Review of image segmentation techniques for biomedical micro-CT: from laboratory absorption imaging to synchrotron phase-contrast
TL;DR
This survey provides the first bi-modality review covering both laboratory and synchrotron biomedical μCT segmentation, consolidates recent segmentation methodologies, identifies major trends in deep-learning techniques, and highlights current limitations across SR-PCI-μCT.
Abstract
X-ray micro-computed tomography (μCT) is widely used in biomedical research for non-destructive, high-resolution imaging. Synchrotron Radiation Phase-Contrast μCT (SR-PCI-μCT) further enhances image quality with higher Signal to Noise Ratio (SNR), improved contrast, and faster acquisition. However, segmenting SR-PCI-μCT 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 μCT. 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-μCT studies. This survey provides the first bi-modality review covering both laboratory and synchrotron biomedical μCT segmentation. It consolidates recent segmentation methodologies, identifies major trends in deep-learning techniques, and highlights current limitations across SR-PCI-μCT. Additionally, it outlines open challenges to guide future research and practical advancements in biomedical μCT segmentation.