Whole slide imaging (WSI) is an emerging technology for digital pathology. The accuracy and speed of autofocusing are critical for the performance of the WSI system. This paper introduces a novel technique of deep autofocusing for WSI. Instead of mechanically adjusting the focal distance on a tile-by-tile basis, we develop a deep convolutional neural network for tile-wise autofocusing to generate in-focus images from tentative possibly defocused images. This deep autofocusing network (DAFNet) works with only two images taken at different focal distances; in contrast, traditional methods need to take, for each tile of the target ultra high-resolution pathology image, a stack of as many as 21 shots with varying focal distances. The novel architecture design of DAFNet facilitates the fusion of complementary information of the two input images of different focal distances. The proposed off-line reconstruction strategy allows high throughput scanning of sample slides done without compromising image quality, because DAFNet can rectify errors in focal distance and bring the scanned tiles back into focus by learnt non-linear dual-input blur-to-sharp mapping. Experimental results demonstrate the refocusing capability of the DAFNet method.

中文翻译：

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通过双镜头深度自动对焦快速进行全幻灯片成像

全玻片成像（WSI）是一种新兴的数字病理技术。自动对焦的准确性和速度对于WSI系统的性能至关重要。本文介绍了一种新颖的技术深度自动对焦用于WSI。代替在逐个图块的基础上机械地调整焦距，我们开发了一个深度卷积神经网络，用于逐个图块自动聚焦，以从可能散焦的临时图像生成聚焦图像。这种深层自动对焦网络（DAFNet）仅适用于在不同焦距下拍摄的两个图像。相反，传统方法需要针对目标超高分辨率病理图像的每个图块，拍摄多达21张具有不同焦距的镜头。DAFNet的新颖架构设计有助于融合不同焦距的两个输入图像的互补信息。拟议的离线重建策略可在不影响图像质量的情况下对样品玻片进行高通量扫描，因为DAFNet可以纠正焦距误差，并通过学习的非线性双输入模糊到锐化映射使扫描的图块重新聚焦。实验结果证明了DAFNet方法的重新聚焦能力。