Nuclei segmentation of fluorescence microscopy images using convolutional neural networks

Fluorescence microscopy has emerged as a powerful tool for studying cell biology because it enables the acquisition of 3D image volumes deeper into tissue and the imaging of complex subcellular structures. Quantitative analysis of these structures, which is needed to characterize the structure and constitution of tissue volumes, is facilitated by nuclei segmentation. However, manual segmentation is a laborious and intractable process due to the size and complexity of the data. In this paper, we describe a nuclei segmentation method using a deep convolutional neural network, data augmentation to generate training images of different shapes and contrasts, a refinement process combining segmentation results of horizontal, frontal, and sagittal planes in a volume, and a watershed technique to count the number of nuclei. Our results indicate that compared to 3D ground truth data, our method is able to successfully segment and count 3D nuclei.

1. Introduction

Fluorescence microscopy is an optical microscopy technique that has enabled the visualization of subcellular structures of in vivo tissue or cells [1]. Two-photon microscopy with near-infrared excitation light can now provide the ability to image deeper into tissue [2], [3]. This has resulted in acquisition of large data volumes that increasingly require automated image analysis techniques for quantification studies. In particular, automatic image segmentation techniques are now required for the analysis of biological structures produced by fluorescence microscony.