Ultrasound imaging is the most widely adopted medical modality globally due to its low cost and portability, yet artificial intelligence (AI) deployment remains constrained by reliance on GPU-accelerated models, creating a structural paradox where the cost of "intelligence" exceeds that of the imaging device itself. Here, we present the systematic adaptation and extensive evaluation of UltraSeg, an ultra-lightweight architecture originally developed for colonoscopic polyp segmentation, now engineered for point-of-care ultrasound (POCUS) across ten public datasets spanning six anatomical sites (breast, thyroid, kidney, carotid, fetal, and small-animal tumor). We systematically validate both variants in ultrasound domains: UltraSeg-130K (0.13M parameters) achieves 89.7 FPS on single-core CPUs and 34.8 FPS on a refurbished mobile device, while UltraSeg-500K (0.5M parameters) delivers 44.6 FPS on CPU and 16.1 FPS on mobile device. UltraSeg-500K matches or exceeds the Dice performance of the 31M-parameter UNet and approaches 105M-parameter TransUNet in average performance, with superior zero-shot cross-dataset generalization on external validation sets (UDIAT, DDTI). By enabling clinical-grade segmentation without GPU dependency, this work brings AI costs in line with ultrasound accessibility, making advanced diagnostics available in resource-limited settings.

翻译：超声成像因其低成本与便携性成为全球应用最广泛的医学影像模态，然而人工智能部署仍受限于对GPU加速模型的依赖，形成"智能"成本反超成像设备的结构性悖论。本文系统展示了UltraSeg这一超轻量级架构的适应性改造与全面评估——该架构原用于结肠镜息肉分割，现经工程优化后适用于床旁超声，在覆盖六个解剖部位（乳腺、甲状腺、肾脏、颈动脉、胎儿及小动物肿瘤）的十个公开数据集上进行验证。我们系统验证了两种变体在超声领域的性能：UltraSeg-130K（0.13M参数）在单核CPU上达到89.7 FPS，在翻新移动设备上达34.8 FPS；UltraSeg-500K（0.5M参数）在CPU与移动设备上分别实现44.6 FPS和16.1 FPS。UltraSeg-500K在Dice指标上可媲美甚至超越31M参数的UNet，平均性能接近105M参数的TransUNet，且在外部验证集（UDIAT、DDTI）上展现出卓越的零样本跨数据集泛化能力。本研究通过实现无GPU依赖的临床级分割，使AI成本与超声可及性相匹配，为资源有限环境下的先进诊断提供可能。