2dSpAn-Auto: an automated tool for analysis of two-dimensional dendritic spine images.

Background: Quantitative analysis of dendritic spine morphology and density is crucial for understanding synaptic plasticity and its role in neuropsychiatric disorders, including Alzheimer's disease and schizophrenia. While both 3D and 2D approaches exist for spine analysis, 2D methods offer advantages in computational efficiency, rapid assessment, and more reasonable to use in case of limited z-resolution images acquired through confocal and previous generation super-resolution microscopy. In this work, we developed a modality-agnostic spine segmentation approach based on 2D skeletonization. Specifically, we implemented two analytical workflows, viz., 2dSpAn-Auto.b, that implements binary skeletonization alogrithm and 2dSpAn-Auto.f, that generates fuzzy skeletons directly from gray-scale images. Our developed method enables fast and automatic segmentation and morphological analysis of 2D maximum intensity projection images of dendritic spines. Expert users can fine-tune parameters when needed, though default settings prove robust across various imaging conditions. The developed 2dSpAn-Auto software tool is most suitable for automated batch processing while maintaining user flexibility through an intuitive graphical interface.

Results: 2dSpAn-Auto is validated across multiple imaging modalities (in vitro, ex vivo, and in vivo) for automatic assessment of dendritic spine parameters including spine density, morphometry (spine area, spine length, head width, minimum and average neck width), and total dendrite length. Validation studies demonstrate high accuracy and reproducibility across varying imaging protocols and experimental conditions. Multiple images from similar experimental setups can be processed seamlessly in the batch mode.

Conclusions: 2dSpAn-Auto provides a robust, interpretable solution for fast analysis of dendritic spines, a critical need in neurological research and clinical assessment. The combination of automated processing with optional expert oversight makes it suitable for both routine analysis and specialized research applications. The software, complete with the source code and comprehensive documentation, is available to the research community for non-commercial use under GNU General Public License (GPL) v3.