Martin Pfister

Aberrant angiogenesis is a hallmark of many pathologies. In cancer, tumor growth and metastasis strongly depend on angiogenesis triggered by neoplastic cells. Antiangiogenic therapies are approved to treat different kinds of cancer. However, the success of these treatments is so far limited as some patients do not respond at all and others develop resistances. Thus, a deeper understanding of the mechanisms driving tumor angiogenesis and the variations in tumor vessels is crucial. Optical coherence tomography angiography (OCTA) is a fast volumetric imaging technique that provides detailed insights into tumor vascularization and perfusion of vessels in a label‐free and non‐invasive manner. An ultra‐high resolution OCTA and confocal fluorescence imaging pipeline are developed to analyze tumor vascularization and blood perfusion in vivo, using a zebrafish cancer model. OCTA imaging operating at 800 nm is optimized to show slow blood flow allowing to compare the functionality of blood vessels in healthy and tumor‐bearing zebrafish. Furthermore, effects of small compounds on tumor vascularization can be investigated with our setup. The key outcomes include a qualitative assessment of vascularization and blood vessel perfusion, along with a quantitative analysis of vessel structure, to evaluate how effective the drugs were at different concentrations.

During wound healing, the rapid re-establishment of a functional microcirculation in the wounded tissue is of utmost importance. We applied optical coherence tomography (OCT) angiography to evaluate vascular remodeling in an excisional wound model in the pinnae of C57BL/6 and db/db mice receiving different proangiogenic topical treatments. Analysis of the high-resolution OCT angiograms, including the four quantitative parameters vessel density, vessel length, number of bifurcations, and vessel tortuosity, revealed changes of the microvasculature and allowed identification of the overlapping wound healing phases hemostasis, inflammation, proliferation, and remodeling. Angiograms acquired in the inflammatory phase in the first days showed a dilation of vessels and recruitment of pre-existing capillaries. In the proliferative phase, angiogenesis with the sprouting of new capillaries into the wound tissue led to an increase of the OCT angiography parameters vessel density, normalized vessel length, number of bifurcations, and vessel tortuosity by 28–47%, 39–52%, 33–48%, and 3–8% versus baseline, respectively. After the peak observed on study days four to seven, the parameters slowly decreased but remained still elevated 18 days after wounding, indicating a continuing remodeling phase. Our study suggests that OCT angiography has the potential to serve as a valuable preclinical research tool in studies investigating impaired vascular remodeling during wound healing and potential new treatment strategies.

We trained a deep learning algorithm to use skin optical coherence tomography (OCT) angiograms to differentiate between healthy and type 2 diabetic mice. OCT angiograms were acquired with a custom‐built OCT system based on an akinetic swept laser at 1322 nm with a lateral resolution of ∼13 μm and using split‐spectrum amplitude decorrelation. Our data set consisted of 24 stitched angiograms of the full ear, with a size of approximately 8.2 × 8.2 mm, evenly distributed between healthy and diabetic mice. The deep learning classification algorithm uses the ResNet v2 convolutional neural network architecture and was trained on small patches extracted from the full ear angiograms. For individual patches, we obtained a cross‐validated accuracy of 0.925 and an area under the receiver operating characteristic curve (ROC AUC) of 0.974. Averaging over multiple patches extracted from each ear resulted in the correct classification of all 24 ears.

We present vessel density alterations in response to flicker stimulation using optical coherence tomography angiography and identified the superficial capillary plexus as the layer with the most pronounced effect. This points out the physiological importance of the microvasculature in mediating functional hyperemia and suggests a fine-tuned plexus-specific mechanism to meet cellular metabolic demands.

Clinical evaluation of skin lesions requires precise and reproducible technologies for their qualitative and quantitative assessment. In this study, we investigate the applicability of a custom-built dermatologic OCT system for longitudinal assessment of intradermal volumes in a mouse model. The OCT, based on an akinetic swept laser working at 1310 nm was employed for visualization and quantification of intradermal deposits of three different hyaluronic acid-based hydrogel formulations – one commercial and two test substances. Hydrogels were applied in 22 BALB/c mice, and measurements were performed over a six-month time period. All hydrogels increased in volume within the first weeks and degraded steadily thereafter. The half-lifes of the test hydrogels (27.2 ± 13.6 weeks for Hydrogel 1, 31.5 ± 17.2 weeks for Hydrogel 2) were higher in comparison to the commercially available HA hydrogel (21.4 ± 12.0 weeks), although differences were not significant. The sphericity parameter was used for evaluation of the deposit geometry. While on the injection day the sphericities were similar (~0.75 ± 0.04), at later time points significant differences between the different test substances were found (T24: PRV 0.59 ± 0.09, Hydrogel 1 0.70 ± 0.11, Hydrogel 2 0.78 ± 0.07; p ≤ 0.012 for all pairs). This study shows the applicability of OCT imaging for quantitative assessment of the volumetric behavior of intradermal deposits in vivo.

The tear meniscus contains most of the tear fluid and therefore is a good indicator for the state of the tear film. Previously, we used a custom-built optical coherence tomography (OCT) system to study the lower tear meniscus by automatically segmenting the image data with a thresholding-based segmentation algorithm (TBSA). In this report, we investigate whether the results of this image segmentation algorithm are suitable to train a neural network in order to obtain similar or better segmentation results with shorter processing times. Considering the class imbalance problem, we compare two approaches, one directly segmenting the tear meniscus (DSA), the other first localizing the region of interest and then segmenting within the higher resolution image section (LSA). A total of 6658 images labeled by the TBSA were used to train deep convolutional neural networks with supervised learning. Five-fold cross-validation reveals a sensitivity of 96.36% and 96.43%, a specificity of 99.98% and 99.86% and a Jaccard index of 93.24% and 93.16% for the DSA and LSA, respectively. Average segmentation times are up to 228 times faster than the TBSA. Additionally, we report the behavior of the DSA and LSA in cases challenging for the TBSA and further test the applicability to measurements acquired with a commercially available OCT system. The application of deep learning for the segmentation of the tear meniscus provides a powerful tool for the assessment of the tear film, supporting studies for the investigation of the pathophysiology of dry eye-related diseases.

Using optical coherence tomography angiography images from mouse ears, a convolutional neural network successfully recognized diabetes with an accuracy of 0.81 and an area under the curve of 0.90.

Optical coherence tomography (OCT) and high-frequency ultrasound (HFUS), two established imaging modalities in the field of dermatology, were evaluated and compared regarding their applicability for visualization of skin tissue morphology and quantification of murine intradermal structures. The accuracy and reproducibility of both methods were assessed ex vivo and in vivo using a standardized model for intradermal volumes based on injected soft tissue fillers. OCT revealed greater detail in skin morphology, allowing for detection of single layers due to the superior resolution. Volumetric data measured by OCT (7.9 ± 0.3 μl) and HFUS (7.7 ± 0.5 μl) were in good agreement and revealed a high accuracy when compared to the injected volume of 7.98 ± 0.8 µl. In vivo, OCT provided a higher precision (relative SD: 26% OCT vs. 42% HFUS) for the quantification of intradermal structures, whereas HFUS offered increased penetration depth enabling the visualization of deeper structures. A combination of both imaging technologies might be valuable for tumor assessments or other dermal pathologies in clinical settings.

A custom-built ultrahigh-resolution optical coherence tomography (UHR-OCT) system and fluorescein staining were employed for investigation of a scopolamine induced dry eye mouse model. Acquired data was used to evaluate common and complementary findings of the two modalities. Central corneal thickness as measured by UHR-OCT increased significantly over the study period of 24 hours, from 89.0 ± 3.57 µm to 92.2 ± 4.07 µm. Both techniques were able to show corneal lesions with a large range of severity. Localized fluorescein staining was detected in 5% and diffuse staining in 45% of cases where no epithelial damage was visible with OCT. However, OCT revealed stromal defects in 6% and endothelial defects in 18% of the cases, which could not be visualized via fluorescein staining. Thus, while fluorescein staining widely detected defects of the corneal surface in a mouse model of dry eye disease, OCT non-invasively revealed additional information about defect depth and involvement of particular layers.

We present a system for automatic determination of the intradermal volume of hydrogels based on optical coherence tomography (OCT) and deep learning. Volumetric image data was acquired using a custom-built OCT prototype that employs an akinetic swept laser at ~1310 nm with a bandwidth of 87 nm, providing an axial resolution of ~6.5 μm in tissue. Three-dimensional data sets of a 10×10 mm skin patch comprising the intradermal filler and the surrounding tissue were acquired. A convolutional neural network using a u-net-like architecture was trained from slices of 100 OCT volume data sets where the dermal filler volume was manually annotated. Using six-fold cross-validation, a mean accuracy of 0.9938 and a Jaccard similarity coefficient of 0.879 were achieved.