算法
图像质量
人工智能
计算机科学
质量(理念)
鉴定(生物学)
微流控
频道(广播)
红细胞
细胞仪
血细胞
模式识别(心理学)
流式细胞术
图像(数学)
医学
生物
免疫学
材料科学
纳米技术
电信
哲学
植物
认识论
作者
Yuping Yang,He Hong,Junju Wang,Li Chen,Yi Xu,Changrong Ge,Shunbo Li
出处
期刊:Lab on a Chip
[The Royal Society of Chemistry]
日期:2023-01-01
卷期号:23 (8): 2113-2121
摘要
The quality of red blood cells (RBCs) in stored blood has a direct impact on the recovery of patients treated by blood transfusion, which directly reflects the quality of blood. The traditional means for blood quality evaluation involve the use of reagents and multi-step and time-consuming operations. Here, a low-cost, multi-classification, label-free and high-precision method is developed, which combines microfluidic technology and a deep learning algorithm together to recognize and classify RBCs based on morphology. The microfluidic channel is designed to effectively and controllably solve the problem of cell overlap, which has a severe negative impact on the identification of cells. The object detection model in the deep learning algorithm is optimized and used to recognize multiple RBCs simultaneously in the whole field of view, so as to classify them into six morphological subcategories and count the numbers in each subgroup. The mean average precision of the developed object detection model reaches 89.24%. The blood quality can be evaluated by calculating the morphology index (MI) according to the numbers of cells in subgroups. The validation of the method is verified by evaluating three blood samples stored for 7 days, 21 days and 42 days, which have MIs of 84.53%, 73.33% and 24.34%, respectively, indicating good agreement with the actual blood quality. This method has the merits of cell identification in a wide channel, no need for single cell alignment as the image cytometry does and it is not only applicable to the quality evaluation of RBCs, but can also be used for general cell identifications with different morphologies.
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