In scars arising from burns, objective assessment of vascularity is important in the early identification of pathological scarring, and in the assessment of progression and treatment response. We demonstrate the first clinical assessment and automated quantification of vascularity in cutaneous burn scars of human patients in vivo that uses optical coherence tomography (OCT). Scar microvasculature was delineated in three-dimensional OCT images using speckle decorrelation. The diameter and area density of blood vessels were automatically quantified. A substantial increase was observed in the measured density of vasculature in hypertrophic scar tissues (38%) when compared against normal, unscarred skin (22%). A proliferation of larger vessels ($diameter≥100μm$) was revealed in hypertrophic scarring, which was absent from normal scars and normal skin over the investigated physical depth range of 600 μm. This study establishes the feasibility of this methodology as a means of clinical monitoring of scar progression.

The formation of burn-scar tissue in human skin profoundly alters, among other things, the structure of the dermis. We present a method to characterize dermal scar tissue by the measurement of the near-infrared attenuation coefficient using optical coherence tomography (OCT). To generate accurate en face parametric images of attenuation, we found it critical to first identify (using speckle decorrelation) and mask the tissue vasculature from the three-dimensional OCT data. The resulting attenuation coefficients in the vasculature-masked regions of the dermis of human burn-scar patients are lower in hypertrophic ($3.8±0.4mm−1$) and normotrophic ($4.2±0.9mm−1$) scars than in contralateral or adjacent normal skin ($6.3±0.5mm−1$). Our results suggest that the attenuation coefficient of vasculature-masked tissue could be used as an objective means to assess human burn scars.

Intravascular optical coherence tomography (OCT) is an optical imaging modality commonly used in the assessment of coronary artery diseases during percutaneous coronary intervention. Manual segmentation to assess luminal stenosis from OCT pullback scans is challenging and time consuming. We propose a linear-regression convolutional neural network to automatically perform vessel lumen segmentation, parameterized in terms of radial distances from the catheter centroid in polar space. Benchmarked against gold-standard manual segmentation, our proposed algorithm achieves average locational accuracy of the vessel wall of 22 microns, and 0.985 and 0.970 in Dice coefficient and Jaccard similarity index, respectively. The average absolute error of luminal area estimation is 1.38%. The processing rate is 40.6 ms per image, suggesting the potential to be incorporated into a clinical workflow and to provide quantitative assessment of vessel lumen in an intraoperative time frame.

We demonstrate the in vivo assessment of human scars by parametric imaging of birefringence using polarization-sensitive optical coherence tomography (PS-OCT). Such in vivo assessment is subject to artifacts in the detected birefringence caused by scattering from blood vessels. To reduce these artifacts, we preprocessed the PS-OCT data using a vascular masking technique. The birefringence of the remaining tissue regions was then automatically quantified. Results from the scars and contralateral or adjacent normal skin of 13 patients show a correspondence of birefringence with scar type: the ratio of birefringence of hypertrophic scars to corresponding normal skin is $2.2±0.2$ ($mean±standard deviation$), while the ratio of birefringence of normotrophic scars to normal skin is $1.1±0.4$. This method represents a new clinically applicable means for objective, quantitative human scar assessment.