SPROUT: A User-friendly, Scalable Toolkit for Multi-class Segmentation of Volumetric Images

Abstract

The segmentation of fine-grained and complex structures from volumetric data, such as 3D biomedical images, is a manually intensive process, with performance hindered by limited training data and the difficulty of adapting AI models for specialised datasets. Here, we introduce SPROUT, a user-friendly and interpretable segmentation framework that leverages domain-specific priors, enabling experts to translate their knowledge into reproducible, high-quality segmentations across diverse imaging modalities without the need for training data. Its adaptive design facilitates parameter transferability and improves generalisation across similar datasets. Implemented as scripts and a napari plugin, it supports interactive editing and scalable batch processing, lowering the technical barrier for domain experts. We applied SPROUT to datasets spanning different imaging modalities, anatomical complexities, postures, and target structures, producing high-quality segmentations across 2D and 3D tasks. Quantitative comparisons with other methods on a representative dataset showed SPROUT achieved results comparable to expert-corrected interpolation while requiring substantially less manual input. In scenarios where SPROUT achieved high-quality results, supervised models often struggled to reach similar accuracy, highlighting the challenge of deep learning methods in complex domains. We also explored integration with foundation models to accelerate segmentation in high-contrast datasets, illustrating potential for hybrid workflows.