High-resolution full-field optical coherence tomography microscope for the evaluation of freshly excised skin specimens during Mohs surgery: A feasibility study.

Histopathology for tumor margin assessment is time-consuming and expensive. High-resolution full-field optical coherence tomography (FF-OCT) images fresh tissues rapidly at cellular resolution and potentially facilitates evaluation. Here, we define FF-OCT features of normal and neoplastic skin lesions in fresh ex vivo tissues and assess its diagnostic accuracy for malignancies. For this, normal and neoplastic tissues were obtained from Mohs surgery, imaged using FF-OCT, and their features were described. Two expert OCT readers conducted a blinded analysis to evaluate their diagnostic accuracies, using histopathology as the ground truth. A convolutional neural network was built to distinguish and outline normal structures and tumors. Of the 113 tissues imaged, 95 (84%) had a tumor (75 basal cell carcinomas [BCCs] and 17 squamous cell carcinomas [SCCs]). The average reader diagnostic accuracy was 88.1%, with a sensitivity of 93.7%, and a specificity of 58.3%. The artificial intelligence (AI) model achieved a diagnostic accuracy of 87.6 ± 5.9%, sensitivity of 93.2 ± 2.1%, and specificity of 81.2 ± 9.2%. A mean intersection-over-union of 60.3 ± 10.1% was achieved when delineating the nodular BCC from normal structures. Limitation of the study was the small sample size for all tumors, especially SCCs. However, based on our preliminary results, we envision FF-OCT to rapidly image fresh tissues, facilitating surgical margin assessment. AI algorithms can aid in automated tumor detection, enabling widespread adoption of this technique.

Training With Uncertain Annotations for Semantic Segmentation of Basal Cell Carcinoma From Full-Field OCT Images.

Semantic segmentation of basal cell carcinoma (BCC) from full-field optical coherence tomography (FF-OCT) images of human skin has received considerable attention in medical imaging. However, it is challenging for dermatopathologists to annotate the training data due to OCT's lack of color specificity. Very often, they are uncertain about the correctness of the annotations they made. In practice, annotations fraught with uncertainty profoundly impact the effectiveness of model training and hence the performance of BCC segmentation. To address this issue, we propose an approach to model training with uncertain annotations. The proposed approach includes a data selection strategy to mitigate the uncertainty of training data, a class expansion to consider sebaceous gland and hair follicle as additional classes to enhance the performance of BCC segmentation, and a self-supervised pre-training procedure to improve the initial weights of the segmentation model parameters. Furthermore, we develop three post-processing techniques to reduce the impact of speckle noise and image discontinuities on BCC segmentation. The mean Dice score of BCC of our model reaches 0.503±0.003, which, to the best of our knowledge, is the best performance to date for semantic segmentation of BCC from FF-OCT images.

Toward cell nuclei precision between OCT and H&E images translation using signal-to-noise ratio cycle-consistency.

Medical image-to-image translation is often difficult and of limited effectiveness due to the differences in image acquisition mechanisms and the diverse structure of biological tissues. This work presents an unpaired image translation model between in-vivo optical coherence tomography (OCT) and ex-vivo Hematoxylin and eosin (H&E) stained images without the need for image stacking, registration, post-processing, and annotation. The model can generate high-quality and highly accurate virtual medical images, and is robust and bidirectional. Our framework introduces random noise to (1) blur redundant features, (2) defend against self-adversarial attacks, (3) stabilize inverse conversion, and (4) mitigate the impact of OCT speckles. We also demonstrate that our model can be pre-trained and then fine-tuned using images from different OCT systems in just a few epochs. Qualitative and quantitative comparisons with traditional image-to-image translation models show the robustness of our proposed signal-to-noise ratio (SNR) cycle-consistency method.

Broadband wavelength-swept Cr4+:YAG crystal fiber laser.

We present a broadband wavelength-swept laser using a 16-µm-core-diameter Cr4+:YAG crystal fiber as the gain medium. The laser-diode-pumped crystal fiber laser has a threshold of only 102 mW due to the low propagation loss and high heat dissipation efficiency. The laser achieves a sweeping wavelength range of 134 nm, centered around 1425 nm, with a scanning speed of 163 k nm/s. Notably, the cross-polarization-coupled excited state absorption of the signal wavelength constrained the long-wavelength lasing limit. This laser has the potential for swept source optical coherence tomography applications, providing an axial resolution of 11.4 µm.

Integration of cellular-resolution optical coherence tomography and Raman spectroscopy for discrimination of skin cancer cells with machine learning.

Significance: An integrated cellular-resolution optical coherence tomography (OCT) module with near-infrared Raman spectroscopy was developed on the discrimination of various skin cancer cells and normal cells. Micron-level three-dimensional (3D) spatial resolution and the spectroscopic capability on chemical component determination can be obtained simultaneously. Aim: We experimentally verified the effectiveness of morphology, intensity, and spectroscopy features for discriminating skin cells. Approach: Both spatial and spectroscopic features were employed for the discrimination of five types of skin cells, including keratinocytes (HaCaT), the cell line of squamous cell carcinoma (A431), the cell line of basal cell carcinoma (BCC-1/KMC), primary melanocytes, and the cell line of melanoma (A375). The cell volume, compactness, surface roughness, average intensity, and internal intensity standard deviation were extracted from the 3D OCT images. After removing the fluorescence components from the acquired Raman spectra, the entire spectra (600 to 2100 cm-1) were used. Results: An accuracy of 85% in classifying five types of skin cells was achieved. The cellular-resolution OCT images effectively differentiate cancer and normal cells, whereas Raman spectroscopy can distinguish the cancer cells with nearly 100% accuracy. Conclusions: Among the OCT image features, cell surface roughness, internal average intensity, and standard deviation of internal intensity distribution effectively differentiate the cancerous and normal cells. The three features also worked well in sorting the keratinocyte and melanocyte. Using the full Raman spectra, the melanoma and keratinocyte-based cell carcinoma cancer cells can be discriminated effectively.

Ultra-broadband wavelength-swept Ti:sapphire crystal fiber laser.

An ultra-broadband wavelength-swept laser (WSL) was generated using glass-clad Ti:sapphire crystal fiber as the gain media. Due to the low signal propagation loss of the crystal fiber, the swept laser has a tuning bandwidth of 250 nm (i.e., 683 nm to 933 nm) at a repetition rate of 1200 Hz. The steady-state and pulsed dynamics of the WSL were analyzed. The 0.018-nm instantaneous linewidth corresponds to a 3-dB coherence roll-off of 7 mm. When using the laser for swept-source optical coherence tomography, an estimated axial resolution of 1.8 µm can be achieved.

Identification of Sex and Age from Macular Optical Coherence Tomography and Feature Analysis Using Deep Learning.

PURPOSE: To develop deep learning models for identification of sex and age from macular optical coherence tomography (OCT) and to analyze the features for differentiation of sex and age. DESIGN: Algorithm development using database of macular OCT. METHODS: We reviewed 6147 sets of macular OCT images from the healthy eyes of 3134 individuals from a single eye center in Taiwan. Deep learning-based algorithms were used to develop models for the identification of sex and age, and 10-fold cross-validation was applied. Gradient-weighted class activation mapping was used for feature analysis. RESULTS: The accuracy for sex prediction using deep learning from macular OCT was 85.6% ± 2.1% compared with accuracy of 61.9% using macular thickness and 61.4% ± 4.0% using deep learning from infrared fundus photography (P < .001 for both). The mean absolute error for age prediction using deep learning from macular OCT was 5.78 ± 0.29 years. A thorough analysis of the prediction accuracy and the gradient-weighted class activation mapping showed that the cross-sectional foveal contour lead to a better sex distinction than macular thickness or fundus photography, and the age-related characteristics of macula were on the whole layers of retina rather than the choroid. CONCLUSIONS: Sex and age could be identified from macular OCT using deep learning with good accuracy. The main sexual difference of macula lies in the foveal contour, and the whole layers of retina differ with aging. These novel findings provide useful information for further investigation in the pathogenesis of sex- and age-related macular structural diseases.

Depth-dependent in vivo human skin backscattering spectra extraction from full-field optical coherence tomography.

With homemade active crystalline fibers, we generated bright and broadband light sources for full-field optical coherence tomography, offering deep penetration into skin tissues with cellular resolution at a high frame rate. Extraction of backscattered spectra from the tissue has potential applications in biomedicine. The hysteresis nonlinearity of the piezoelectric transducer actuating the Mirau interferometer has been greatly reduced by a feedforward compensation approach. The linearized hysteresis response enables us to extract depth-dependent spectra accurately. To validate, the complex dispersion of a fused silica plate was characterized with 2% error. Further validation on an in vitro setting, the backscattered spectra from indocyanine green pigment and nonpigmented microspheres were obtained and verified. For in vivo skin measurement, the backscattered spectra show depth-dependent spectral shift and bandwidth variation due to the complex skin anatomy and pigment absorption. Such a high-speed spectra acquisition of in vivo deep tissue backscattering could lead to disease diagnosis in clinical settings.

Classification of squamous cell carcinoma from FF-OCT images: Data selection and progressive model construction.

We investigate the speed and performance of squamous cell carcinoma (SCC) classification from full-field optical coherence tomography (FF-OCT) images based on the convolutional neural network (CNN). Due to the unique characteristics of SCC features, the high variety of CNN, and the high volume of our 3D FF-OCT dataset, progressive model construction is a time-consuming process. To address the issue, we develop a training strategy for data selection that makes model training 16 times faster by exploiting the dependency between images and the knowledge of SCC feature distribution. The speedup makes progressive model construction computationally feasible. Our approach further refines the regularization, channel attention, and optimization mechanism of SCC classifier and improves the accuracy of SCC classification to 87.12% at the image level and 90.10% at the tomogram level. The results are obtained by testing the proposed approach on an FF-OCT dataset with over one million mouse skin images.

Comparing the results of manual and automated quantitative corneal neuroanalysing modules for beginners.

This study aimed to evaluate the reliability of in vivo confocal microscopic neuroanalysis by beginners using manual and automated modules. Images of sub-basal corneal nerve plexus (SCNP) from 108 images of 18 healthy participants were analyzed by 7 beginner observers using manual (CCMetrics, [CCM]) and automated (ACCMetrics, [ACCM]) module. SCNP parameters analyzed included corneal nerve fiber density (NFD), corneal nerve branch density (NBD), corneal nerve fiber length (NFL), and tortuosity coefficient (TC). The intra-observer repeatability, inter-observer reliability, inter-module agreement, and left-right eye symmetry level of SCNP parameters were examined. All observers showed good intra-observer repeatability using CCM (intraclass correlation coefficient [ICC] > 0.60 for all), except when measuring TC. Two observers demonstrated especially excellent repeatability in analyzing NFD, NBD, and NFL using manual mode, indicating the quality of interpretation may still be observer-dependent. Among all SCNP parameters, NFL had the best inter-observer reliability (Spearman's rank-sum correlation coefficient [SpCC] and ICC > 0.85 for the 3 original observers) and left-right symmetry level (SpCC and ICC > 0.60). In the additional analysis of inter-observer reliability using results by all 7 observers, only NFL showed good inter-observer reliability (ICC = 0.79). Compared with CCM measurements, values of ACCM measurements were significantly lower, implying a poor inter-module agreement. Our result suggested that performance of quantitative corneal neuroanalysis by beginners maybe acceptable, with NFL being the most reliable parameter, and automated method cannot fully replace manual work.

Mirau-type full-field optical coherence tomography with switchable partially spatially coherent illumination modes.

A crystalline-fiber-based Mirau-type full-field optical coherence tomography (FF-OCT) system utilizing two partially coherent illumination modes is presented. Using a diode-pumped Ti:sapphire crystalline fiber with a high numerical aperture, spatially-incoherent broadband emission can be generated with high radiance. With two modes of different spatial coherence settings, either deeper penetration depth or higher B-scan rate can be achieved. In a wide-field illumination mode, the system functions like FF-OCT with partially coherent illumination to improve the penetration depth. In a strip-field illumination mode, a compressed field is generated on the sample, and a low-speckle B-scan can be acquired by compounding pixel lines within.

Generation of Augmented Capillary Network Optical Coherence Tomography Image Data of Human Skin for Deep Learning and Capillary Segmentation.

The segmentation of capillaries in human skin in full-field optical coherence tomography (FF-OCT) images plays a vital role in clinical applications. Recent advances in deep learning techniques have demonstrated a state-of-the-art level of accuracy for the task of automatic medical image segmentation. However, a gigantic amount of annotated data is required for the successful training of deep learning models, which demands a great deal of effort and is costly. To overcome this fundamental problem, an automatic simulation algorithm to generate OCT-like skin image data with augmented capillary networks (ACNs) in a three-dimensional volume (which we called the ACN data) is presented. This algorithm simultaneously acquires augmented FF-OCT and corresponding ground truth images of capillary structures, in which potential functions are introduced to conduct the capillary pathways, and the two-dimensional Gaussian function is utilized to mimic the brightness reflected by capillary blood flow seen in real OCT data. To assess the quality of the ACN data, a U-Net deep learning model was trained by the ACN data and then tested on real in vivo FF-OCT human skin images for capillary segmentation. With properly designed data binarization for predicted image frames, the testing result of real FF-OCT data with respect to the ground truth achieved high scores in performance metrics. This demonstrates that the proposed algorithm is capable of generating ACN data that can imitate real FF-OCT skin images of capillary networks for use in research and deep learning, and that the model for capillary segmentation could be of wide benefit in clinical and biomedical applications.

Submicron spatial resolution optical coherence tomography for visualising the 3D structures of cells cultivated in complex culture systems.

Three-dimensional (3D) configuration of in vitro cultivated cells has been recognised as a valuable tool in developing stem cell and cancer cell therapy. However, currently available imaging approaches for live cells have drawbacks, including unsatisfactory resolution, lack of cross-sectional and 3D images, and poor penetration of multi-layered cell products, especially when cells are cultivated on semitransparent carriers. Herein, we report a prototype of a full-field optical coherence tomography (FF-OCT) system with isotropic submicron spatial resolution in en face and cross-sectional views that provides a label-free, non-invasive platform with high-resolution 3D imaging. We validated the imaging power of this prototype by examining (1) cultivated neuron cells (N2A cell line); (2) multilayered, cultivated limbal epithelial sheets (mCLESs); (3) neuron cells (N2A cell line) and mCLESs cultivated on a semitransparent amniotic membrane (stAM); and (4) directly adherent colonies of neuron-like cells (DACNs) covered by limbal epithelial cell sheets. Our FF-OCT exhibited a penetrance of up to 150 µm in a multilayered cell sheet and displayed the morphological differences of neurons and epithelial cells in complex coculture systems. This FF-OCT is expected to facilitate the visualisation of cultivated cell products in vitro and has a high potential for cell therapy and translational medicine research.

Detecting mouse squamous cell carcinoma from submicron full-field optical coherence tomography images by deep learning.

The standard medical practice for cancer diagnosis requires histopathology, which is an invasive and time-consuming procedure. Optical coherence tomography (OCT) is an alternative that is relatively fast, noninvasive, and able to capture three-dimensional structures of epithelial tissue. Unlike most previous OCT systems, which cannot capture crucial cellular-level information for squamous cell carcinoma (SCC) diagnosis, the full-field OCT (FF-OCT) technology used in this paper is able to produce images at sub-micron resolution and thereby facilitates the development of a deep learning algorithm for SCC detection. Experimental results show that the SCC detection algorithm can achieve a classification accuracy of 80% for mouse skin. Using the sub-micron FF-OCT imaging system, the proposed SCC detection algorithm has the potential for in-vivo applications.

Dermal epidermal junction detection for full-field optical coherence tomography data of human skin by deep learning.

Full-field optical coherence tomography (FF-OCT) has been developed to obtain three-dimensional (3D) OCT data of human skin for early diagnosis of skin cancer. Detection of dermal epidermal junction (DEJ), where melanomas and basal cell carcinomas originate, is an essential step for skin cancer diagnosis. However, most existing DEJ detection methods consider each cross-sectional frame of the 3D OCT data independently, leaving the relationship between neighboring frames unexplored. In this paper, we exploit the continuity of 3D OCT data to enhance DEJ detection. In particular, we propose a method for noise reduction of the training data and a multi-directional convolutional neural network to predict the probability of epidermal pixels in the 3D OCT data, which is more stable than one-directional convolutional neural network for DEJ detection. Our crosscheck refinement method also exploits the domain knowledge to generate a smooth DEJ surface. The average mean error of the entire DEJ detection system is approximately 6 µm.

En Face and Cross-sectional Corneal Tomograms Using Sub-micron spatial resolution Optical Coherence Tomography.

Accurate diagnosis of corneal pathology and morphological identification of different corneal layers require clear delineation of corneal three-dimensional structures and en face or cross-sectional imaging of palisade of Vogt (POV), neovascularization (NV) or corneal nerves. Here we report a prototype of full-field optical coherence tomography (FF-OCT) system with isotropic sub-micron spatial resolution in the en face and cross-sectional views. It can also provide three-dimensional reconstructed images and a large field of view (FOV) by stitching tomograms side by side. We validated the imaging power of this prototype in in vivo rat and rabbit eyes, and quantified anatomical characteristics such as corneal layer thickness, endothelial cell density and the intensity profile of different layers. This FF-OCT delineated the ridge-like structure of POV, corneal nerve bundles, and conjunctival vessels in rat eyes. It also clearly identified the vessel walls and red blood cells in rabbit model of corneal NV. The findings provided by this FF-OCT are expected to facilitate corneal disease diagnosis and treatment.

Spectroscopic characterization of Si/Mo thin-film stack at extreme ultraviolet range.

A noninvasive method for characterizing Si/Mo thin-film stack thickness and its complex transfer function using common-path optical coherence tomography is proposed, analyzed, and experimentally demonstrated. A laser-produced plasma (LPP)-based extreme ultraviolet (EUV) source was excited by a four-stage nanosecond Yb:fiber laser amplifier with a pulse energy of 1.01 mJ. The tabletop LPP EUV source was compact and stable for generating the EUV interference fringes. The measured complex transfer function of the Si/Mo stack was verified near the pristine 13.5-nm wavelength range.

Extracting Blood Vessels From Full-Field OCT Data of Human Skin by Short-Time RPCA.

Recent advances in optical coherence tomography (OCT) lead to the development of OCT angiography to provide additional helpful information for diagnosis of diseases like basal cell carcinoma. In this paper, we investigate how to extract blood vessels of human skin from full-field OCT (FF-OCT) data using the robust principal component analysis (RPCA) technique. Specifically, we propose a short-time RPCA method that divides the FF-OCT data into segments and decomposes each segment into a low-rank structure representing the relatively static tissues of human skin and a sparse matrix representing the blood vessels. The method mitigates the problem associated with the slow-varying background and is free of the detection error that RPCA may have when dealing with FF-OCT data. Both short-time RPCA and RPCA methods can extract blood vessels from FF-OCT data with heavy speckle noise, but the former takes only half the computation time of the latter. We evaluate the performance of the proposed method by comparing the extracted blood vessels with the ground truth vessels labeled by a dermatologist and show that the proposed method works equally well for FF-OCT volumes of different quality. The average F-measure improvements over the correlation-mapping OCT method, the modified amplitude-decorrelation OCT angiography method, and the RPCA method, respectively, are 0.1835, 0.1032, and 0.0458.

Compact millijoule diode-seeded two-stage fiber master oscillator power amplifier using a multipass and forward pumping scheme.

The multipass scheme for a diode-seeded fiber master oscillator power amplifier with a nanojoule-to-millijoule output energy level at a repetition rate of <100 kHz is numerically analyzed for comparison to an experimental benchmark. For a 6/125 single-mode preamplifier with a small input energy (<1 nJ), there is a significant improvement in the output energy from 0.7% to 80% and 95% of the maximum extractable energy using the double-pass and four-pass schemes, respectively. For a 30/250 large-mode-area power amplifier using the double-pass and forward pumping scheme, the required input energy is decreased from 100 µJ to 18 µJ for millijoule energy extraction with accompanying Stokes waves of less than 10% of the total energy. The system based on the full master oscillator power amplifier configuration with an output energy exceeding millijoule level can be optimally simplified to two stages for commercialization.