraints imposed by limited imaging data and resource-constrained embedded hardware. Semantic segmentation of the extrusion process, differentiating between nozzle, extruded bioink, and surrounding background, enables in situ monitoring critical to maintaining print quality and biological viability. In this work, we introduce a lightweight semantic segmentation framework tailored for real-time bioprinting applications. We present a novel, manually annotated dataset comprising 787 RGB images captured during the bioprinting process, labeled across three classes: nozzle, bioink, and background. To achieve fast and efficient inference suitable for integration with bioprinting systems, we propose a BioLite U-Net architecture that leverages depthwise separable convolutions to drastically reduce computational load without compromising accuracy. Our model is benchmarked against MobileNetV2 and MobileNetV3-based segmentation baselines using mean Intersection over Union (mIoU), Dice score, and pixel accuracy. All models were evaluated on a Raspberry Pi 4B to assess real-world feasibility. The proposed BioLite U-Net achieves an mIoU of 92.85% and a Dice score of 96.17%, while being over 1300x smaller than MobileNetV2-DeepLabV3+. On-device inference takes 335 ms per frame, demonstrating near real-time capability. Compared to MobileNet baselines, BioLite U-Net offers a superior tradeoff between segmentation accuracy, efficiency, and deployability, making it highly suitable for intelligent, closed-loop bioprinting systems.

翻译：生物打印是一个快速发展的领域，它通过精确沉积负载细胞的生物墨水，为制造组织和器官模型提供了一种变革性方法。确保打印结构在实时的保真度和一致性仍然是一个核心挑战，特别是在成像数据有限和资源受限的嵌入式硬件所施加的约束下。对挤出过程进行语义分割，区分喷嘴、挤出的生物墨水和周围背景，是实现原位监测的关键，这对于维持打印质量和生物活性至关重要。在本工作中，我们引入了一个专为实时生物打印应用量身定制的轻量级语义分割框架。我们提出了一个新颖的手动标注数据集，包含在生物打印过程中捕获的787张RGB图像，标注为喷嘴、生物墨水和背景三个类别。为了实现适合与生物打印系统集成的快速高效推理，我们提出了BioLite U-Net架构，该架构利用深度可分离卷积大幅降低计算负载，同时不牺牲准确性。我们使用平均交并比（mIoU）、Dice分数和像素精度，将我们的模型与基于MobileNetV2和MobileNetV3的分割基线进行了基准测试。所有模型均在Raspberry Pi 4B上评估以衡量实际可行性。所提出的BioLite U-Net实现了92.85%的mIoU和96.17%的Dice分数，同时其模型大小比MobileNetV2-DeepLabV3+小了1300倍以上。在设备上的推理速度为每帧335毫秒，展示了近实时能力。与MobileNet基线相比，BioLite U-Net在分割准确性、效率和可部署性之间提供了更优的权衡，使其非常适用于智能闭环生物打印系统。