Melanoma and Nevi Subtype Histopathological Characterization with Optical Coherence Tomography.

BACKGROUND: Melanoma incidence has continued to rise in the latest decades, and the forecast is not optimistic. Non-invasive diagnostic imaging techniques such as optical coherence tomography (OCT) are largely studied; however, there is still no agreement on its use for the diagnosis of melanoma. For dermatologists, the differentiation of non-invasive (junctional nevus, compound nevus, intradermal nevus, and melanoma in-situ) versus invasive (superficial spreading melanoma and nodular melanoma) lesions is the key issue in their daily routine. METHODS: This work performs a comparative analysis of OCT images using haematoxylin-eosin (HE) and anatomopathological features identified by a pathologist. Then, optical and textural properties are extracted from OCT images with the aim to identify subtle features that could potentially maximize the usefulness of the imaging technique in the identification of the lesion's potential invasiveness. RESULTS: Preliminary features reveal differences discriminating melanoma in-situ from superficial spreading melanoma and also between melanoma and nevus subtypes that pose a promising baseline for further research. CONCLUSIONS: Answering the final goal of diagnosing non-invasive versus invasive lesions with OCT does not seem feasible in the short term, but the obtained results demonstrate a step forward to achieve this.

Modeling and Synthesis of Breast Cancer Optical Property Signatures With Generative Models.

Is it possible to find deterministic relationships between optical measurements and pathophysiology in an unsupervised manner and based on data alone? Optical property quantification is a rapidly growing biomedical imaging technique for characterizing biological tissues that shows promise in a range of clinical applications, such as intraoperative breast-conserving surgery margin assessment. However, translating tissue optical properties to clinical pathology information is still a cumbersome problem due to, amongst other things, inter- and intrapatient variability, calibration, and ultimately the nonlinear behavior of light in turbid media. These challenges limit the ability of standard statistical methods to generate a simple model of pathology, requiring more advanced algorithms. We present a data-driven, nonlinear model of breast cancer pathology for real-time margin assessment of resected samples using optical properties derived from spatial frequency domain imaging data. A series of deep neural network models are employed to obtain sets of latent embeddings that relate optical data signatures to the underlying tissue pathology in a tractable manner. These self-explanatory models can translate absorption and scattering properties measured from pathology, while also being able to synthesize new data. The method was tested on a total of 70 resected breast tissue samples containing 137 regions of interest, achieving rapid optical property modeling with errors only limited by current semi-empirical models, allowing for mass sample synthesis and providing a systematic understanding of dataset properties, paving the way for deep automated margin assessment algorithms using structured light imaging or, in principle, any other optical imaging technique seeking modeling. Code is available.

Context-free hyperspectral image enhancement for wide-field optical biomarker visualization.

Many well-known algorithms for the color enhancement of hyperspectral measurements in biomedical imaging are based on statistical assumptions that vary greatly with respect to the proportions of different pixels that appear in a given image, and thus may thwart their application in a surgical environment. This article attempts to explain why this occurs with SVD-based enhancement methods, and proposes the separation of spectral enhancement from analysis. The resulting method, termed affinity-based color enhancement, or ACE for short, achieves multi- and hyperspectral image coloring and contrast based on current spectral affinity metrics that can physically relate spectral data to a particular biomarker. This produces tunable, real-time results which are analogous to the current state-of-the-art algorithms, without suffering any of their inherent context-dependent limitations. Two applications of this method are shown as application examples: vein contrast enhancement and high-precision chromophore concentration estimation.

Custom Scanning Hyperspectral Imaging System for Biomedical Applications: Modeling, Benchmarking, and Specifications.

Prototyping hyperspectral imaging devices in current biomedical optics research requires taking into consideration various issues regarding optics, imaging, and instrumentation. In summary, an ideal imaging system should only be limited by exposure time, but there will be technological limitations (e.g., actuator delay and backlash, network delays, or embedded CPU speed) that should be considered, modeled, and optimized. This can be achieved by constructing a multiparametric model for the imaging system in question. The article describes a rotating-mirror scanning hyperspectral imaging device, its multiparametric model, as well as design and calibration protocols used to achieve its optimal performance. The main objective of the manuscript is to describe the device and review this imaging modality, while showcasing technical caveats, models and benchmarks, in an attempt to simplify and standardize specifications, as well as to incentivize prototyping similar future designs.

On the spectral signature of melanoma: a non-parametric classification framework for cancer detection in hyperspectral imaging of melanocytic lesions.

Early detection and diagnosis is a must in secondary prevention of melanoma and other cancerous lesions of the skin. In this work, we present an online, reservoir-based, non-parametric estimation and classification model that allows for this functionality on pigmented lesions, such that detection thresholding can be tuned to maximize accuracy and/or minimize overall false negative rates. This system has been tested in a dataset consisting of 116 patients and a total of 124 hyperspectral images of nevi, raised nevi and melanomas, detecting up to 100% of the suspicious lesions at the expense of some false positives.

Directional Kernel Density Estimation for Classification of Breast Tissue Spectra.

In Breast Conserving Therapy, surgeons measure the thickness of healthy tissue surrounding an excised tumor (surgical margin) via post-operative histological or visual assessment tests that, for lack of enough standardization and reliability, have recurrence rates in the order of 33%. Spectroscopic interrogation of these margins is possible during surgery, but algorithms are needed for parametric or dimension reduction processing. One methodology for tumor discrimination based on dimensionality reduction and nonparametric estimation-in particular, Directional Kernel Density Estimation-is proposed and tested on spectral image data from breast samples. Once a hyperspectral image of the tumor has been captured, a surgeon assists by establishing Regions of Interest where tissues are qualitatively differentiable. After proper normalization, Directional KDE is used to estimate the likelihood of every pixel in the image belonging to each specified tissue class. This information is enough to yield, in almost real time and with 98% accuracy, results that coincide with those provided by histological H&E validation performed after the surgery.

Hessian analysis for the delineation of amorphous anomalies in optical coherence tomography images of the aortic wall.

The aortic aneurysm is a disease originated mainly in the media layer of the aortic wall due to the occurrence of degraded areas of altered biological composition. These anomalous regions affect the structure and strength of the aorta artery, being their occurrence and extension proportional to the arterial vessel health. Optical Coherence Tomography (OCT) is applied to obtain cross-sectional images of the artery wall. The backscattering mechanisms in tissue make aorta images difficult to analyze due to noise and strong attenuation with penetration. The morphology of anomalies in pathological specimens is also diverse with amorphous shapes and varied dimensions, being these factors strongly related with tissue degradation and the aorta physiological condition. Hessian analysis of OCT images from aortic walls is used to assess the accurate delineation of these anomalous regions. A specific metric of the Hessian determinant is used to delineate degraded regions under blurry conditions and noise. A multiscale approach, based on an anisotropic Gaussian kernel filter, is applied to highlight and aggregate all the heterogeneity present in the aortic wall. An accuracy estimator metric has been implemented to evaluate and optimize the delineation process avoiding subjectivity. Finally, a degradation quantification score has been developed to assess aorta wall condition by OCT with validation against common histology.

Identification of vessel wall degradation in ascending thoracic aortic aneurysms with OCT.

Degradation of the wall of human ascending thoracic aorta has been assessed through Optical Coherence Tomography (OCT). OCT images of the media layer of the aortic wall exhibit micro-structure degradation in case of diseased aortas from aneurysmal vessels. The OCT indicator of degradation depends on the dimension of areas of the media layer where backscattered reflectivity becomes smaller due to a disorder on the morphology of elastin, collagen and smooth muscle cells (SMCs). Efficient pre-processing of the OCT images is required to accurately extract the dimension of degraded areas after an optimized thresholding procedure. OCT results have been validated against conventional histological analysis. The OCT qualitative assessment has achieved a pair sensitivity-specificity of 100%-91.6% in low-high degradation discrimination when a threshold of 4965.88µm(2) is selected. This threshold suggests to have physiological meaning. The OCT quantitative evaluation of degradation achieves a correlation of 0.736 between the OCT indicator and the histological score. This in-vitro study can be transferred to the clinical scenario to provide an intraoperative assessment tool to guide cardiovascular surgeons in open repair interventions.

Optical coherence tomography assessment of vessel wall degradation in thoracic aortic aneurysms.

Optical coherence tomography images of human thoracic aorta from aneurysms reveal elastin disorders and smooth muscle cell alterations when visualizing the media layer of the aortic wall. These disorders can be employed as indicators for wall degradation and, therefore, become a hallmark for diagnosis of risk of aneurysm under intraoperative conditions. Two approaches are followed to evaluate this risk: the analysis of the reflectivity decay along the penetration depth and the textural analysis of a two-dimensional spatial distribution of the aortic wall backscattering. Both techniques require preprocessing stages for the identification of the air-sample interface and for the segmentation of the media layer. Results show that the alterations in the media layer of the aortic wall are better highlighted when the textural approach is considered and also agree with a semiquantitative histopathological grading that assesses the degree of wall degradation. The correlation of the co-occurrence matrix attains a sensitivity of 0.906 and specificity of 0.864 when aneurysm automatic diagnosis is evaluated with a receiver operating characteristic curve.

Direct identification of breast cancer pathologies using blind separation of label-free localized reflectance measurements.

Breast tumors are blindly identified using Principal (PCA) and Independent Component Analysis (ICA) of localized reflectance measurements. No assumption of a particular theoretical model for the reflectance needs to be made, while the resulting features are proven to have discriminative power of breast pathologies. Normal, benign and malignant breast tissue types in lumpectomy specimens were imaged ex vivo and a surgeon-guided calibration of the system is proposed to overcome the limitations of the blind analysis. A simple, fast and linear classifier has been proposed where no training information is required for the diagnosis. A set of 29 breast tissue specimens have been diagnosed with a sensitivity of 96% and specificity of 95% when discriminating benign from malignant pathologies. The proposed hybrid combination PCA-ICA enhanced diagnostic discrimination, providing tumor probability maps, and intermediate PCA parameters reflected tissue optical properties.

Combined fluorescence and reflectance spectroscopy for in vivo quantification of cancer biomarkers in low- and high-grade glioma surgery.

Biomarkers are indicators of biological processes and hold promise for the diagnosis and treatment of disease. Gliomas represent a heterogeneous group of brain tumors with marked intra- and inter-tumor variability. The extent of surgical resection is a significant factor influencing post-surgical recurrence and prognosis. Here, we used fluorescence and reflectance spectral signatures for in vivo quantification of multiple biomarkers during glioma surgery, with fluorescence contrast provided by exogenously-induced protoporphyrin IX (PpIX) following administration of 5-aminolevulinic acid. We performed light-transport modeling to quantify multiple biomarkers indicative of tumor biological processes, including the local concentration of PpIX and associated photoproducts, total hemoglobin concentration, oxygen saturation, and optical scattering parameters. We developed a diagnostic algorithm for intra-operative tissue delineation that accounts for the combined tumor-specific predictive capabilities of these quantitative biomarkers. Tumor tissue delineation achieved accuracies of up to 94% (specificity = 94%, sensitivity = 94%) across a range of glioma histologies beyond current state-of-the-art optical approaches, including state-of-the-art fluorescence image guidance. This multiple biomarker strategy opens the door to optical methods for surgical guidance that use quantification of well-established neoplastic processes. Future work would seek to validate the predictive power of this proof-of-concept study in a separate larger cohort of patients.

Automated classification of breast pathology using local measures of broadband reflectance.

We demonstrate that morphological features pertinent to a tissue's pathology may be ascertained from localized measures of broadband reflectance, with a mesoscopic resolution (100-µm lateral spot size) that permits scanning of an entire margin for residual disease. The technical aspects and optimization of a k-nearest neighbor classifier for automated diagnosis of pathologies are presented, and its efficacy is validated in 29 breast tissue specimens. When discriminating between benign and malignant pathologies, a sensitivity and specificity of 91 and 77% was achieved. Furthermore, detailed subtissue-type analysis was performed to consider how diverse pathologies influence scattering response and overall classification efficacy. The increased sensitivity of this technique may render it useful to guide the surgeon or pathologist where to sample pathology for microscopic assessment.

Feasibility study of imaging spectroscopy to monitor the quality of online welding.

An online welding quality system based on the use of imaging spectroscopy is proposed and discussed. Plasma optical spectroscopy has already been successfully applied in this context by establishing a direct correlation between some spectroscopic parameters, e.g., the plasma electronic temperature and the resulting seam quality. Given that the use of the so-called hyperspectral devices provides both spatial and spectral information, we propose their use for the particular case of arc welding quality monitoring in an attempt to determine whether this technique would be suitable for this industrial situation. Experimental welding tests are presented, and the ability of the proposed solution to identify simulated defects is proved. Detailed spatial analyses suggest that this additional dimension can be used to improve the performance of the entire system.

Automated identification of tumor microscopic morphology based on macroscopically measured scatter signatures.

An automated algorithm and methodology is presented to identify tumor-tissue morphologies based on broadband scatter data measured by raster scan imaging of the samples. A quasi-confocal reflectance imaging system was used to directly measure the tissue scatter reflectance in situ, and the spectrum was used to identify the scattering power, amplitude, and total wavelength-integrated intensity. Pancreatic tumor and normal samples were characterized using the instrument, and subtle changes in the scatter signal were encountered within regions of each sample. Discrimination between normal versus tumor tissue was readily performed using a K-nearest neighbor classifier algorithm. A similar approach worked for regions of tumor morphology when statistical preprocessing of the scattering parameters was included to create additional data features. This type of automated interpretation methodology can provide a tool for guiding surgical resection in areas where microscopy imaging cannot be realized efficiently by the surgeon. In addition, the results indicate important design changes for future systems.

Multi-Line Fit Model for the Detection of Methane at ν(2) + 2ν(3) Band using Hollow-Core Photonic Bandgap Fibres.

Hollow-core photonic bandgap fibres (HC-PBFs) have emerged as a novel technology in the field of gas sensing. The long interaction pathlengths achievable with these fibres are especially advantageous for the detection of weakly absorbing gases. In this work, we demonstrate the good performance of a HC-PBF in the detection of the ν(2) + 2ν(3) band of methane, at 1.3 µm. The Q-branch manifold, at 1331.55 nm, is targeted for concentration monitoring purposes. A computationally optimized multi-line model is used to fit the Q-branch. Using this model, a detection limit of 98 ppmv (parts per million by volume) is estimated.

Defect Detection in Arc-Welding Processes by Means of the Line-to-Continuum Method and Feature Selection.

Plasma optical spectroscopy is widely employed in on-line welding diagnostics. The determination of the plasma electron temperature, which is typically selected as the output monitoring parameter, implies the identification of the atomic emission lines. As a consequence, additional processing stages are required with a direct impact on the real time performance of the technique. The line-to-continuum method is a feasible alternative spectroscopic approach and it is particularly interesting in terms of its computational efficiency. However, the monitoring signal highly depends on the chosen emission line. In this paper, a feature selection methodology is proposed to solve the uncertainty regarding the selection of the optimum spectral band, which allows the employment of the line-to-continuum method for on-line welding diagnostics. Field test results have been conducted to demonstrate the feasibility of the solution.

Gas Sensor Based on Photonic Crystal Fibres in the 2ν(3) and ν(2) + 2ν(3) Vibrational Bands of Methane.

In this work, methane detection is performed on the 2ν(3) and ν(2) + 2ν(3) absorption bands in the Near-Infrared (NIR) wavelength region using an all-fibre optical sensor. Hollow-core photonic bandgap fibres (HC-PBFs) are employed as gas cells due to their compactness, good integrability in optical systems and feasibility of long interaction lengths with gases. Sensing in the 2ν(3) band of methane is demonstrated to achieve a detection limit one order of magnitude better than that of the ν(2) + 2ν(3) band. Finally, the filling time of a HC-PBF is demonstrated to be dependent on the fibre length and geometry.

Arc-Welding Spectroscopic Monitoring based on Feature Selection and Neural Networks.

A new spectral processing technique designed for application in the on-line detection and classification of arc-welding defects is presented in this paper. A noninvasive fiber sensor embedded within a TIG torch collects the plasma radiation originated during the welding process. The spectral information is then processed in two consecutive stages. A compression algorithm is first applied to the data, allowing real-time analysis. The selected spectral bands are then used to feed a classification algorithm, which will be demonstrated to provide an efficient weld defect detection and classification. The results obtained with the proposed technique are compared to a similar processing scheme presented in previous works, giving rise to an improvement in the performance of the monitoring system.