SignificanceAdvances in label-free imaging have impacted many areas of biological and biomedical imaging ranging from cell biology and cancer to pathology and neuroscience. Despite the great progress and advantages of these methods, it is clear that to realize their full potential, validation by extrinsic labels and probes is critically needed.AimThis perspective calls for developing and applying innovative labels and probes to validate both existing and emerging label-free imaging methods.ApproachMajor representative types of label-free imaging methods are briefly presented discussing their advantages and differing contrasts. Their biological applications are also reviewed with a focus on how validation of label-free methods with carefully developed labeling approaches will greatly aid in further intrinsic contrast imaging adoption and likely lead to more sophisticated image-based biomarkers and a better understanding of the underlying signals.ConclusionsExpanded efforts in extrinsic label validation will significantly push forward the utilization and adoption of label-free methods both in basic research and clinical models.
Second-harmonic generation (SHG) imaging can help reveal interactions between collagen fibers and cancer cells. Quantitative analysis of SHG images of collagen fibers is challenged by the heterogeneity of collagen structures and low signal-to-noise ratio often found while imaging collagen in tissue. The role of collagen in breast cancer progression can be assessed post acquisition via enhanced computation. To facilitate this, we have implemented and evaluated four algorithms for extracting fiber information, such as number, length, and curvature, from a variety of SHG images of collagen in breast tissue. The image-processing algorithms included a Gaussian filter, SPIRAL-TV filter, Tubeness filter, and curvelet-denoising filter. Fibers are then extracted using an automated tracking algorithm called fiber extraction (FIRE). We evaluated the algorithm performance by comparing length, angle and position of the automatically extracted fibers with those of manually extracted fibers in twenty-five SHG images of breast cancer. We found that the curvelet-denoising filter followed by FIRE, a process we call CT-FIRE, outperforms the other algorithms under investigation. CT-FIRE was then successfully applied to track collagen fiber shape changes over time in an in vivo mouse model for breast cancer.
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