Robust mitochondria segmentation and morphological profiling using soft X-ray tomography.
Authors
Affiliations (7)
Affiliations (7)
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Roorkee, Roorkee, Uttarakhand, India.
- Department of Biosciences and Bioengineering, Indian Institute of Technology (IIT) Roorkee, Roorkee, Uttarakhand, India.
- Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA, USA.
- Department of Earthquake Engineering, Indian Institute of Technology (IIT) Roorkee, Roorkee, Uttarakhand, India.
- Department of Electronics and Communication Engineering, Indian Institute of Technology (IIT) Roorkee, Roorkee, Uttarakhand, India.
- Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA, USA; Department of Quantitative Computational Biology, University of Southern California, Los Angeles, CA, USA.
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Roorkee, Roorkee, Uttarakhand, India; Department of Biosciences and Bioengineering, Indian Institute of Technology (IIT) Roorkee, Roorkee, Uttarakhand, India. Electronic address: [email protected].
Abstract
Mitochondrial morphology is central to cellular function, yet large-scale quantification is limited by the lack of high-resolution whole-cell imaging and efficient segmentation tools. Soft X-ray tomography (SXT) provides native-state 3D whole-cells images, but organelle segmentation remains a bottleneck. We present MitoXRNet, a data- and parameter-efficient 3D deep learning model for mitochondria and nucleus segmentation in SXT tomograms. Using multi-axis 3D slicing, Sobel filter-based boundary enhancement, and a combined Binary-Cross-Entropy and Robust-Dice loss, MitoXRNet achieves a 73.8% Dice score on INS-1E cells with only 1.4 M parameters, outperforming existing methods. A larger 22.6 M variant generalized well to unseen data. Automated segmentation enabled quantitative analysis of mitochondrial remodeling under metabolic stimuli: glucose increased mitochondrial volume and matrix density, while GIP and GKA increased mitochondria number, reduced volume, and elevated density, indicating smaller, denser, more dynamic populations. MitoXRNet provides a scalable framework for high-throughput morphological and biophysical profiling of organelles in native-state SXT data.