Purpose: Fluorescence microscopy visualizes three-dimensional subcellular structures in tissue with two-photon microscopy achieving deeper penetration into tissue. Nuclei detection, which is essential for analyzing tissue for clinical and research purposes, remains a challenging problem due to the spatial variability of nuclei. Recent advancements in deep learning techniques have enabled the analysis of fluorescence microscopy data to localize and segment nuclei. However, these localization or segmentation techniques would require additional steps to extract characteristics of nuclei. We develop a 3D convolutional neural network, called Sphere Estimation Network (SphEsNet), to extract characteristics of nuclei without any postprocessing steps.
Approach: To simultaneously estimate the center locations of nuclei and their sizes, SphEsNet is composed of two branches to localize nuclei center coordinates and to estimate their radii. Synthetic microscopy volumes automatically generated using a spatially constrained cycle-consistent adversarial network are used for training the network because manually generating 3D real ground truth volumes would be extremely tedious.
Results: Three SphEsNet models based on the size of nuclei were trained and tested on five real fluorescence microscopy data sets from rat kidney and mouse intestine. Our method can successfully detect nuclei in multiple locations with various sizes. In addition, our method was compared with other techniques and outperformed them based on object-level precision, recall, and F1 score. Our model achieved 89.90% for F1 score.
Conclusions: SphEsNet can simultaneously localize nuclei and estimate their size without additional steps. SphEsNet can be potentially used to extract more information from nuclei in fluorescence microscopy images.
Fluorescence microscopy is used to image multiple subcellular structures in living cells which are not readily
observed using conventional optical microscopy. Moreover, two-photon microscopy is widely used to image
structures deeper in tissue. Recent advancement in fluorescence microscopy has enabled the generation of large
data sets of images at different depths, times, and spectral channels. Thus, automatic object segmentation is
necessary since manual segmentation would be inefficient and biased. However, automatic segmentation is still
a challenging problem as regions of interest may not have well defined boundaries as well as non-uniform pixel
intensities. This paper describes a method for segmenting tubular structures in fluorescence microscopy images
of rat kidney and liver samples using adaptive histogram equalization, foreground/background segmentation,
steerable filters to capture directional tendencies, and connected-component analysis. The results from several
data sets demonstrate that our method can segment tubular boundaries successfully. Moreover, our method has
better performance when compared to other popular image segmentation methods when using ground truth data
obtained via manual segmentation.