Universal in vivo Textural Model for Human Skin based on Optical Coherence Tomograms

SPMLD: A skin pathological image dataset for non-melanoma with detailed lesion area annotation

Skin tumors are the most common tumors in humans and the clinical characteristics of three common non-melanoma tumors (IDN, SK, BCC) are similar, resulting in a high misdiagnosis rate. The accurate differential diagnosis of these tumors needs to be judged based on pathological images. However, a shortage of experienced dermatological pathologists leads to bias in the diagnostic accuracy of these skin tumors in China. In this paper, we establish a skin pathological image dataset, SPMLD, for three non-melanoma to achieve automatic and accurate intelligent identification for them. Meanwhile, we propose a lesion-area-based enhanced classification network with the KLS module and an attention module. Specifically, we first collect thousands of H&E-stained tissue sections from patients with clinically and pathologically confirmed IDN, SK, and BCC from a single-center hospital. Then, we scan them to construct a pathological image dataset of these three skin tumors. Furthermore, we mark the complete lesion area of the entire pathology image to better learn the pathologist's diagnosis process. In addition, we applied the proposed network for lesion classification prediction on the SPMLD dataset. Finally, we conduct a series of experiments to demonstrate that this annotation and our network can effectively improve the classification results of various networks. The source dataset and code are available at https://github.com/efss24/SPMLD.git.

Three-Dimensional Bioprinted Skin Microrelief and Its Role in Skin Aging

Skin aging is a complex physiological process, in which cells and the extracellular matrix (ECM) interreact, which leads to a change in the mechanical properties of skin, which in turn affects the cell secretion and ECM deposition. The natural skin microrelief that exists from birth has rarely been taken into account when evaluating skin aging, apart from the common knowledge that microreliefs might serve as the starting point or initialize micro-wrinkles. In fact, microrelief itself also changes with aging. Does the microrelief have other, better uses? In this paper, owing to the fast-developing 3D printing technology, skin wrinkles with microrelief of different age groups were successfully manufactured using the Digital light processing (DLP) technology. The mechanical properties of skin samples with and without microrelief were tested. It was found that microrelief has a big impact on the elastic modulus of skin samples. In order to explore the role of microrelief in skin aging, the wrinkle formation was numerically analyzed. The microrelief models of different age groups were created using the modified Voronoi algorithm for the first time, which offers fast and flexible mesh formation. We found that skin microrelief plays an important role in regulating the modulus of the epidermis, which is the dominant factor in wrinkle formation. The wrinkle length and depth were also analyzed numerically for the first time, owing to the additional dimension offered by microrelief. The results showed that wrinkles are mainly caused by the modulus change of the epidermis in the aging process, and compared with the dermis, the hypodermis is irrelevant to wrinkling. Hereby, we developed a hypothesis that microrelief makes the skin adaptive to the mechanical property changes from aging by adjusting its shape and size. The native-like skin samples with microrelief might shed a light on the mechanism of wrinkling and also help with understanding the complex physiological processes associated with human skin.

Using optical coherence tomography to optimize Mohs micrographic surgery

Mohs micrographic surgery (MMS) is considered the gold standard for treating high-risk cutaneous basal cell carcinoma (BCC), but is expensive, time-consuming, and can be unpredictable as to how many stages will be required or how large the final lesion and corresponding surgical defect will be. This study is meant to investigate whether optical coherence tomography (OCT), a highly researched modality in dermatology, can be used preoperatively to map out the borders of BCC, resulting in fewer stages of MMS or a smaller final defect. In this prospective study, 22 patients with BCC undergoing surgical excision were enrolled at a single institution. All patients had previously received a diagnostic biopsy providing confirmation of BCC and had been referred to our center for excision with MMS. Immediately prior to performing MMS, OCT was used to map the borders of the lesion. MMS then proceeded according to standard protocol. OCT images were compared to histopathology for agreement. Histopathologic analysis of 7 of 22 MMS specimens (32%) revealed a total absence of BCC, indicating resolution of BCC after previous diagnostic biopsy. This outcome was correctly predicted by OCT imaging in 6 of 7 cases (86%). Nine tumors (9/22, 41%) had true BCC and required a single MMS stage, which was successfully predicted by pre-operative OCT analysis in 7 of 9 cases (78%). The final six tumors (27%) had true BCC and required two MMS stages for complete excision; preoperative OCT successfully predicted the need for a second stage in five cases (5/6, 83.3%). Overall, OCT diagnosed BCC with 95.5% accuracy (Cohen's kappa, κ = 0.89 (p-value =  < 0.01) in the center of the lesion. Following a diagnostic biopsy, OCT can be used to verify the existence or absence of residual basal cell carcinoma. When residual tumor is present that requires excision with MMS, OCT can be used to predict tumor borders, optimize surgery and minimize the need for additional surgical stages.

Rapid measurement of epidermal thickness in OCT images of skin

Epidermal thickness (ET) changes are associated with several skin diseases. To measure ET, segmentation of optical coherence tomography (OCT) images is essential; manual segmentation is very time-consuming and requires training and some understanding of how to interpret OCT images. Fast results are important in order to analyze ET over different regions of skin in rapid succession to complete a clinical examination and enable the physician to discuss results with the patient in real time. The well-known CNN-graph search (CNN-GS) methodology delivers highly accurate results, but at a high computational cost. Our objective was to build a computational core, based on CNN-GS, able to accurately segment OCT skin images in real time. We accomplished this by fine-tuning the hyperparameters, testing a range of speed-up algorithms including pruning and quantization, designing a novel pixel-skipping process, and implementing the final product with efficient use of core and threads on a multicore central processing unit (CPU). We name this product CNN-GS-skin. The method identifies two defined boundaries on OCT skin images in order to measure ET. We applied CNN-GS-skin to OCT skin images, taken from various body sites of 63 healthy individuals. Compared with CNN-GS, our described method reduced computation time by 130 [Formula: see text] with minimal reduction in ET determination accuracy (from 96.38 to 94.67%).

Multimodal Method for Differentiating Various Clinical Forms of Basal Cell Carcinoma and Benign Neoplasms In Vivo

Correct classification of skin lesions is a key step in skin cancer screening, which requires high accuracy and interpretability. This paper proposes a multimodal method for differentiating various clinical forms of basal cell carcinoma and benign neoplasms that includes machine learning. This study was conducted on 37 neoplasms, including benign neoplasms and five different clinical forms of basal cell carcinoma. The proposed multimodal screening method combines diffuse reflectance spectroscopy, optical coherence tomography and high-frequency ultrasound. Using diffuse reflectance spectroscopy, the coefficients of melanin pigmentation, erythema, hemoglobin content, and the slope coefficient of diffuse reflectance spectroscopy in the wavelength range 650-800 nm were determined. Statistical texture analysis of optical coherence tomography images was used to calculate first- and second-order statistical parameters. The analysis of ultrasound images assessed the shape of the tumor according to parameters such as area, perimeter, roundness and other characteristics. Based on the calculated parameters, a machine learning algorithm was developed to differentiate the various clinical forms of basal cell carcinoma. The proposed algorithm for classifying various forms of basal cell carcinoma and benign neoplasms provided a sensitivity of 70.6 ± 17.3%, specificity of 95.9 ± 2.5%, precision of 72.6 ± 14.2%, F1 score of 71.5 ± 15.6% and mean intersection over union of 57.6 ± 20.1%. Moreover, for differentiating basal cell carcinoma and benign neoplasms without taking into account the clinical form, the method achieved a sensitivity of 89.1 ± 8.0%, specificity of 95.1 ± 0.7%, F1 score of 89.3 ± 3.4% and mean intersection over union of 82.6 ± 10.8%.

Photoacoustic imaging for cutaneous melanoma assessment: a comprehensive review

Significance

Cutaneous melanoma (CM) has a high morbidity and mortality rate, but it can be cured if the primary lesion is detected and treated at an early stage. Imaging techniques such as photoacoustic (PA) imaging (PAI) have been studied and implemented to aid in the detection and diagnosis of CM.

Aim

Provide an overview of different PAI systems and applications for the study of CM, including the determination of tumor depth/thickness, cancer-related angiogenesis, metastases to lymph nodes, circulating tumor cells (CTCs), virtual histology, and studies using exogenous contrast agents.

Approach

A systematic review and classification of different PAI configurations was conducted based on their specific applications for melanoma detection. This review encompasses animal and preclinical studies, offering insights into the future potential of PAI in melanoma diagnosis in the clinic.

Results

PAI holds great clinical potential as a noninvasive technique for melanoma detection and disease management. PA microscopy has predominantly been used to image and study angiogenesis surrounding tumors and provide information on tumor characteristics. Additionally, PA tomography, with its increased penetration depth, has demonstrated its ability to assess melanoma thickness. Both modalities have shown promise in detecting metastases to lymph nodes and CTCs, and an all-optical implementation has been developed to perform virtual histology analyses. Animal and human studies have successfully shown the capability of PAI to detect, visualize, classify, and stage CM.

Conclusions

PAI is a promising technique for assessing the status of the skin without a surgical procedure. The capability of the modality to image microvasculature, visualize tumor boundaries, detect metastases in lymph nodes, perform fast and label-free histology, and identify CTCs could aid in the early diagnosis and classification of CM, including determination of metastatic status. In addition, it could be useful for monitoring treatment efficacy noninvasively.

The utility of high-frequency 18 MHz ultrasonography for preoperative evaluation of acral melanoma thickness in Chinese patients

Background

Despite the increasing use of preoperative ultrasound evaluation for melanoma, there is limited research on the use of this technique for Acral Melanoma (AM).

Methods

This retrospective study analyzed the electronic medical records of patients who underwent preoperative evaluation for cutaneous melanoma maximum thickness using an 18 MHz probe and histopathological examination between December 2017 and March 2021 at the Department of Dermatology in Xiangya Hospital, Central South University.

Results

A total of 105 patients were included in the study. The mean tumor thickness was 3.9 mm (s.d., 2.3), with 63% of the specimens showing ulceration and 44 patients showing lymph node metastasis. The results showed a good correlation between the high-frequency ultrasonography (HFUS) and histopathological thickness measurements, with a Spearman's correlation coefficient of 0.83 [(95% CI 0.73-0.90) (P < 0.001)]. The positive predictive value (PPV) of sonography in identifying tumor thickness was also found to be high.

Conclusion

Our study suggests that high-frequency 18 MHz ultrasonography is an effective tool for the preoperative evaluation of AM thickness. The HFUS measurements correlated well with the histopathological thickness measurements, making it a valuable and reliable method for clinicians to assess the thickness of melanoma lesions preoperatively.

Optical coherence tomography confirms non-malignant pigmented lesions in phacomatosis pigmentokeratotica using a support vector machine learning algorithm

INTRODUCTION

Phacomatosis pigmentokeratotica (PPK), an epidermal nevus syndrome, is characterized by the coexistence of nevus spilus and nevus sebaceus. Within the nevus spilus, an extensive range of atypical nevi of different morphologies may manifest. Pigmented lesions may fulfill the ABCDE criteria for melanoma, which may prompt a physician to perform a full-thickness biopsy.

MOTIVATION

Excisions result in pain, mental distress, and physical disfigurement. For patients with a significant number of nevi with morphologic atypia, it may not be physically feasible to biopsy a large number of lesions. Optical coherence tomography (OCT) is a non-invasive imaging modality that may be used to visualize non-melanoma and melanoma skin cancers.

MATERIALS AND METHOD

In this study, we used OCT to image pigmented lesions with morphologic atypia in a patient with PPK and assessed their quantitative optical properties compared to OCT cases of melanoma. We implement a support vector machine learning algorithm with Gabor wavelet transformation algorithm during post-image processing to extract optical properties and calculate attenuation coefficients.

RESULTS

The algorithm was trained and tested to extract and classify textural data.

CONCLUSION

We conclude that implementing this post-imaging machine learning algorithm to OCT images of pigmented lesions in PPK has been able to successfully confirm benign optical properties. Additionally, we identified remarkable differences in attenuation coefficient values and tissue optical characteristics, further defining separating benign features of pigmented lesions in PPK from malignant features.

Deep learning-based optical coherence tomography image analysis of human brain cancer

Real-time intraoperative delineation of cancer and non-cancer brain tissues, especially in the eloquent cortex, is critical for thorough cancer resection, lengthening survival, and improving quality of life. Prior studies have established that thresholding optical attenuation values reveals cancer regions with high sensitivity and specificity. However, threshold of a single value disregards local information important to making more robust predictions. Hence, we propose deep convolutional neural networks (CNNs) trained on labeled OCT images and co-occurrence matrix features extracted from these images to synergize attenuation characteristics and texture features. Specifically, we adapt a deep ensemble model trained on 5,831 examples in a training dataset of 7 patients. We obtain 93.31% sensitivity and 97.04% specificity on a holdout set of 4 patients without the need for beam profile normalization using a reference phantom. The segmentation maps produced by parsing the OCT volume and tiling the outputs of our model are in excellent agreement with attenuation mapping-based methods. Our new approach for this important application has considerable implications for clinical translation.

Classification of neck tissues in OCT images by using convolutional neural network

Identification and classification of surrounding neck tissues are very important in thyroid surgery. The advantages of optical coherence tomography (OCT), high resolution, non-invasion, and non-destruction make it have great potential in identifying different neck tissues during thyroidectomy. We studied the automatic classification for neck tissues in OCT images based on convolutional neural network in this paper. OCT images of five kinds of neck tissues were collected firstly by our home-made swept source (SS-OCT) system, and a dataset was built for neural network training. Three image classification neural networks: LeNet, VGGNet, and ResNet, were used to train and test the dataset. The impact of transfer learning on the classification of neck tissue OCT images was also studied. Through the comparison of accuracy, it was found that ResNet has the best classification accuracy among the three networks. In addition, transfer learning did not significantly improve the accuracy, but it can somewhat accelerate the convergence of the network and shorten the network training time.

Film-Terminated Fibrillar Microstructures with Improved Adhesion on Skin-like Surfaces

Adhesives for interaction with human skin and tissues are needed for multiple applications. Micropatterned dry adhesives are potential candidates, allowing for a conformal contact and glue-free adhesion based on van der Waals interactions. In this study, we investigate the superior adhesion of film-terminated fibrillar microstructures (fibril diameter, 60 μm; aspect ratio, 3) in contact with surfaces of skin-like roughness (Rz 50 μm). Adhesion decays only moderately with increasing roughness, in contrast to unstructured samples. Sinusoidal model surfaces adhere when their wavelengths exceed about four fibril diameters. The film-terminated microstructure exhibits a saturation of the compressive force during application, implying a pressure safety regime protecting delicate counter surfaces. Applications of this novel adhesive concept are foreseen in the fields of wearable electronics and wound dressing.

Semisupervised representative learning for measuring epidermal thickness in human subjects in optical coherence tomography by leveraging datasets from rodent models

SIGNIFICANCE

Morphological changes in the epidermis layer are critical for the diagnosis and assessment of various skin diseases. Due to its noninvasiveness, optical coherence tomography (OCT) is a good candidate for observing microstructural changes in skin. Convolutional neural network (CNN) has been successfully used for automated segmentation of the skin layers of OCT images to provide an objective evaluation of skin disorders. Such method is reliable, provided that a large amount of labeled data is available, which is very time-consuming and tedious. The scarcity of patient data also puts another layer of difficulty to make the model more generalizable.

AIM

We developed a semisupervised representation learning method to provide data augmentations.

APPROACH

We used rodent models to train neural networks for accurate segmentation of clinical data.

RESULT

The learning quality is maintained with only one OCT labeled image per volume that is acquired from patients. Data augmentation introduces a semantically meaningful variance, allowing for better generalization. Our experiments demonstrate the proposed method can achieve accurate segmentation and thickness measurement of the epidermis.

CONCLUSION

This is the first report of semisupervised representative learning applied to OCT images from clinical data by making full use of the data acquired from rodent models. The proposed method promises to aid in the clinical assessment and treatment planning of skin diseases.

Non-invasive scoring of cellular atypia in keratinocyte cancers in 3D LC-OCT images using Deep Learning

Diagnosis based on histopathology for skin cancer detection is today's gold standard and relies on the presence or absence of biomarkers and cellular atypia. However it suffers drawbacks: it requires a strong expertise and is time-consuming. Moreover the notion of atypia or dysplasia of the visible cells used for diagnosis is very subjective, with poor inter-rater agreement reported in the literature. Lastly, histology requires a biopsy which is an invasive procedure and only captures a small sample of the lesion, which is insufficient in the context of large fields of cancerization. Here we demonstrate that the notion of cellular atypia can be objectively defined and quantified with a non-invasive in-vivo approach in three dimensions (3D). A Deep Learning (DL) algorithm is trained to segment keratinocyte (KC) nuclei from Line-field Confocal Optical Coherence Tomography (LC-OCT) 3D images. Based on these segmentations, a series of quantitative, reproducible and biologically relevant metrics is derived to describe KC nuclei individually. We show that, using those metrics, simple and more complex definitions of atypia can be derived to discriminate between healthy and pathological skins, achieving Area Under the ROC Curve (AUC) scores superior than 0.965, largely outperforming medical experts on the same task with an AUC of 0.766. All together, our approach and findings open the door to a precise quantitative monitoring of skin lesions and treatments, offering a promising non-invasive tool for clinical studies to demonstrate the effects of a treatment and for clinicians to assess the severity of a lesion and follow the evolution of pre-cancerous lesions over time.

Identification of oral precancerous and cancerous tissue by swept source optical coherence tomography

BACKGROUND AND OBJECTIVES

Distinguishing cancer from precancerous lesions is critical and challenging in oral medicine. As a noninvasive method, optical coherence tomography (OCT) has the advantages of real-time, in vivo, and large-depth imaging. Texture information hidden in OCT images can provide an important auxiliary effect for improving diagnostic accuracy. The aim of this study is to explore a reliable and accurate OCT-based method for the screening and diagnosis of human oral diseases, especially oral cancer.

MATERIALS AND METHODS

Fresh ex vivo oral tissues including normal mucosa, leukoplakia with epithelial hyperplasia (LEH), and oral squamous cell carcinoma (OSCC) were imaged intraoperatively by a homemade OCT system, and 58 texture features were extracted to create computational models of these tissues. A principal component analysis algorithm was employed to optimize the combination of texture feature vectors. The identification based on artificial neural network (ANN) was proposed and the sensitivity/specificity was calculated statistically to evaluate the classification performance.

RESULTS

A total of 71 sites of three types of oral tissues were measured, and 5176 OCT images of three types of oral tissues were used in this study. The superior classification result based on ANN was obtained with an average accuracy of 98.17%. The sensitivity and specificity of normal mucosa, LEH, and OSCC are 98.17% / 98.38%, 93.81% / 98.54%, and 98.11% / 99.04%, respectively.

CONCLUSION

It is demonstrated from the high accuracies, sensitivities, and specificities that texture-based analysis can be used to identify oral precancerous and cancerous tissue in OCT images, and it has the potential to help surgeons in diseases screening and diagnosis effectively.

Contrast enhancement and speckle suppression in OCT images based on a selective weighted variational enhancement model and an SP-FOOPDE algorithm

Simultaneous contrast enhancement and speckle suppression in optical coherence tomography (OCT) are of great significance to medical diagnosis. In this paper, we propose a selective weighted variational enhancement (SWVE) model to enhance the structural parts of OCT images, and then present a shape-preserving fourth-order-oriented partial differential equations (SP-FOOPDE) algorithm to suppress speckle noise. To be specific, in the SWVE model, we first introduce the fast and robust fuzzy c-means clustering (FRFCM) algorithm to generate masks based on the gray-level histograms of the reconstructed OCT images and utilize the masks to distinguish the structural parts from the background. Then the retinex-based weighted variational model, combined with gamma correction, is adopted to enhance the structural parts by multiplying the estimated reflectance with the adjusted illumination. In the despeckling process, we present an SP-FOOPDE algorithm with the fidelity term modified by the shearlet transform to strike a splendid balance between noise suppression and structural preservation. Experimental results show that the proposed method performs well in contrast enhancement and speckle suppression, with better quality metrics of the MSE, PSNR, CNR, ENL, EKI, and ν and better noise immunity than the related method. Moreover, the application to the segmentation preprocessing exhibits that the retinal structure of the OCT images processed by the proposed method can be completely segmented.

Classification of oral salivary gland tumors based on texture features in optical coherence tomography images

Currently, the diagnoses of oral diseases primarily depend on the visual recognition of experienced clinicians. It has been proven that automatic recognition based on images can support clinical decision-making by extracting and analyzing objective hidden information. In recent years, optical coherence tomography (OCT) has become a powerful optical imaging technique with the advantages of high resolution and non-invasion. In our study, a dataset composed of four kinds of oral salivary gland tumors (SGTs) was obtained from a homemade swept-source OCT, including two benign and two malignant tumors. Seventy-six texture features were extracted from OCT images to create computational models of diseases. It was demonstrated that the artificial neural network (ANN) based on principal component analysis (PCA) can obtain high diagnostic sensitivity and specificity (higher than 99%) for these four kinds of tumors. The classification accuracy of each tumor is larger than 99%. In addition, the performances of two classifiers (ANN and support vector machine) were quantitatively evaluated based on SGTs. It was proven that the texture features in OCT images provided objective information to classify oral tumors.

Optical Coherence Tomography and Microdialysis for Microneedle-Mediated Penetration Enhancement Study of Paeoniflorin-Loaded Ethosomes

BACKGROUND

To understand the cumulative effect of topical formulations after medication, evaluate the therapeutic effect of microneedle-assisted (MN-assisted) paeoniflorin-loaded ethosomes (TGP-E), and explore the potential for deep penetration of drugs, this paper uses microdialysis to systematically study the percutaneous pharmacokinetics of TGP-E.

METHODS

First, optical coherence tomography (OCT) was used to study the effectiveness of microneedle puncture. Second, a microdialysis method and a UPLC-MS method for determining the amount of paeoniflorin (Pae) in dialysate were established. Finally, the transdermal pharmacokinetics of TGP-E was studied using in vivo microdialysis in rats under the above MN-assisted conditions.

RESULTS

The optimal MN-assisted conditions were obtained at a microneedle length of 500 μm, a pressure of 3 N, and an action time of 3 min. The pharmacokinetic results demonstrated that the maximum drug concentration (Cmax) and the area under the curve (AUC) of the TGP-E gel were higher than the TGP-saline solution gel, and the mean retention time was lower. These indicated that microneedle can promote the entry of the ethosomes into the skin for in vivo experiments and greatly improve the possibility of deep penetration of the water-soluble Pae.

CONCLUSION

Therefore, the microneedle-ethosomes delivery system is a more ideal means for promoting the deep penetration of Pae. These findings may provide a reference for the combination of multiple penetration-enhancement ways to promote drug absorption, and also provide a new insight to realize the development of novel, safe, and more effective dosage forms and administration routes of drugs.

A Weighted Average Phase Velocity Inversion Model for Depth-Resolved Elasticity Evaluation in Human Skin In-Vivo

OBJECTIVE

In current surface acoustic wave (SAW) elastography field, wavelength-depth inversion model is a straightforward and widely used inversion model for depth-resolved elasticity profile reconstruction. However, the elasticity directly evaluated from the wavelength-depth relationship is biased. Thus, a new inversion model, termed weighted average phase velocity (WAPV) inversion model, is proposed to provide depth-resolved Young's modulus estimate with better accuracy.

METHODS

The forward model for SAW phase velocity dispersion curve generation was derived from the numerical simulations of SAWs in layered materials, and inversion was implemented by matching the measured phase velocity dispersion curve to the one generated from the forward model using the least squares fitting. Three two-layer agar phantoms with different top-layer thicknesses and one three-layer agar phantom were tested to validate the proposed inversion model. Then the model was demonstrated on human skin at various sites (palm, forearm and back of hand) in-vivo.

RESULTS

In multi-layered agar phantoms, depth-resolved elasticity estimates provided by the model have a maximal total inversion error of 15.2% per sample after inversion error compensation. In in-vivo human skin, the quantified bulk Young's moduli (palm: 212 ± 78 kPa; forearm: 32 ± 11 kPa and back of hand: 29 ± 8 kPa) are comparable to the reference values in the literature.

CONCLUSION

The WAPV inversion model can provide accurate depth-resolved Young's modulus estimates in layered biological soft tissues.

SIGNIFICANCE

The proposed model can predict depth-resolved elasticity in layered biological soft tissues with a reasonable accuracy which traditional wavelength-depth inversion model cannot provide.

Melanoma Biomarkers and Their Potential Application for In Vivo Diagnostic Imaging Modalities

Melanoma is the deadliest form of skin cancer and remains a diagnostic challenge in the dermatology clinic. Several non-invasive imaging techniques have been developed to identify melanoma. The signal source in each of these modalities is based on the alteration of physical characteristics of the tissue from healthy/benign to melanoma. However, as these characteristics are not always sufficiently specific, the current imaging techniques are not adequate for use in the clinical setting. A more robust way of melanoma diagnosis is to "stain" or selectively target the suspect tissue with a melanoma biomarker attached to a contrast enhancer of one imaging modality. Here, we categorize and review known melanoma diagnostic biomarkers with the goal of guiding skin imaging experts to design an appropriate diagnostic tool for differentiating between melanoma and benign lesions with a high specificity and sensitivity.

Multimodal imaging with integrated auto-fluorescence and optical coherence tomography for identification of neck tissues

We report a multimodal optical system by combining OCT with autofluorescence imaging for identifying neck tissues, which can use the advantages of large field of view and high sensitivity for identifying parathyroid glands of fluorescence imaging, and high-resolution structural imaging of OCT to confirm them and identify lymph nodes and metastatic lymph nodes at the same time. It is proven that this multimodal optical system can be used to identify different neck tissues effectively and efficiently. We think that integrated auto-fluorescence and OCT imaging have the great potential in the application of navigation and assistant diagnosis of thyroid surgery.

Novel Cell-Based and Tissue Engineering Approaches for Induction of Angiogenesis as an Alternative Therapy for Diabetic Retinopathy

Diabetic retinopathy (DR) is the most frequent microvascular complication of long-term diabetes and the most common cause of blindness, increasing morbidity in the working-age population. The most effective therapies for these complications include laser photocoagulation and anti-vascular endothelial growth factor (VEGF) intravitreal injections. However, laser and anti-VEGF drugs are untenable as a final solution as they fail to address the underlying neurovascular degeneration and ischemia. Regenerative medicine may be a more promising approach, aimed at the repair of blood vessels and reversal of retinal ischemia. Stem cell therapy has introduced a novel way to reverse the underlying ischemia present in microvascular complications in diseases such as diabetes. The present review discusses current treatments, their side effects, and novel cell-based and tissue engineering approaches as a potential alternative therapeutic approach.

BPBSAM: Body part-specific burn severity assessment model

BACKGROUND AND OBJECTIVE

Burns are a serious health problem leading to several thousand deaths annually, and despite the growth of science and technology, automated burns diagnosis still remains a major challenge. Researchers have been exploring visual images-based automated approaches for burn diagnosis. Noting that the impact of a burn on a particular body part can be related to the skin thickness factor, we propose a deep convolutional neural network based body part-specific burns severity assessment model (BPBSAM).

METHOD

Considering skin anatomy, BPBSAM estimates burn severity using body part-specific support vector machines trained with CNN features extracted from burnt body part images. Thus BPBSAM first identifies the body part of the burn images using a convolutional neural network in training of which the challenge of limited availability of burnt body part images is successfully addressed by using available larger-size datasets of non-burn images of different body parts considered (face, hand, back, and inner forearm). We prepared a rich labelled burn images datasets: BI & UBI and trained several deep learning models with existing models as pipeline for body part classification and feature extraction for severity estimation.

RESULTS

The proposed novel BPBSAM method classified the severity of burn from color images of burn injury with an overall average F1 score of 77.8% and accuracy of 84.85% for the test BI dataset and 87.2% and 91.53% for the UBI dataset, respectively. For burn images body part classification, the average accuracy of around 93% is achieved, and for burn severity assessment, the proposed BPBSAM outperformed the generic method in terms of overall average accuracy by 10.61%, 4.55%, and 3.03% with pipelines ResNet50, VGG16, and VGG19, respectively.

CONCLUSIONS

The main contributions of this work along with burn images labelled datasets creation is that the proposed customized body part-specific burn severity assessment model can significantly improve the performance in spite of having small burn images dataset. This highly innovative customized body part-specific approach could also be used to deal with the burn region segmentation problem. Moreover, fine tuning on pre-trained non-burn body part images network has proven to be robust and reliable.

Contrast-enhanced optical coherence tomography for melanoma detection: An in vitro study

Optical coherence tomography (OCT), with a high-spatial resolution (<10 microns), intermediate penetration depth (~1.5 mm) and volumetric imaging capability is a great candidate to be used as a diagnostic-assistant modality in dermatology. At this time, the accuracy of OCT for melanoma detection is lower than anticipated. In this letter, we studied for the first time, the use of a novel contrast agent consist of ultra-small nanoparticles conjugated to a melanoma biomarker to improve the accuracy of OCT for differentiation of melanoma cells from nonmelanoma cells, in vitro. We call this approach SMall nanoparticle Aggregation-enhanced Radiomics of Tumor (SMART)-OCT imaging. This initial proof of concept study is the first step toward the broad utilization of this method for high accuracy all types of tumor detection applications.

Intraoperative use of optical coherence tomography to differentiate normal and diseased thyroid and parathyroid tissues from lymph node and fat

The purpose of this study is twofold: (1) to determine the feasibility of optical coherence tomography (OCT) to differentiate normal and diseased tissue of the neck region intraoperatively and (2) to evaluate how accurately a cohort of test subjects can identify various tissue types when shown a sample set of OCT images. In this in vivo, prospective, single institutional study, an OCT imaging system (Niris, Imalux, Cleveland, OH) was used to image parathyroid, thyroid, lymph node, and fat tissue in 76 patients during neck surgery. Biopsies were performed for comparison of OCT images with histology in select cases (n = 20). Finally, a group of either surgeons or scientists familiar with OCT (n = 17) were shown a sample of OCT images and asked to identify the tissue. A total of 437 OCT images were analyzed, and characteristic features of each tissue type were identified. OCT demonstrated distinct differences in structural architecture and signal intensity that allows differentiation between thyroid and parathyroid tissues, lymph nodes, and fat. OCT images were also compared with histology with good correlation. There was no difference in correctly identifying OCT-imaged tissue type between surgeons and scientists. This study is the first in vivo OCT imaging study to evaluate both normal and diseased tissues that may be encountered during neck surgery. OCT has the potential to become a valuable intraoperative tool to differentiate diseased and normal thyroid tissue intraoperatively to obtain an "optical biopsy" in real time without fixation, staining, or tissue resection.

Identification of oral cancer in OCT images based on an optical attenuation model

Surgery is still the first choice to treat oral cancer, where it is important to detect surgical margins in order to reduce cancer recurrence and maintain oral-maxillofacial function simultaneously. As a non-invasive and in situ imaging technique, optical coherence tomography (OCT) can obtain images close to the resolution of histopathology, which makes it have great potential in intraoperative diagnosis. However, it is not enough to find surgical margins accurately just observing OCT images directly and qualitatively. The purpose of this study is to identify oral cancer in OCT images by investigating the quantitative difference of cancer and non-cancer tissue. Based on an available optical attenuation model and the axial confocal PSF of a home-made swept source OCT system, by using fresh ex vivo human oral tissues from 14 patients of oral squamous cell carcinoma (OSCC) as the samples, diagnosis with sensitivity (97.88%) and specificity (83.77%) was achieved at the attenuation threshold of 4.7 mm^-1, and the accuracy of identification reached 91.15% in our study. Our preliminary results demonstrated that the oral cancer resection will be guided accurately and the clinical application of OCT will be further promoted by deeply mining the information hidden in OCT images.

Real-time Burn Classification using Ultrasound Imaging

This article presents a real-time approach for classification of burn depth based on B-mode ultrasound imaging. A grey-level co-occurrence matrix (GLCM) computed from the ultrasound images of the tissue is employed to construct the textural feature set and the classification is performed using nonlinear support vector machine and kernel Fisher discriminant analysis. A leave-one-out cross-validation is used for the independent assessment of the classifiers. The model is tested for pair-wise binary classification of four burn conditions in ex vivo porcine skin tissue: (i) 200 °F for 10 s, (ii) 200 °F for 30 s, (iii) 450 °F for 10 s, and (iv) 450 °F for 30 s. The average classification accuracy for pairwise separation is 99% with just over 30 samples in each burn group and the average multiclass classification accuracy is 93%. The results highlight that the ultrasound imaging-based burn classification approach in conjunction with the GLCM texture features provide an accurate assessment of altered tissue characteristics with relatively moderate sample sizes, which is often the case with experimental and clinical datasets. The proposed method is shown to have the potential to assist with the real-time clinical assessment of burn degrees, particularly for discriminating between superficial and deep second degree burns, which is challenging in clinical practice.

The potential role of using vaccine patches to induce immunity: platform and pathways to innovation and commercialization

Introduction: In the last two decades, the evidence related to using vaccine patches with multiple short projections (≤1 mm) to deliver vaccines through the skin increased significantly and demonstrated their potential as an innovative delivery platform.Areas covered: We review the vaccine patch literature published in English as of 1 March 2019, as well as available information from key stakeholders related to vaccine patches as a platform. We identify key research topics related to basic and translational science on skin physical properties and immunobiology, patch development, and vaccine manufacturing.Expert opinion: Currently, vaccine patch developers continue to address some basic science and other platform issues in the context of developing a potential vaccine patch presentation for an existing or new vaccine. Additional clinical data and manufacturing experience could shift the balance toward incentivizing existing vaccine manufactures to further explore the use of vaccine patches to deliver their products. Incentives for innovation of vaccine patches differ for developed and developing countries, which will necessitate different strategies (e.g. public-private partnerships, push, or pull mechanisms) to support the basic and applied research needed to ensure a strong evidence base and to overcome translational barriers for vaccine patches as a delivery platform.

Radiomics Analysis on Contrast-Enhanced Spectral Mammography Images for Breast Cancer Diagnosis: A Pilot Study

Contrast-enhanced spectral mammography is one of the latest diagnostic tool for breast care; therefore, the literature is poor in radiomics image analysis useful to drive the development of automatic diagnostic support systems. In this work, we propose a preliminary exploratory analysis to evaluate the impact of different sets of textural features in the discrimination of benign and malignant breast lesions. The analysis is performed on 55 ROIs extracted from 51 patients referred to Istituto Tumori “Giovanni Paolo II” of Bari (Italy) from the breast cancer screening phase between March 2017 and June 2018. We extracted feature sets by calculating statistical measures on original ROIs, gradiented images, Haar decompositions of the same original ROIs, and on gray-level co-occurrence matrices of the each sub-ROI obtained by Haar transform. First, we evaluated the overall impact of each feature set on the diagnosis through a principal component analysis by training a support vector machine classifier. Then, in order to identify a sub-set for each set of features with higher diagnostic power, we developed a feature importance analysis by means of wrapper and embedded methods. Finally, we trained an SVM classifier on each sub-set of previously selected features to compare their classification performances with respect to those of the overall set. We found a sub-set of significant features extracted from the original ROIs with a diagnostic accuracy greater than 80 % . The features extracted from each sub-ROI decomposed by two levels of Haar transform were predictive only when they were all used without any selection, reaching the best mean accuracy of about 80 % . Moreover, most of the significant features calculated by HAAR decompositions and their GLCMs were extracted from recombined CESM images. Our pilot study suggested that textural features could provide complementary information about the characterization of breast lesions. In particular, we found a sub-set of significant features extracted from the original ROIs, gradiented ROI images, and GLCMs calculated from each sub-ROI previously decomposed by the Haar transform.

Development of a Stationary 3D Photoacoustic Imaging System Using Sparse Single-Element Transducers: Phantom Study

Photoacoustic imaging (PAI) is an emerging label-free and non-invasive modality for imaging biological tissues. PAI has been implemented in different configurations, one of which is photoacoustic computed tomography (PACT) with a potential wide range of applications, including brain and breast imaging. Hemispherical Array PACT (HA-PACT) is a variation of PACT that has solved the limited detection-view problem. Here, we designed an HA-PACT system consisting of 50 single element transducers. For implementation, we initially performed a simulation study, with parameters close to those in practice, to determine the relationship between the number of transducers and the quality of the reconstructed image. We then used the greatest number of transducers possible on the hemisphere and imaged copper wire phantoms coated with a light absorbing material to evaluate the performance of the system. Several practical issues such as light illumination, arrangement of the transducers, and an image reconstruction algorithm have been comprehensively studied.

Dynamic programming and automated segmentation of optical coherence tomography images of the neonatal subglottis: enabling efficient diagnostics to manage subglottic stenosis

Subglottic stenosis (SGS) is a challenging disease to diagnose in neonates. Long-range optical coherence tomography (OCT) is an optical imaging modality that has been described to image the subglottis in intubated neonates. A major challenge associated with OCT imaging is the lack of an automated method for image analysis and micrometry of large volumes of data that are acquired with each airway scan (1 to 2 Gb). We developed a tissue segmentation algorithm that identifies, measures, and conducts image analysis on tissue layers within the mucosa and submucosa and compared these automated tissue measurements with manual tracings. We noted small but statistically significant differences in thickness measurements of the mucosa and submucosa layers in the larynx (p  <  0.001), subglottis (p  =  0.015), and trachea (p  =  0.012). The automated algorithm was also shown to be over 8 times faster than the manual approach. Moderate Pearson correlations were found between different tissue texture parameters and the patient’s gestational age at birth, age in days, duration of intubation, and differences with age (mean age 17 days). Automated OCT data analysis is necessary in the diagnosis and monitoring of SGS, as it can provide vital information about the airway in real time and aid clinicians in making management decisions for intubated neonates.

Optical coherence tomography angiography and photoacoustic imaging in dermatology

Optical coherence tomography angiography (OCTA) is a relatively novel functional extension of the widely accepted ophthalmic imaging tool named optical coherence tomography (OCT). Since OCTA's debut in ophthalmology, researchers have also been trying to expand its translational application in dermatology. The ability of OCTA to resolve microvasculature has shown promising results in imaging skin diseases. Meanwhile, photoacoustic imaging (PAI), which uses laser pulse induced ultrasound waves as the signal, has been studied to differentiate human skin layers and to help in skin disease diagnosis. This perspective article gives a short review of OCTA and PAI in the field of photodermatology. After an introduction to the principles of OCTA and PAI, we describe the most updated results of skin disease imaging using these two optical imaging modalities. We also place emphasis on dual modality imaging combining OCTA and photoacoustic tomography (PAT) for dermatological applications. In the end, the challenges and prospects of these two imaging modalities in dermatology are discussed.

Photoacoustic/Ultrasound/Optical Coherence Tomography Evaluation of Melanoma Lesion and Healthy Skin in a Swine Model

The marked increase in the incidence of melanoma coupled with the rapid drop in the survival rate after metastasis has promoted the investigation into improved diagnostic methods for melanoma. High-frequency ultrasound (US), optical coherence tomography (OCT), and photoacoustic imaging (PAI) are three potential modalities that can assist a dermatologist by providing extra information beyond dermoscopic features. In this study, we imaged a swine model with spontaneous melanoma using these modalities and compared the images with images of nearby healthy skin. Histology images were used for validation.

An Application of Simulated Annealing in Compensation of Nonlinearity of Scanners

Galvo scanners are popular devices for fast transversal scanning. A triangular signal is usually employed to drive galvo scanners at scanning rates close to the inverse of their response time where scanning deflection becomes a nonlinear function of applied voltage. To address this, the triangular signal is synthesized from several short ramps with different slopes. An optimization algorithm similar to a simulated annealing algorithm is used for finding the optimal signal shape to drive the galvo scanners. As a result, a significant reduction in the nonlinearity of the galvo scanning is obtained.

Optical Radiomic Signatures Derived from Optical Coherence Tomography Images Improve Identification of Melanoma

The current gold standard for clinical diagnosis of melanoma is excisional biopsy and histopathologic analysis. Approximately 15-30 benign lesions are biopsied to diagnose each melanoma. In addition, biopsies are invasive and result in pain, anxiety, scarring, and disfigurement of patients, which can add additional burden to the health care system. Among several imaging techniques developed to enhance melanoma diagnosis, optical coherence tomography (OCT), with its high-resolution and intermediate penetration depth, can potentially provide required diagnostic information noninvasively. Here, we present an image analysis algorithm, "optical properties extraction (OPE)," which improves the specificity and sensitivity of OCT by identifying unique optical radiomic signatures pertinent to melanoma detection. We evaluated the performance of the algorithm using several tissue-mimicking phantoms and then tested the OPE algorithm on 69 human subjects. Our data show that benign nevi and melanoma can be differentiated with 97% sensitivity and 98% specificity. These findings suggest that the adoption of OPE algorithm in the clinic can lead to improvements in melanoma diagnosis and patient experience. SIGNIFICANCE: This study describes a noninvasive, safe, simple-to-implement, and accurate method for the detection and differentiation of malignant melanoma versus benign nevi.

Development of Low-Cost Fast Photoacoustic Computed Tomography: System Characterization and Phantom Study

A low-cost Photoacoustic Computed Tomography (PACT) system consisting of 16 single-element transducers has been developed. Our design proposes a fast rotating mechanism of 360o rotation around the imaging target, generating comparable images to those produced by large-number-element (e.g., 512, 1024, etc.) ring-array PACT systems. The 2D images with a temporal resolution of 1.5 s and a spatial resolution of 240 µm were achieved. The performance of the proposed system was evaluated by imaging complex phantom. The purpose of the proposed development is to provide researchers a low-cost alternative 2D photoacoustic computed tomography system with comparable resolution to the current high performance expensive ring-array PACT systems.

Vibration analysis of healthy skin: toward a noninvasive skin diagnosis methodology

Several noninvasive imaging techniques have been developed to monitor the health of skin and enhance the diagnosis of skin diseases. Among them, skin elastography is a popular technique used to measure the elasticity of the skin. A change in the elasticity of the skin can influence its natural frequencies and mode shapes. We propose a technique to use the resonant frequencies and mode shapes of the skin to monitor its health. Our study demonstrates how the resonant frequencies and mode shapes of skin can be obtained using numerical and experimental analysis. In our study, natural frequencies and mode shapes are obtained via two methods: (1) finite element analysis: an eigensolution is performed on a finite element model of normal skin, including stratum corneum, epidermis, dermis, and subcutaneous layers and (2) digital image correlation (DIC): several in-vivo measurements have been performed using DIC. The experimental results show a correlation between the DIC and FE results suggesting a noninvasive method to obtain vibration properties of the skin. This method can be further examined to be eventually used as a method to differentiate healthy skin from diseased skin. Prevention, early diagnosis, and treatment are critical in helping to reduce the incidence, morbidity, and mortality associated with skin cancer; thus, making the current study significant and important in the field of skin biomechanics.

Demonstration of Triband Multi-Focal Imaging with Optical Coherence Tomography

We demonstrate an extended depth of focus optical coherence tomography (OCT) system based on the use of chromatic aberration to create displaced focal planes in the sample. The system uses a wavelength-swept source tuning over three spectral bands and three separate interferometers, each of which interfaces to a single illumination/collection fiber. The resulting three imaged volumes are merged in post-processing to generate an image with a larger depth of focus than is obtained from each band individually. The improvements are demonstrated in structural imaging of a porous phantom and a lipid-cleared murine brain, and by angiographic imaging of human skin. By using a coaxial approach with Gaussian beams, this approach enables an extended focus with relatively simple microscope optics and data-merging algorithms.

Granular Cell Tumor Imaging Using Optical Coherence Tomography

BACKGROUND

Granular cell tumor (GCT) is a relatively uncommon tumor that may affect the skin. The tumor can develop anywhere on the body, although it is predominately seen in oral cavities and in the head and neck regions. Here, we present the results of optical coherence tomography (OCT) imaging of a large GCT located on the abdomen of a patient. We also present an analytical method to differentiate between healthy tissue and GCT tissues.

MATERIALS AND METHODS

A multibeam, Fourier domain, swept source OCT was used for imaging. The OCT had a central wavelength of 1305 ± 15 nm and lateral and axial resolutions of 7.5 and 10 µm, respectively. Qualitative and quantitative analyses of the tumor and healthy skin are reported.

RESULTS

Abrupt changes in architectures of the dermal and epidermal layers in the GCT lesion were observed. These architectural changes were not observed in healthy skin.

DISCUSSION

To quantitatively differentiate healthy skin from tumor regions, an optical attenuation coefficient analysis based on single-scattering formulation was performed. The methodology introduced here could have the capability to delineate boundaries of a tumor prior to surgical excision.

Refractive index correction in optical coherence tomography images of multilayer tissues

We propose an algorithm to compensate for the refractive index error in the optical coherence tomography (OCT) images of multilayer tissues, such as skin. The performance of the proposed method has been evaluated on one- and two-layer solid phantoms, as well as the skin of rat paw.

The value of ultrahigh resolution OCT in dermatology - delineating the dermo-epidermal junction, capillaries in the dermal papillae and vellus hairs

Optical coherence tomography (OCT) imaging of the skin is gaining recognition and is increasingly applied to dermatological research. A key dermatological parameter inferred from an OCT image is the epidermal (Ep) thickness as a thickened Ep can be an indicator of a skin disease. Agreement in the literature on the signal characters of Ep and the subjacent skin layer, the dermis (D), is evident. Ambiguities of the OCT signal interpretation in the literature is however seen for the transition region between the Ep and D, which from histology is known as the dermo-epidermal junction (DEJ); a distinct junction comprised of the lower surface of a single cell layer in epidermis (the stratum basale) connected to an even thinner membrane (the basement membrane). The basement membrane is attached to the underlying dermis. In this work we investigate the impact of an improved axial and lateral resolution on the applicability of OCT for imaging of the skin. To this goal, OCT images are compared produced by a commercial OCT system (Vivosight from Michaelson Diagnostics) and by an in-house built ultrahigh resolution (UHR-) OCT system for dermatology. In 11 healthy volunteers, we investigate the DEJ signal characteristics. We perform a detailed analysis of the dark (low) signal band clearly seen for UHR-OCT in the DEJ region where we, by using a transition function, find the signal transition of axial sub-resolution character, which can be directly attributed to the exact location of DEJ, both in normal (thin/hairy) and glabrous (thick) skin. To our knowledge no detailed delineating of the DEJ in the UHR-OCT image has previously been reported, despite many publications within this field. For selected healthy volunteers, we investigate the dermal papillae and the vellus hairs and identify distinct features that only UHR-OCT can resolve. Differences are seen in tracing hairs of diameter below 20 μm, and in imaging the dermal papillae where, when utilising the UHR-OCT, capillary structures are identified in the hand palm, not previously reported in OCT studies and specifically for glabrous skin not reported in any other in vivo optical imaging studies.