Abstract 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 g…