A weak-labelling and deep learning approach for in-focus object segmentation in 3D widefield microscopy.

Three-dimensional information is crucial to our understanding of biological phenomena. The vast majority of biological microscopy specimens are inherently three-dimensional. However, conventional light microscopy is largely geared towards 2D images, while 3D microscopy and image reconstruction remain feasible only with specialised equipment and techniques. Inspired by the working principles of one such technique-confocal microscopy, we propose a novel approach to 3D widefield microscopy reconstruction through semantic segmentation of in-focus and out-of-focus pixels. For this, we explore a number of rule-based algorithms commonly used for software-based autofocusing and apply them to a dataset of widefield focal stacks. We propose a computation scheme allowing the calculation of lateral focus score maps of the slices of each stack using these algorithms. Furthermore, we identify algorithms preferable for obtaining such maps. Finally, to ensure the practicality of our approach, we propose a surrogate model based on a deep neural network, capable of segmenting in-focus pixels from the out-of-focus background in a fast and reliable fashion. The deep-neural-network-based approach allows a major speedup for data processing making it usable for online data processing.