This work demonstrates a multi-lens microscopic imaging system that overlaps multiple independent fields of view on a single sensor for high-efficiency automated specimen analysis. Automatic detection, classification and counting of various morphological features of interest is now a crucial component of both biomedical research and disease diagnosis. While convolutional neural networks (CNNs) have dramatically improved the accuracy of counting cells and sub-cellular features from acquired digital image data, the overall throughput is still typically hindered by the limited space-bandwidth product (SBP) of conventional microscopes. Here, we show both in simulation and experiment that overlapped imaging and co-designed analysis software can achieve accurate detection of diagnostically-relevant features for several applications, including counting of white blood cells and the malaria parasite, leading to multi-fold increase in detection and processing throughput with minimal reduction in accuracy.

翻译：这项工作展示了一种多镜头微型成像系统,它与用于高效自动标本分析的单一传感器的多个独立观察领域重叠。自动检测、分类和计数各种值得关注的形态特征现在是生物医学研究和疾病诊断的一个关键组成部分。虽然进化神经网络大大提高了从获得的数字图像数据中计数细胞和子细胞特征的准确性,但总吞吐量仍然通常受到常规显微镜有限空间带宽产品(SBP)的阻碍。在这里,我们在模拟和实验中都显示,重叠成像和共同设计的分析软件能够准确探测若干应用的诊断相关特征,包括计算白血细胞和疟疾寄生虫,导致通过最小精确度降低的成份的多倍检测和处理增加。