SXTractor: A Self-Supervised Feature Extractor of Soft X-Ray Images That Enables Few-Shot Tomogram Segmentation

Abstract

Soft X-ray tomography (SXT) is a powerful, non-invasive bio-imaging technique that enables visualization of cellular structures in near-native states. Despite its potential, the development of dedicated image analysis tools -- particularly deep-learning-based models -- has been limited, largely due to the limited accessibility of soft X-ray microscopes and the scarcity of labeled SXT data. To address this deficit, in this work, we present SXTractor, a self-supervised SXT feature extractor based on the DINO framework. SXTractor can be fine-tuned with minimal labeled data and effectively adapted to various downstream tasks. We demonstrate its utility on few-shot tomogram segmentation, where it significantly outperforms the model when trained from scratch. Furthermore, it achieves few-shot segmentation performance comparable to that of the Segment Anything Model (SAM), despite SAM being a segmentation-specific model pretrained on millions of labeled images with a significantly larger model size. Most importantly, SXTractor enables a diverse range of downstream applications of deep learning to SXT, thus offering a practical and scalable solution for SXT image analysis in data-constrained settings.