MOTIVATION: Morphological analysis of dendritic spines is critical to understanding the function and dysfunction of neural circuits. The growing trends of the large-scale electron microscopy (EM) imaging systems and automatic cellular reconstruction provide unprecedented opportunities to investigate the ultrastructure of dendrites. This morphometric analysis of dendritic spines requires accurate compartment segmentation methods as well as meaningful quantification methods. However, most existing methods rely on surface or volumetric information alone, which may not deliver accurate segmentation results. RESULTS: We developed VSOT, a method based on Volume-Surface Optimization, designed for the accurate structural analysis of dendritic reconstruction. VSOT accurately segments dendritic reconstructions into compartments, including spine, spine head, and spine neck, by leveraging advanced optimization techniques that integrate local surface and global volumetric information. Our tests on public datasets of spine segmentation, as well as on a first-of-its-kind dataset of head-neck segmentation that we manually constructed, show that VSOT offers more accurate results than peer methods. When applied to a large EM dataset of different brain layers, VSOT reveals how the structure of dendrites varies across brain areas. Furthermore, we explored the structural relationships between neurons and astrocytes at tripartite synapses. With the newly developed computation methods, neuroscientists can exploit the large-scale volumetric EM data to address various scientific questions and advance the understanding of neural circuits. AVAILABILITY AND IMPLEMENTATION: VSOT is available at https://github.com/yu-lab-vt/VSOT. The data and codes in this study are available at Zenodo (https://doi.org/10.5281/zenodo.15115542).

3D segmentation of nuclei images is a fundamental task for many biological studies. Despite the rapid advances of large-volume 3D imaging acquisition methods and the emergence of sophisticated algorithms to segment the nuclei in recent years, a benchmark with all cells completely annotated is still missing, making it hard to accurately assess and further improve the performance of the algorithms. The existing nuclei segmentation benchmarks either worked on 2D only or annotated a small number of 3D cells, perhaps due to the high cost of 3D annotation for large-scale data. To fulfill the critical need, we constructed NIS3D, a 3D, high cell density, large-volume, and completely annotated Nuclei Image Segmentation benchmark, assisted by our newly designed semi-automatic annotation software. NIS3D provides more than 22,000 cells across multiple most-used species in this area. Each cell is labeled by three independent annotators, so we can measure the variability of each annotation. A confidence score is computed for each cell, allowing more nuanced testing and performance comparison. A comprehensive review on the methods of segmenting 3D dense nuclei was conducted. The benchmark was used to evaluate the performance of several selected state-of-the-art segmentation algorithms. The best of current methods is still far away from human-level accuracy, corroborating the necessity of generating such a benchmark. The testing results also demonstrated the strength and weakness of each method and pointed out the directions of further methodological development. The dataset can be downloaded here: https://github.com/yu-lab-vt/NIS3D.

The variance-stabilizing transformation (VST) problem is to transform heteroscedastic data to homoscedastic data so that they are more tractable for subsequent analysis. However, most of the existing approaches focus on finding an analytical solution for a certain parametric distribution, which severely limits the applications, because simple distributions cannot faithfully describe the real data while more complicated distributions cannot be analytically solved. In this paper, we converted the VST problem into a convex optimization problem, which can always be efficiently solved, identified the specific structure of the convex problem, which further improved the efficiency of the proposed algorithm, and showed that any finite discrete distributions and the discretized version of any continuous distributions from real data can be variance-stabilized in an easy and nonparametric way. We demonstrated the new approach on bioimaging data and achieved superior performance compared to peer algorithms in terms of not only the variance homoscedasticity but also the impact on subsequent analysis such as denoising. Source codes are available at https://github.com/yu-lab-vt/ConvexVST.

MOTIVATION: Synapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synaptic punctum imaging data, several issues are impeding satisfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness of different neurites and synaptic puncta is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio due to constraints of experiment facilities and availability of sensitive antibodies. These issues make the detection of synapses challenging and necessitates developing a new tool to easily and accurately quantify synapses. RESULTS: We present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. SynQuant is unsupervised, works for both 2D and 3D data, and can handle multiple staining channels. Through extensive experiments on one synthetic and three real datasets with ground truth annotation or manually labeling, SynQuant was demonstrated to outperform peer specialized unsupervised synapse detection tools as well as generic spot detection methods. AVAILABILITY AND IMPLEMENTATION: Java source code, Fiji plug-in, and test data are available at https://github.com/yu-lab-vt/SynQuant. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.