A deep learning-based tool specifically for segmentation within volumetric images.

Wide applicability is established by testing performance on diverse biological datasets.

Robustness across various semantic and instance segmentation tasks is demonstrated.

We present the Volume Segmentation Tool (VST), a deep learning software tool that implements volumetric image segmentation in volume electron microscopy image stack data from a wide range of biological sample types. VST automates the handling of data preprocessing, data augmentation, and network building, as well as the configuration for model training, while adapting to the specific dataset. We have tried to make VST more accessible by designing it to operate entirely on local hardware and have provided a browser-based interface with additional features for visualizations of the networks and augmented datasets. VST can utilise contour map prediction to support instance segmentation on top of semantic segmentation. Through examples from various resin-embedded sample derived transmission electron microscopy and scanning electron microscopy datasets, we demonstrate that VST achieves state of the art performance compared to existing approaches.

Semantic segmentation

Instance segmentation

Machine learning

Software tools

Image analysis

Volume electron microscopy

All segmentation results discussed in this paper are available upon request.

© 2025 The Author(s). Published by Elsevier Inc.