Jiang Shaowei, Liao Jun, Bian Zichao, Guo Kaikai, Zhang Yongbing, Zheng Guoan
Biomedical Engineering, University of Connecticut, Storrs, CT, 06269, USA.
These authors contributed equally to this work.
Biomed Opt Express. 2018 Mar 8;9(4):1601-1612. doi: 10.1364/BOE.9.001601. eCollection 2018 Apr 1.
A whole slide imaging (WSI) system has recently been approved for primary diagnostic use in the US. The image quality and system throughput of WSI is largely determined by the autofocusing process. Traditional approaches acquire multiple images along the optical axis and maximize a figure of merit for autofocusing. Here we explore the use of deep convolution neural networks (CNNs) to predict the focal position of the acquired image without axial scanning. We investigate the autofocusing performance with three illumination settings: incoherent Kohler illumination, partially coherent illumination with two plane waves, and one-plane-wave illumination. We acquire 130,000 images with different defocus distances as the training data set. Different defocus distances lead to different spatial features of the captured images. However, solely relying on the spatial information leads to a relatively bad performance of the autofocusing process. It is better to extract defocus features from transform domains of the acquired image. For incoherent illumination, the Fourier cutoff frequency is directly related to the defocus distance. Similarly, autocorrelation peaks are directly related to the defocus distance for two-plane-wave illumination. In our implementation, we use the spatial image, the Fourier spectrum, the autocorrelation of the spatial image, and combinations thereof as the inputs for the CNNs. We show that the information from the transform domains can improve the performance and robustness of the autofocusing process. The resulting focusing error is ~0.5 µm, which is within the 0.8-µm depth-of-field range. The reported approach requires little hardware modification for conventional WSI systems and the images can be captured on the fly without focus map surveying. It may find applications in WSI and time-lapse microscopy. The transform- and multi-domain approaches may also provide new insights for developing microscopy-related deep-learning networks. We have made our training and testing data set (12 GB) open-source for the broad research community.
最近，一种全玻片成像（WSI）系统已在美国获批用于初步诊断。WSI的图像质量和系统通量在很大程度上取决于自动对焦过程。传统方法是沿光轴采集多幅图像，并最大化自动对焦的品质因数。在此，我们探索使用深度卷积神经网络（CNN）来预测采集图像的焦平面位置，而无需进行轴向扫描。我们研究了三种照明设置下的自动对焦性能：非相干柯勒照明、双平面波部分相干照明和单平面波照明。我们采集了约130,000张具有不同离焦距离的图像作为训练数据集。不同的离焦距离会导致所捕获图像的不同空间特征。然而，仅依靠空间信息会导致自动对焦过程的性能相对较差。最好从采集图像的变换域中提取离焦特征。对于非相干照明，傅里叶截止频率与离焦距离直接相关。同样，对于双平面波照明，自相关峰值与离焦距离直接相关。在我们的实现中，我们使用空间图像、傅里叶频谱、空间图像的自相关及其组合作为CNN的输入。我们表明，来自变换域的信息可以提高自动对焦过程的性能和鲁棒性。由此产生的聚焦误差约为0.5 µm，在0.8 µm的景深范围内。所报道的方法对于传统WSI系统几乎不需要硬件修改，并且可以在不进行焦平面地图测量的情况下即时捕获图像。它可能在WSI和延时显微镜中找到应用。变换和多域方法也可能为开发与显微镜相关的深度学习网络提供新的见解。我们已将我们的训练和测试数据集（约12 GB）开源给广大研究社区。
