Objectives: Artificial dermal scaffold (ADS) has undergone rapid development and been increasingly used for treating skin wound in clinics due to its good biocompatibility, controllable degradation, and low risk of disease infection. To obtain good treatment efficacy, ADS needs to be monitored longitudinally during the treatment process. For example, scaffold-tissue fit, cell in-growth, vascular regeneration, and scaffold degradation are the key properties to be inspected. However, to date, there are no effective, real-time, and noninvasive techniques to meet the requirement of the scaffold monitoring above. Materials and Methods: In this study, we propose to use optical coherence tomography (OCT) to monitor ADS in vivo through three-dimensional imaging. A swept source OCT system with a handheld probe was developed for in vivo skin imaging. Moreover, a cell in-growth, vascular regeneration, and scaffold degradation rate (IRDR) was defined with the volume reduction rate of the scaffold's collagen sponge layer. To measure the IRDR, a semiautomatic image segmentation algorithm was designed based on U-Net to segment the collagen sponge layer of the scaffold from OCT images. Results: The results show that the scaffold-tissue fit can be clearly visualized under OCT imaging. The IRDR can be computed based on the volume of the segmented collagen sponge layer. It is observed that the IRDR appeared to a linear function of the time and in addition, the IRDR varied among different skin parts. Conclusion: Overall, it can be concluded that OCT has a good potential to monitor ADS in vivo. This can help guide the clinicians to control the treatment with ADS to improve the therapy.

Objective. Corneal nerve fiber (CNF) has been found to exhibit morphological changes associated with various diseases, which can therefore be utilized to aid in the early diagnosis of those diseases. CNF is usually visualized under corneal confocal microscopy (CCM) in clinic. To obtain the diagnostic biomarkers from CNF image produced from CCM, image processing and quantitative analysis are needed. Usually, CNF is segmented first and then CNF’s centerline is extracted, allowing for measuring geometrical and topological biomarkers of CNF, such as density, tortuosity, and length. Consequently, the accuracy of the segmentation and centerline extraction can make a big impact on the biomarker measurement. Thus, this study is aimed to improve the accuracy and universality of centerline extraction. Approach. We developed a new thinning algorithm based on neighborhood statistics, called neighborhood-statistics thinning (NST), to extract the centerline of CNF. Compared with traditional thinning and skeletonization techniques, NST exhibits a better capability to preserve the fine structure of CNF which can effectively benefit the biomarkers measurement above. Moreover, NST incorporates a fitting process, which can make centerline extraction be less influenced by image segmentation. Main results. This new method is evaluated on three datasets which are segmented with five different deep learning networks. The results show that NST is superior to thinning and skeletonization on all the CNF-segmented datasets with a precision rate above 0.82. Last, NST is attempted to be applied for the diagnosis of keratitis with the quantitative biomarkers measured from the extracted centerlines. Longer length and higher density but lower tortuosity were found on the CNF of keratitis patients as compared to healthy patients. Significance. This demonstrates that NST has a good potential to aid in the diagnostics of eye diseases in clinic.

Optical coherence tomography angiography (OCTA), as a functional extension of optical coherence tomography (OCT), has exhibited a great potential to aid in clinical diagnostics. Currently, OCTA still suffers from motion artifact and noise. Therefore, in this article, we propose to implement compressive sensing (CS) on B‐scans to reduce motion artifact by increasing B‐scan rate. Meanwhile, a noise reduction filter is specially designed by combining CS, Gaussian filter and median filter. Specially, CS filtering is realized by averaging multiple CS repetitions on en‐face OCTA images with varied sampling functions. The method is evaluated on in vivo OCTA images of human skin. The results show that vasculature structures can be reconstructed well through CS on B‐scans with a sampling rate of 70%. Moreover, the noise can be significantly eliminated by the developed filter. This implies that our method has a good potential to expedite OCTA imaging and improve the image quality.

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