用深度学习实时分析血细胞聚集，提升无标记血液诊断精度
Real-time deep learning phase imaging flow cytometer reveals blood cell aggregate biomarkers for haematology diagnostics
- 基于数字全息显微镜与图神经网络，实时识别血细胞聚集体
- 处理30GB以上数据，1.5分钟内完成检测，血小板聚集识别误差仅8.9%
- 适合临床点诊场景，可嵌入常规血液检测流程
尽管自动化血液学中稀有血细胞聚集体的分析仍具挑战性，但其可能显著推动无标记功能诊断的发展。传统流式细胞仪虽能高效计数并分类白细胞，却无法识别聚集体，需人工复核。定量相位成像流式细胞术可捕捉聚集体的精细形态，但受限于海量数据存储与离线处理，难以临床应用。将隐藏生物标志物纳入常规血液检测可大幅提升诊断水平。本文提出RT-HAD，一种端到端的深度学习图像与数据处理框架，用于离轴数字全息显微镜（DHM），结合物理一致的全息重建与检测，以图结构表示每个血细胞，实现聚集体识别。RT-HAD可实时处理超过30 GB图像数据，周转时间小于1.5分钟，血小板聚集体检测误差率为8.9%，达到实验室可接受水平，解决了点诊场景下的大数据难题。
原文摘要 · Abstract (English)
While analysing rare blood cell aggregates remains challenging in automated haematology, they could markedly advance label-free functional diagnostics. Conventional flow cytometers efficiently perform cell counting with leukocyte differentials but fail to identify aggregates with flagged results, requiring manual reviews. Quantitative phase imaging flow cytometry captures detailed aggregate morphologies, but clinical use is hampered by massive data storage and offline processing. Incorporating hidden biomarkers into routine haematology panels would significantly improve diagnostics without flagged results. We present RT-HAD, an end-to-end deep learning-based image and data processing framework for off-axis digital holographic microscopy (DHM), which combines physics-consistent holographic reconstruction and detection, representing each blood cell in a graph to recognize aggregates. RT-HAD processes >30 GB of image data on-the-fly with turnaround time of <1.5 min and error rate of 8.9% in platelet aggregate detection, which matches acceptable laboratory error rates of haematology biomarkers and solves the big data challenge for point-of-care diagnostics.
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