Multi-Scale Deep Neural Network Microscopic Image Segmentation

Abstract

With digital technology and fast imaging speed, huge amount of microscopic images are produced in biomedical research every day. A key procedure in processing and analyzing the images involves recognition and segmentation of features, patterns, or regions that are of interest to the researchers. Such work is often manually done by humans, which is very time-consuming and thus impractical when image data are massive. Traditional image segmentation algorithms have difficulty in identifying high-level features (e.g. a particular morphology of a cell type or an organelle). Plus, many microscopic images, especially super-resolution STED images, are highly noisy. The analysis of noisy images poses an even higher challenge to traditional algorithms. Compared with computer algorithms, humans are incredibly good at identifying high-level features. Machine learning, which let computer learn human behaviors, is thus a suitable solution. Deep learning is a set of emerging machine learning methods that is being successfully applied in many applications, such as face recognition and speech recognition. It has also been used to process specific types of microscopic images.We have designed a multi-scale convolution neural network for segmentizing noisy microscopic images. Images at original resolution and down-sampled images at multiple scales are feed to the network for information extraction. The purpose of adopting this multi-scale architecture is to obtain local fine resolution while maintain larger field of views, which gives network low classification error rate while maintain reasonable processing speed. The network has been tested on segmentation of mitochondria labeled with mitotracker. Network separate image pixels into two categories: “background” and “mitochondria”. Training and testing of the network is done with standard back-propagation algorithm, with human labeled segmentation data. Preliminary result shows the trained network well outperformed multiple traditional image segmentation methods.