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

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月30日

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  • Hao Song,
  • Ning Zhu

摘要

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.