Automated video-mosaicking approach for confocal microscopic imaging in vivo: an approach to address challenges in imaging living tissue and extend field of view

Real-Time Endomicroscopic Image Mosaicking with an EKF-based Sensor Fusion Approach

This study presents a real-time sensor fusion framework based on the extended Kalman filter (EKF) for accurate and robust endomicroscopic image mosaicking. The sensor fusion framework incorporates an optical tracking system that can track 6-DOF pose of the imaging probe with high accuracy in real time in conjunction with 2D local image registration from image feature matching between two consecutive frames. We evaluated the performance of the real-time image mosaicking based on the sensor fusion compared with the image or tracker only approach. As a result, it could retain the microscopic level of image detail from the image-based approach and also achieve a robust image mosaic without any drift by using the accurate optical tracking system.

Confocal Microscopy for Diagnosis and Management of Cutaneous Malignancies: Clinical Impacts and Innovation

Cutaneous malignancies are common malignancies worldwide, with rising incidence. Most skin cancers, including melanoma, can be cured if diagnosed correctly at an early stage. Thus, millions of biopsies are performed annually, posing a major economic burden. Non-invasive skin imaging techniques can aid in early diagnosis and save unnecessary benign biopsies. In this review article, we will discuss in vivo and ex vivo confocal microscopy (CM) techniques that are currently being utilized in dermatology clinics for skin cancer diagnosis. We will discuss their current applications and clinical impact. Additionally, we will provide a comprehensive review of the advances in the field of CM, including multi-modal approaches, the integration of fluorescent targeted dyes, and the role of artificial intelligence for improved diagnosis and management.

Skin Cancer Pathobiology at a Glance: A Focus on Imaging Techniques and Their Potential for Improved Diagnosis and Surveillance in Clinical Cohorts

Early diagnosis is essential for completely eradicating skin cancer and maximizing patients' clinical benefits. Emerging optical imaging modalities such as reflectance confocal microscopy (RCM), optical coherence tomography (OCT), magnetic resonance imaging (MRI), near-infrared (NIR) bioimaging, positron emission tomography (PET), and their combinations provide non-invasive imaging data that may help in the early detection of cutaneous tumors and surgical planning. Hence, they seem appropriate for observing dynamic processes such as blood flow, immune cell activation, and tumor energy metabolism, which may be relevant for disease evolution. This review discusses the latest technological and methodological advances in imaging techniques that may be applied for skin cancer detection and monitoring. In the first instance, we will describe the principle and prospective clinical applications of the most commonly used imaging techniques, highlighting the challenges and opportunities of their implementation in the clinical setting. We will also highlight how imaging techniques may complement the molecular and histological approaches in sharpening the non-invasive skin characterization, laying the ground for more personalized approaches in skin cancer patients.

Imaging technologies for presurgical margin assessment of basal cell carcinoma

Basal cell carcinoma is the most common cancer worldwide, necessitating the development of techniques to decrease treatment costs through efficiency and efficacy. Mohs micrographic surgery, a specialized surgical technique involving staged resection of the tumor with complete histologic evaluation of the peripheral margins, is highly utilized. Reducing stages by even 5% to 10% would result in significant improvement in care and economic benefits. Noninvasive imaging could aid in both establishing the diagnosis of suspicious skin lesions and streamlining the surgical management of skin cancers by improving presurgical estimates of tumor sizes. Herein, we review the current state of imaging techniques in dermatology and their applications for diagnosis and tumor margin assessment of basal cell carcinoma prior to Mohs micrographic surgery.

Intensity-based nonrigid endomicroscopic image mosaicking incorporating texture relevance for compensation of tissue deformation

Image mosaicking has emerged as a universal technique to broaden the field-of-view of the probe-based confocal laser endomicroscopy (pCLE) imaging system. However, due to the influence of probe-tissue contact forces and optical components on imaging quality, existing mosaicking methods remain insufficient to deal with practical challenges. In this paper, we present the texture encoded sum of conditional variance (TESCV) as a novel similarity metric, and effectively incorporate it into a sequential mosaicking scheme to simultaneously correct rigid probe shift and nonrigid tissue deformation. TESCV combines both intensity dependency and texture relevance to quantify the differences between pCLE image frames, where a discriminative binary descriptor named fully cross-detected local derivative pattern (FCLDP) is designed to extract more detailed structural textures. Furthermore, we also analytically derive the closed-form gradient of TESCV with respect to the transformation variables. Experiments on the circular dataset highlighted the advantage of the TESCV metric in improving mosaicking performance compared with the other four recently published metrics. The comparison with the other four state-of-the-art mosaicking methods on the spiral and manual datasets indicated that the proposed TESCV-based method not only worked stably at different contact forces, but was also suitable for both low- and high-resolution imaging systems. With more accurate and delicate mosaics, the proposed method holds promises to meet clinical demands for intraoperative optical biopsy.

Intensity-Mosaic: automatic panorama mosaicking of disordered images with insufficient features

Purpose: Handling low-quality and few-feature medical images is a challenging task in automatic panorama mosaicking. Current mosaicking methods for disordered input images are based on feature point matching, whereas in this case intensity-based registration achieves better performance than feature-point registration methods. We propose a mosaicking method that enables the use of mutual information (MI) registration for mosaicking randomly ordered input images with insufficient features. Approach: Dimensionality reduction is used to map disordered input images into a low dimensional space. Based on the low dimensional representation, the image global correspondence can be recognized efficiently. For adjacent image pairs, we optimize the MI metric for registration. The panorama is then created after image blending. We demonstrate our method on relatively lower-cost handheld devices that acquire images from the retina in vivo, kidney ex vivo, and bladder phantom, all of which contain sparse features. Results: Our method is compared with three baselines: AutoStitch, "dimension reduction + SIFT," and "MI-Only." Our method compared to the first two feature-point based methods exhibits 1.25 (ex vivo microscope dataset) to two times (in vivo retina dataset) rate of mosaic completion, and MI-Only has the lowest complete rate among three datasets. When comparing the subsequent complete mosaics, our target registration errors can be 2.2 and 3.8 times reduced when using the microscopy and bladder phantom datasets. Conclusions: Using dimensional reduction increases the success rate of detecting adjacent images, which makes MI-based registration feasible and narrows the search range of MI optimization. To the best of our knowledge, this is the first mosaicking method that allows automatic stitching of disordered images with intensity-based alignment, which provides more robust and accurate results when there are insufficient features for classic mosaicking methods.

Real-Time Nonrigid Mosaicking of Laparoscopy Images

The ability to extend the field of view of laparoscopy images can help the surgeons to obtain a better understanding of the anatomical context. However, due to tissue deformation, complex camera motion and significant three-dimensional (3D) anatomical surface, image pixels may have non-rigid deformation and traditional mosaicking methods cannot work robustly for laparoscopy images in real-time. To solve this problem, a novel two-dimensional (2D) non-rigid simultaneous localization and mapping (SLAM) system is proposed in this paper, which is able to compensate for the deformation of pixels and perform image mosaicking in real-time. The key algorithm of this 2D non-rigid SLAM system is the expectation maximization and dual quaternion (EMDQ) algorithm, which can generate smooth and dense deformation field from sparse and noisy image feature matches in real-time. An uncertainty-based loop closing method has been proposed to reduce the accumulative errors. To achieve real-time performance, both CPU and GPU parallel computation technologies are used for dense mosaicking of all pixels. Experimental results on in vivo and synthetic data demonstrate the feasibility and accuracy of our non-rigid mosaicking method.

TEM image restoration from fast image streams

Microscopy imaging experiments generate vast amounts of data, and there is a high demand for smart acquisition and analysis methods. This is especially true for transmission electron microscopy (TEM) where terabytes of data are produced if imaging a full sample at high resolution, and analysis can take several hours. One way to tackle this issue is to collect a continuous stream of low resolution images whilst moving the sample under the microscope, and thereafter use this data to find the parts of the sample deemed most valuable for high-resolution imaging. However, such image streams are degraded by both motion blur and noise. Building on deep learning based approaches developed for deblurring videos of natural scenes we explore the opportunities and limitations of deblurring and denoising images captured from a fast image stream collected by a TEM microscope. We start from existing neural network architectures and make adjustments of convolution blocks and loss functions to better fit TEM data. We present deblurring results on two real datasets of images of kidney tissue and a calibration grid. Both datasets consist of low quality images from a fast image stream captured by moving the sample under the microscope, and the corresponding high quality images of the same region, captured after stopping the movement at each position to let all motion settle. We also explore the generalizability and overfitting on real and synthetically generated data. The quality of the restored images, evaluated both quantitatively and visually, show that using deep learning for image restoration of TEM live image streams has great potential but also comes with some limitations.

Robust Mosaicing of Endomicroscopic Videos via Context-Weighted Correlation Ratio

Probe-based confocal laser endomicroscopy (pCLE) is a promising imaging tool that provides in situ and in vivo optical imaging to perform real-time pathological assessments. However, due to limited field of view, it is difficult for clinicians to get a full understanding of the scanned tissues. In this paper, we develop a novel mosaicing framework to assemble all frame sequences into a full view image. First, a hybrid rigid registration that combines feature matching and template matching is presented to achieve a global alignment of all frames. Then, the parametric free-form deformation (FFD) model with a multiresolution architecture is implemented to accommodate non-rigid tissue distortions. More importantly, we devise a robust similarity metric called context-weighted correlation ratio (CWCR) to promote registration accuracy, where spatial and geometric contexts are incorporated into the estimation of functional intensity dependence. Experiments on both robotic setup and manual manipulation have demonstrated that the proposed scheme significantly precedes some state-of-the-art mosaicing schemes in the presence of intensity fluctuations, insufficient overlap and tissue distortions. Moreover, the comparisons of the proposed CWCR metric and two other metrics have validated the effectiveness of the context-weighted strategy in quantifying the differences between two frames. Benefiting from more rational and delicate mosaics, the proposed scheme is more suitable to instruct diagnosis and treatment during optical biopsies.

Real-time video mosaicking to guide handheld in vivo microscopy

Handheld and endoscopic optical-sectioning microscopes are being developed for noninvasive screening and intraoperative consultation. Imaging a large extent of tissue is often desired, but miniature in vivo microscopes tend to suffer from limited fields of view. To extend the imaging field during clinical use, we have developed a real-time video mosaicking method, which allows users to efficiently survey larger areas of tissue. Here, we modified a previous post-processing mosaicking method so that real-time mosaicking is possible at >30 frames/second when using a device that outputs images that are 400 × 400 pixels in size. Unlike other real-time mosaicking methods, our strategy can accommodate image rotations and deformations that often occur during clinical use of a handheld microscope. We perform a feasibility study to demonstrate that the use of real-time mosaicking is necessary to enable efficient sampling of a desired imaging field when using a handheld dual-axis confocal microscope.

Software-Based Phase Control, Video-Rate Imaging, and Real-Time Mosaicing With a Lissajous-Scanned Confocal Microscope

We present software-based methods for automatic phase control and for mosaicing high-speed, Lissajous-scanned images. To achieve imaging speeds fast enough for mosaicing, we first increase the image update rate tenfold from 3 to 30 Hz, then vertically interpolate each sparse image in real-time to eliminate fixed pattern noise. We validate our methods by imaging fluorescent beads and automatically maintaining phase control over the course of one hour. We then image fixed mouse brain tissues at varying update rates and compare the resulting mosaics. Using reconstructed image data as feedback for phase control eliminates the need for phase sensors and feedback controllers, enabling long-term imaging experiments without additional hardware. Mosaicing subsampled images results in video-rate imaging speeds, nearly fully recovered spatial resolution, and millimeter-scale fields of view.

Multichannel correlation improves the noise tolerance of real-time hyperspectral microimage mosaicking

Live-subject microscopies, including microendoscopy and other related technologies, offer promise for basic biology research as well as the optical biopsy of disease in the clinic. However, cellular resolution generally comes with the trade-off of a microscopic field-of-view. Microimage mosaicking enables stitching many small scenes together to aid visualization, quantitative interpretation, and mapping of microscale features, for example, to guide surgical intervention. The development of hyperspectral and multispectral systems for biomedical applications provides motivation for adapting mosaicking algorithms to process a number of simultaneous spectral channels. We present an algorithm that mosaics multichannel video by correlating channels of consecutive frames as a basis for efficiently calculating image alignments. We characterize the noise tolerance of the algorithm by using simulated video with known ground-truth alignments to quantify mosaicking accuracy and speed, showing that multiplexed molecular imaging enhances mosaic accuracy by leveraging observations of distinct molecular constituents to inform frame alignment. A simple mathematical model is introduced to characterize the noise suppression provided by a given group of spectral channels, thus predicting the performance of selected subsets of data channels in order to balance mosaic computation accuracy and speed. The characteristic noise tolerance of a given number of channels is shown to improve through selection of an optimal subset of channels that maximizes this model. We also demonstrate that the multichannel algorithm produces higher quality mosaics than the analogous single-channel methods in an empirical test case. To compensate for the increased data rate of hyperspectral video compared to single-channel systems, we employ parallel processing via GPUs to alleviate computational bottlenecks and to achieve real-time mosaicking even for video-rate multichannel systems anticipated in the future. This implementation paves the way for real-time multichannel mosaicking to accompany next-generation hyperspectral and multispectral video microscopy.

Confidence-based dynamic optimization model for biomedical image mosaicking

Biomedical image mosaicking is a trending topic. It consists of computing a single large image from multiple observations and becomes a challenging task when said observations barely overlap or are subject to illumination changes, poor resolution, blur, or either highly textured or predominantly homogeneous content. Because such challenges are common in biomedical images, classical keypoint/feature-based methods perform poorly. In this paper, we propose a new framework based on pairwise template matching coupled with a constrained, confidence-driven global optimization strategy to solve the issue of microscopic biomedical image mosaicking. First we provide experimental results that show significant improvement on a subjective level. Then we describe a new validation strategy for objective assessment, which shows improvement as well.

Evaluation of a Combined Reflectance Confocal Microscopy-Optical Coherence Tomography Device for Detection and Depth Assessment of Basal Cell Carcinoma

Importance

The limited tissue sampling of a biopsy can lead to an incomplete assessment of basal cell carcinoma (BCC) subtypes and depth. Reflectance confocal microscopy (RCM) combined with optical coherence tomography (OCT) imaging may enable real-time, noninvasive, comprehensive three-dimensional sampling in vivo, which may improve the diagnostic accuracy and margin assessment of BCCs.

Objective

To determine the accuracy of a combined RCM-OCT device for BCC detection and deep margin assessment.

Design, Setting, and Participants

This pilot study was carried out on 85 lesions from 55 patients referred for physician consultation or Mohs surgery at Memorial Sloan Kettering Skin Cancer Center in Hauppauge, New York. These patients were prospectively and consecutively enrolled in the study between January 1, 2017, and December 31, 2017. Patients underwent imaging, with the combined RCM-OCT probe, for previously biopsied, histopathologically confirmed BCCs and lesions clinically or dermoscopically suggestive of BCC. Only patients with available histopathologic examination after imaging were included.

Main Outcomes and Measures

Improvements in sensitivity, specificity, and diagnostic accuracy for BCC using the combined RCM-OCT probe as well as the correlation between OCT-estimated depth and histopathologically measured depth were investigated.

Results

In total, 85 lesions from 55 patients (27 [49%] were female and 28 [51%] were male with a median [range] age of 59 [21-90] years) were imaged. Imaging was performed on 25 previously biopsied and histopathologically confirmed BCCs and 60 previously nonbiopsied but clinically or dermoscopically suspicious lesions. Normal skin and BCC features were correlated and validated with histopathologic examination. In previously biopsied lesions, residual tumors were detected in 12 of 25 (48%) lesions with 100% sensitivity (95% CI, 73.5%-100%) and 23.1% specificity (95% CI, 5.0%-53.8%) for combined RCM-OCT probe. In previously nonbiopsied and suspicious lesions, BCCs were diagnosed in 48 of 60 (80%) lesions with 100% sensitivity (95% CI, 92.6%-100%) and 75% specificity (95% CI, 42.8%-94.5%). Correlation was observed between depth estimated with OCT and depth measured with histopathologic examination: the coefficient of determination (R2) was 0.75 (R = 0.86; P < .001) for all lesions, 0.73 (R = 0.85; P < .001) for lesions less than 500 μm deep, and 0.65 (R = 0.43; P < .001) for lesions greater than 500 μm deep.

Conclusions and Relevance

Combined RCM-OCT imaging may be prospectively used to comprehensively diagnose lesions suggestive of BCC and triage for treatment. Further validation of this device must be performed on a larger cohort.

High-resolution microscopy for imaging cancer pathobiology

PURPOSE OF REVIEW

Light microscopy plays an essential role in clinical diagnosis and understanding the pathogenesis of cancer. Conventional bright-field microscope is used to visualize abnormality in tissue architecture and nuclear morphology, but often suffers from many limitations. This review focuses on the potential of new imaging techniques to improve basic and clinical research in pathobiology.

RECENT FINDINGS

Light microscopy has significantly expanded its ability in resolution, imaging volume, speed and contrast. It now allows 3D high-resolution volumetric imaging of tissue architecture from large tissue and molecular structures at nanometer resolution.

SUMMARY

Pathologists and researchers now have access to various imaging tools to study cancer pathobiology in both breadth and depth. Although clinical adoption of a new technique is slow, the new imaging tools will provide significant new insights and open new avenues for improving early cancer detection, personalized risk assessment and identifying the best treatment strategies.

Toward Quantitative Neurosurgical Guidance With High-Resolution Microscopy of 5-Aminolevulinic Acid-Induced Protoporphyrin IX

Low-power fluorescence microscopy of 5-ALA-induced PpIX has emerged as a valuable intraoperative imaging technology for improving the resection of malignant gliomas. However, current fluorescence imaging tools are not highly sensitive nor quantitative, which limits their effectiveness for optimizing operative decisions near the surgical margins of gliomas, in particular non-enhancing low-grade gliomas. Intraoperative high-resolution optical-sectioning microscopy can potentially serve as a valuable complement to low-power fluorescence microscopy by providing reproducible quantification of tumor parameters at the infiltrative margins of diffuse gliomas. In this forward-looking perspective article, we provide a brief discussion of recent technical advancements, pilot clinical studies, and our vision of the future adoption of handheld optical-sectioning microscopy at the final stages of glioma surgeries to enhance the extent of resection. We list a number of challenges for clinical acceptance, as well as potential strategies to overcome such obstacles for the surgical implementation of these in vivo microscopy techniques.

Feasibility of a Video-Mosaicking Approach to Extend the Field-of-View For Reflectance Confocal Microscopy in the Oral Cavity In Vivo

BACKGROUND

Reflectance confocal microscopy (RCM) is a developing approach for noninvasive detection of oral lesions with label-free contrast and cellular-level resolution. For access into the oral cavity, confocal microscopes are being configured with small-diameter telescopic probes and small objective lenses. However, a small probe and objective lens allows for a rather small field-of-view relative to the large areas of tissue that must be examined for diagnosis. To extend the field-of-view for intraoral RCM imaging, we are investigating a video-mosaicking approach.

METHODS

A relay telescope and objective lens were adapted to an existing confocal microscope for access into the oral cavity. Imaging was performed using metal three-dimensional-printed objective lens front-end caps with coverslip windows to contact and stabilize the tissue and set depth. Four healthy volunteers (normal oral mucosa), one patient (with an amalgam tattoo) in a clinical setting, and 20 anesthetized patients (with oral squamous cell carcinoma [OSCC]) in a surgical setting were imaged. Instead of the usual still RCM images, videos were recorded and then processed into video-mosaics. Thirty video-mosaics were read and qualitatively assessed by an expert reader of RCM images of the oral mucosa.

RESULTS

Whereas the objective lens' native field-of-view is 0.75 mm × 0.75 mm, the video-mosaics display larger areas, ranging from 2 mm × 2 mm to 4 mm × 2 mm, with resolution, morphologic detail, and image quality that is preserved relative to that observed in the original videos (individual images). Video-mosaics in healthy volunteers' and the patients' images showed cellular morphologic patterns in the lower epithelium and at the epithelial junction, and connective tissue along with capillary loops and blood flow in the deeper lamina propria. In OSCC, tumor nests could be observed along with normal looking mucosa in margin areas.

CONCLUSIONS

Video-mosaicking is a reasonably quick and efficient approach for extending the field-of-view of RCM imaging, which can, to some extent, overcome the inherent limitation of an intraoral probe's small field-of-view. Reading video-mosaics can mimic the procedure for examining pathology: initial visualization of the spatial cellular and morphologic patterns of the tumor and the spread of tumor margins over larger areas of the lesion, followed by digitally zooming (magnifying) for closer inspection of suspicious areas. However, faster processing of videos into video-mosaics will be necessary, to allow examination of video-mosaics in real-time at the bedside. Lasers Surg. Med. 51:439-451, 2019. © 2019 Wiley Periodicals, Inc.

Label-free in vivo pathology of human epithelia with a high-speed handheld dual-axis confocal microscope

There would be clinical value in a miniature optical-sectioning microscope to enable in vivo interrogation of tissues as a real-time and noninvasive alternative to gold-standard histopathology for early disease detection and surgical guidance. To address this need, a reflectance-based handheld line-scanned dual-axis confocal microscope was developed and fully packaged for label-free imaging of human skin and oral mucosa. This device can collect images at >15  frames/s with an optical-sectioning thickness and lateral resolution of 1.7 and 1.1  μm, respectively. Incorporation of a sterile lens cap design enables pressure-sensitive adjustment of the imaging depth by the user during clinical use. In vivo human images and videos are obtained to demonstrate the capabilities of this high-speed optical-sectioning microscopy device.

Peri-operative delineation of non-melanoma skin cancer margins in vivo with handheld reflectance confocal microscopy and video-mosaicking

BACKGROUND

The surgical removal of non-melanoma skin cancers (NMSCs) is guided by the pathologic examination of margins. However, the preparation of histopathology is time consuming, labour-intensive and requires separate laboratory infrastructure. Furthermore, when histopathology indicates positive margins, patients must return for re-excisions. Reflectance confocal microscopy (RCM) with a new video-mosaicking approach can noninvasively delineate margins directly on patients and potentially guide surgery in real-time, augmenting the traditional approaches of histopathology.

OBJECTIVE

To assess a new peri-operative RCM video-mosaicking approach for comprehensive delineation of NMSC margins on patients in vivo.

METHODS

Thirty-five patients undergoing Mohs micrographic surgery (MMS) in the Mohs surgery unit at Memorial Sloan Kettering Cancer Center, New York, NY were included in the study. RCM imaging was performed before and after the first staged excision by acquiring videos along the surgical margins (epidermal, peripheral and deep dermal) of each wound, which were subsequently processed into video-mosaics. Two RCM evaluators read and assessed video-mosaics, and subsequently compared to the corresponding Mohs frozen histopathology.

RESULTS

Reflectance confocal microscopy videos and video-mosaics displayed acceptable imaging quality (resolution and contrast), pre-operatively in 32/35 (91%) NMSC lesions and intra-operatively in 29/35 lesions (83%). Pre-operative delineation of margins correlated with the histopathology in 32/35 (91%) lesions. Intra-operative delineation correlated in 10/14 (71%) lesions for the presence of residual tumour and in 18/21 (86%) lesions for absence. Sensitivity/specificity were 71%/86% and 86%/81% for two RCM video-mosaic evaluators, and overall agreement was 80% and 83% with histopathology, with moderate inter-evaluator agreement (k = 0.59, P ≤ 0.0002).

CONCLUSIONS

Peri-operative RCM video-mosaicking of NMSC margins directly on patients may potentially guide surgery in real-time, serve as an adjunct to histopathology, reduce time spent in clinic and reduce the need for re-excisions. Further testing in larger studies is needed.

Diagnostic accuracy of reflectance confocal microscopy for pigmented skin lesions presenting dermoscopic features of cutaneous melanoma

Introduction

The incidence and mortality of melanoma are rising rapidly. Despite ongoing research and the introduction of new therapeutic methods, advanced melanoma is still considered incurable. Early detection and surgical excision of the tumor increases patients’ survival. Since the diagnostic protocol includes surgical excision of all suspicious lesions, it is burdened with a high rate of unnecessary excisions that cause unwanted scarring. This is why the development of accurate diagnostic techniques is crucial. The most common diagnostic tool in early diagnosis of cutaneous melanoma is dermoscopy, though there are emerging new techniques, such as reflectance confocal microscopy and optical coherence tomography.

Aim

To evaluate diagnostic accuracy of reflectance confocal microscopy as a secondary examination in melanocytic lesions previously diagnosed as melanomas by means of dermoscopy.

Material and methods

Forty-six melanocytic lesions presenting dermoscopic features of cutaneous malignant melanoma were examined by means of reflectance confocal microscopy.

Results

The RCM evaluation showed sensitivity at the level of 100% and specificity at 62%.

Conclusions

It can be estimated that double evaluation of melanocytic lesions by dermoscopy and reflectance confocal microscopy may allow up to 62% of unnecessary excisions to be avoided.

Advances in the use of reflectance confocal microscopy in melanoma

In vivo reflectance confocal microscopy (RCM) is a noninvasive high-resolution skin imaging tool that has become an important adjunct to clinical exam, dermoscopy and histopathology assessment, in the diagnosis and management of melanoma. RCM generates a horizontal view of the skin, whereby cellular and subcellular (e.g., nuclei, melanophages, collagen) structures, to the level of the upper dermis, are projected onto a screen at near-histological resolution. Morphologic descriptors, standardized terminology, and diagnostic algorithms are well established for the RCM assessment of melanoma, melanocytic, and nonmelanocytic lesions. Clinical applications of RCM in melanoma are broad and include diagnosis, assessment of large lesions on cosmetically sensitive areas, directing areas to biopsy, delineating margins prior to surgery, detecting response to treatment and assessing recurrence. This review will provide an overview of RCM technology, findings by melanoma subtype, clinical applications, as well as explore the accuracy of RCM for melanoma diagnosis, pitfalls and emerging uses of this technology ex vivo.

Smartphone confocal microscopy for imaging cellular structures in human skin in vivo

We report development of a low-cost smartphone confocal microscope and its first demonstration of in vivo human skin imaging. The smartphone confocal microscope uses a slit aperture and diffraction grating to conduct two-dimensional confocal imaging without using any beam scanning devices. Lateral and axial resolutions of the smartphone confocal microscope were measured as 2 and 5 µm, respectively. In vivo confocal images of human skin revealed characteristic cellular structures, including spinous and basal keratinocytes and papillary dermis. Results suggest that the smartphone confocal microscope has a potential to examine cellular details in vivo and may help disease diagnosis in resource-poor settings, where conducting standard histopathologic analysis is challenging.