Review of Image Segmentation Techniques for Biomedical Micro-CT
This paper provides a comprehensive review of image segmentation techniques specifically tailored for biomedical micro-computed tomography (micro-CT) applications. It covers the spectrum from traditional laboratory-based absorption imaging to advanced synchrotron-based phase-contrast imaging. The review aims to bridge the gap between diverse imaging modalities and the critical post-processing step of image segmentation. Understanding and applying appropriate segmentation methods are crucial for accurate quantitative analysis of biological tissues and structures at the micro-scale. The authors discuss the evolution and current state-of-the-art in segmentation algorithms, highlighting their strengths and limitations in the context of biomedical imaging challenges. Specific attention is given to how different imaging physics, such as absorption versus phase contrast, influence the choice and performance of segmentation techniques. The review serves as a valuable resource for researchers and practitioners working with micro-CT data, offering insights into selecting and implementing effective segmentation strategies for various biomedical research questions. It underscores the importance of robust segmentation for reliable data interpretation and the advancement of micro-CT in medical diagnostics and research.
This review addresses the critical need for robust image segmentation in biomedical micro-CT, a field experiencing rapid technological advancement. As imaging modalities evolve from standard absorption contrast to more sensitive synchrotron phase-contrast techniques, segmentation algorithms must adapt to capture finer details and reduce artifacts. The paper's focus on bridging this gap highlights a systemic challenge: ensuring that analytical tools keep pace with imaging capabilities. Future developments will likely see increased integration of deep learning and AI-driven segmentation, potentially offering greater accuracy and automation. However, the interpretability and generalizability of these AI models across different micro-CT setups and biological samples will remain key areas for research and validation, ensuring that technological progress translates into reliable scientific discovery and clinical utility.
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