Review of image segmentation techniques for biomedical micro-CT: from laboratory absorption imaging to synchrotron phase-contrast

X-ray micro-computed tomography ( μ CT) is widely used in biomedical research for non-destructive, high-resolution imaging. Synchrotron Radiation Phase-Contrast μ CT (SR-PCI- μ CT) further enhances image quality with higher Signal to Noise Ratio (SNR), improved contrast, and faster acquisition. However, segmenting SR-PCI- μ CT images remains challenging due to their heterogeneous image property and limited training data. Additionally, the increasing throughput of synchrotron facilities demands robust, efficient segmentation methods. This paper reviews a list of recent segmentation approaches in biomedical μ CT. Traditional methods remain simple and effective for simple, high-contrast structures but require extensive tuning and generalize poorly to complex, low-contrast soft tissues. Data-driven models provide higher accuracy and robustness yet rely heavily on large expert-annotated datasets, limiting reproducibility and cross-dataset adaptability. Recent advance on vision transformers have shifted the paradigm from task-specified to more domain-specified segmentation, though these techniques are still evolving and require adaptation for SR-PCI- μ CT studies. This survey provides the first bi-modality review covering both laboratory and synchrotron biomedical μ CT segmentation. It consolidates recent segmentation methodologies, identifies major trends in deep-learning techniques, and highlights current limitations across SR-PCI- μ CT. Additionally, it outlines open challenges to guide future research and practical advancements in biomedical μ CT segmentation.