Robust Deep Learning-based 3D Segmentation and Morphological Analysis of Mitochondria using Soft X-ray Tomography

Arun Kumar Yadav , Aneesh Deshmukh , Pushkar Bharadwaj , Anuj Baliyan , Kate L. White , Jitin Singla ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.1101/2025.10.16.682919

Mitochondrial morphology is crucial for cellular function, but large-scale analysis is limited by challenges in high-resolution imaging and segmentation. MitoXRNet, a compact 3D deep-learning model, efficiently segments mitochondria and nuclei from Soft X-ray Tomography data using multi-axis slicing, Sobel-based boundary enhancement, and combined BCE–Robust Dice loss. With 1.4M parameters, it achieves a Dice score of 73.8% on INS-1E cells, outperforming existing models. Automated analysis indicated that glucose induced larger mitochondria and higher matrix density, and that GIP and GKA induced smaller and denser mitochondria, highlighting previously unreported β-cell mitochondrial remodeling. MitoXRNet allows for scalable profiling of organelle-level morpho-biophysical data. Highlights A data-efficient method for 3D segmentation of mitochondria and nucleus from Soft X-ray tomograms. Incorporates domain-specific Sobel filter-based preprocessing to improve segmentation accuracy and quality under imperfect or noisy labels. Enables rapid and automated analysis of mitochondrial morphology, facilitating quantitative assessment of pharmacological effects on cellular ultrastructure. Graphical Abstract