Rapid Whole Slide Imaging via Learning-based Two-shot Virtual Autofocusing

• URL: http://arxiv.org/abs/2003.06630v1
• Date: Sat, 14 Mar 2020 13:40:33 GMT
• Title: Rapid Whole Slide Imaging via Learning-based Two-shot Virtual Autofocusing
• Authors: Qiang Li, Xianming Liu, Kaige Han, Cheng Guo, Xiangyang Ji, and Xiaolin Wu
• Abstract summary: Whole slide imaging (WSI) is an emerging technology for digital pathology. We propose the concept of textitvirtual autofocusing, which does not rely on mechanical adjustment to conduct refocusing.
• Score: 57.90239401665367
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Whole slide imaging (WSI) is an emerging technology for digital pathology. The process of autofocusing is the main influence of the performance of WSI. Traditional autofocusing methods either are time-consuming due to repetitive mechanical motions, or require additional hardware and thus are not compatible to current WSI systems. In this paper, we propose the concept of \textit{virtual autofocusing}, which does not rely on mechanical adjustment to conduct refocusing but instead recovers in-focus images in an offline learning-based manner. With the initial focal position, we only perform two-shot imaging, in contrast traditional methods commonly need to conduct as many as 21 times image shooting in each tile scanning. Considering that the two captured out-of-focus images retain pieces of partial information about the underlying in-focus image, we propose a U-Net-inspired deep neural network based approach for fusing them into a recovered in-focus image. The proposed scheme is fast in tissue slides scanning, enabling a high-throughput generation of digital pathology images. Experimental results demonstrate that our scheme achieves satisfactory refocusing performance.

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