Kaza Nischita, Ojaghi Ashkan, Robles Francisco E
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.
Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA.
BME Front. 2022 Jul 1;2022:9853606. doi: 10.34133/2022/9853606. eCollection 2022.
. We present a fully automated hematological analysis framework based on single-channel (single-wavelength), label-free deep-ultraviolet (UV) microscopy that serves as a fast, cost-effective alternative to conventional hematology analyzers. . Hematological analysis is essential for the diagnosis and monitoring of several diseases but requires complex systems operated by trained personnel, costly chemical reagents, and lengthy protocols. Label-free techniques eliminate the need for staining or additional preprocessing and can lead to faster analysis and a simpler workflow. In this work, we leverage the unique capabilities of deep-UV microscopy as a label-free, molecular imaging technique to develop a deep learning-based pipeline that enables virtual staining, segmentation, classification, and counting of white blood cells (WBCs) in single-channel images of peripheral blood smears. . We train independent deep networks to virtually stain and segment grayscale images of smears. The segmented images are then used to train a classifier to yield a quantitative five-part WBC differential. Our virtual staining scheme accurately recapitulates the appearance of cells under conventional Giemsa staining, the gold standard in hematology. The trained cellular and nuclear segmentation networks achieve high accuracy, and the classifier can achieve a quantitative five-part differential on unseen test data. . This proposed automated hematology analysis framework could greatly simplify and improve current complete blood count and blood smear analysis and lead to the development of a simple, fast, and low-cost, point-of-care hematology analyzer.
我们提出了一种基于单通道（单波长）、无标记深紫外（UV）显微镜的全自动血液学分析框架，它可作为传统血液学分析仪的一种快速、经济高效的替代方案。血液学分析对于多种疾病的诊断和监测至关重要，但需要由经过培训的人员操作的复杂系统、昂贵的化学试剂和冗长的流程。无标记技术无需染色或额外的预处理，可实现更快的分析和更简单的工作流程。在这项工作中，我们利用深紫外显微镜作为一种无标记分子成像技术的独特功能，开发了一种基于深度学习的流程，能够对外周血涂片的单通道图像中的白细胞（WBC）进行虚拟染色、分割、分类和计数。我们训练独立的深度网络对涂片的灰度图像进行虚拟染色和分割。然后，将分割后的图像用于训练分类器，以产生定量的五分类白细胞差异计数。我们的虚拟染色方案准确地再现了细胞在传统吉姆萨染色（血液学中的金标准）下的外观。经过训练的细胞和细胞核分割网络具有很高的准确性，并且分类器可以对未见过的测试数据实现定量的五分类差异计数。所提出的这种自动血液学分析框架可以极大地简化和改进当前的全血细胞计数和血液涂片分析，并推动一种简单、快速且低成本的即时检测血液学分析仪的开发。
