Rapid Whole Slide Imaging via Learning-based Two-shot Virtual Autofocusing
This addresses the time-consuming bottleneck in digital pathology for medical professionals, though it is an incremental improvement over existing methods.
The paper tackles the slow autofocusing problem in whole slide imaging by proposing a learning-based virtual autofocusing method that uses only two out-of-focus images per tile, reducing image captures from up to 21 to 2, and achieves satisfactory refocusing performance.
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.