Significance: Melanocytic nevi represent the most common dermal melanocytic lesions in humans. Nevus is typically diagnosed clinically with the naked eye or with dermoscopy. However, it is essential to identify the type of nevus by invasive biopsy for histopathological examination. The use of noninvasive imaging tools can be used to evaluate the types of nevi to reduce unnecessary excisions of benign entities.

Aim: To evaluate the feasibility of using en face and cross-sectional full-field optical coherence tomography (FF-OCT) in differentiation of melanocytic nevi that can facilitate the reduction of unnecessary excisions of benign entities.

Approach: Dual-mode Mirau-type FF-OCT for cross-sectional imaging (B-scan) and en face imaging were used to distinguish the types of nevi.

Results: Although the B-scan reveals the distribution of melanosomes, users can set a specific depth of the en face image to explore the morphology of surrounding skin cells instantly. According to the locations of nevus nests, the different types of nevi, including junction nevus and compound nevus, can be identified using this dual-mode FF-OCT system.

Conclusions: Combining B-scan and en face imaging in vivo FF-OCT enables the examination and navigation of skin tissues in real time and in three dimensions.

Significance: Mechanical assessment of the cornea can provide important structural and functional information regarding its health. Current clinically available tools are limited in their efficacy at measuring corneal mechanical properties. Elastography allows for the direct estimation of mechanical properties of tissues in vivo but is generally performed using external excitation force.

Aim: To show that heartbeat optical coherence elastography (Hb-OCE) can be used to assess the mechanical properties of the cornea in vivo.

Approach: Hb-OCE was utilized to detect Hb-induced deformations in the rabbit cornea in vivo without the need for external excitation. Furthermore, we demonstrate how this technique can distinguish corneal stiffness between untreated (UT) and crosslinked (CXL) tissue.

Results: Our results demonstrate that stiffness changes in the cornea can be detected using only the Hb-induced deformations in the cornea. Additionally, we demonstrate a statistically significant difference in strain between the UT and CXL corneas.

Conclusions: Hb-OCE may be an effective tool for assessing the mechanical properties of the cornea in vivo without the need for external excitation. This tool may be effective for clinical assessment of corneal mechanical properties because it only requires optical coherence tomography imaging and data processing.

Significance: Non-contact, camera-based heart rate variability estimation is desirable in numerous applications, including medical, automotive, and entertainment. Unfortunately, camera-based HRV accuracy and reliability suffer due to two challenges: (a) darker skin tones result in lower SNR and (b) relative motion induces measurement artifacts.

Aim: We propose an algorithm HRVCam that provides sufficient robustness to low SNR and motion-induced artifacts commonly present in imaging photoplethysmography (iPPG) signals.

Approach: HRVCam computes camera-based HRV from the instantaneous frequency of the iPPG signal. HRVCam uses automatic adaptive bandwidth filtering along with discrete energy separation to estimate the instantaneous frequency. The parameters of HRVCam use the observed characteristics of HRV and iPPG signals.

Results: We capture a new dataset containing 16 participants with diverse skin tones. We demonstrate that HRVCam reduces the error in camera-based HRV metrics significantly (more than 50% reduction) for videos with dark skin and face motion.

Conclusion: HRVCam can be used on top of iPPG estimation algorithms to provide robust HRV measurements making camera-based HRV practical.

Significance: We demonstrated the potential of using domain adaptation on functional near-infrared spectroscopy (fNIRS) data to classify different levels of n-back tasks that involve working memory.

Aim: Domain shift in fNIRS data is a challenge in the workload level alignment across different experiment sessions and subjects. To address this problem, two domain adaptation approaches—Gromov–Wasserstein (G-W) and fused Gromov–Wasserstein (FG-W) were used.

Approach: Specifically, we used labeled data from one session or one subject to classify trials in another session (within the same subject) or another subject. We applied G-W for session-by-session alignment and FG-W for subject-by-subject alignment to fNIRS data acquired during different n-back task levels. We compared these approaches with three supervised methods: multiclass support vector machine (SVM), convolutional neural network (CNN), and recurrent neural network (RNN).

Results: In a sample of six subjects, G-W resulted in an alignment accuracy of 68  %    ±  4  %   (weighted mean ± standard error) for session-by-session alignment, FG-W resulted in an alignment accuracy of 55  %    ±  2  %   for subject-by-subject alignment. In each of these cases, 25% accuracy represents chance. Alignment accuracy results from both G-W and FG-W are significantly greater than those from SVM, CNN, and RNN. We also showed that removal of motion artifacts from the fNIRS data plays an important role in improving alignment performance.

Conclusions: Domain adaptation has potential for session-by-session and subject-by-subject alignment of mental workload by using fNIRS data.

Significance: The potential of fluorescence lifetime imaging microscopy (FLIM) is recently being recognized, especially in biological studies. However, FLIM does not directly measure the lifetimes, rather it records the fluorescence decay traces. The lifetimes and/or abundances have to be estimated from these traces during the phase of data processing. To precisely estimate these parameters is challenging and requires a well-designed computer program. Conventionally employed methods, which are based on curve fitting, are computationally expensive and limited in performance especially for highly noisy FLIM data. The graphical analysis, while free of fit, requires calibration samples for a quantitative analysis.

Aim: We propose to extract the lifetimes and abundances directly from the decay traces through machine learning (ML).

Approach: The ML-based approach was verified with simulated testing data in which the lifetimes and abundances were known exactly. Thereafter, we compared its performance with the commercial software SPCImage based on datasets measured from biological samples on a time-correlated single photon counting system. We reconstructed the decay traces using the lifetime and abundance values estimated by ML and SPCImage methods and utilized the root-mean-squared-error (RMSE) as marker.

Results: The RMSE, which represents the difference between the reconstructed and measured decay traces, was observed to be lower for ML than for SPCImage. In addition, we could demonstrate with a three-component analysis the high potential and flexibility of the ML method to deal with more than two lifetime components.

Conclusions: The ML-based approach shows great performance in FLIM data analysis.

Significance: Lupus nephritis (LuN) is a chronic inflammatory kidney disease. The cellular mechanisms by which LuN progresses to kidney failure are poorly characterized. Automated instance segmentation of immune cells in immunofluorescence images of LuN can probe these cellular interactions.

Aim: Our specific goal is to quantify how sample fixation and staining panel design impact automated instance segmentation and characterization of immune cells.

Approach: Convolutional neural networks (CNNs) were trained to segment immune cells in fluorescence confocal images of LuN biopsies. Three datasets were used to probe the effects of fixation methods on cell features and the effects of one-marker versus two-marker per cell staining panels on CNN performance.

Results: Networks trained for multi-class instance segmentation on fresh-frozen and formalin-fixed, paraffin-embedded (FFPE) samples stained with a two-marker panel had sensitivities of 0.87 and 0.91 and specificities of 0.82 and 0.88, respectively. Training on samples with a one-marker panel reduced sensitivity (0.72). Cell size and intercellular distances were significantly smaller in FFPE samples compared to fresh frozen (Kolmogorov–Smirnov, p  ≪  0.0001).

Conclusions: Fixation method significantly reduces cell size and intercellular distances in LuN biopsies. The use of two markers to identify cell subsets showed improved CNN sensitivity relative to using a single marker.

Significance: Raman spectroscopy has been developed for surgical guidance applications interrogating live tissue during tumor resection procedures to detect molecular contrast consistent with cancer pathophysiological changes. To date, the vibrational spectroscopy systems developed for medical applications include single-point measurement probes and intraoperative microscopes. There is a need to develop systems with larger fields of view (FOVs) for rapid intraoperative cancer margin detection during surgery.

Aim: We design a handheld macroscopic Raman imaging system for in vivo tissue margin characterization and test its performance in a model system.

Approach: The system is made of a sterilizable line scanner employing a coherent fiber bundle for relaying excitation light from a 785-nm laser to the tissue. A second coherent fiber bundle is used for hyperspectral detection of the fingerprint Raman signal over an area of 1  cm². Machine learning classifiers were trained and validated on porcine adipose and muscle tissue.

Results: Porcine adipose versus muscle margin detection was validated ex vivo with an accuracy of 99% over the FOV of 95  mm² in ∼3  min using a support vector machine.

Conclusions: This system is the first large FOV Raman imaging system designed to be integrated in the workflow of surgical cancer resection. It will be further improved with the aim of discriminating brain cancer in a clinically acceptable timeframe during glioma surgery.

Significance: While spectral-domain optical coherence tomography (SD-OCT) is a preferred form of OCT imaging, sensitivity roll-off limits its applicability for certain biomedical imaging applications.

Aim: The aim of this work is to extend the imaging range of conventional SD-OCT systems for imaging large luminal organs such as the gastrointestinal tract.

Approach: We present an SD-OCT system operating at a center wavelength of 1300 nm that uses two delayed reference arms to reduce sensitivity roll-off and an optical switch and a fiber optic delay line to ensure that the interference spectra are acquired from the same sample time window.

Result: The proposed system was used to image swine colon ex vivo and duodenum in vivo, demonstrating improved image quality due to a ∼14  dB increase in sensitivity at the edges of the ranging depth.

Conclusion: The proposed system requires modest hardware implementation and is compatible with catheter-based endoscopic helical scanning with enhanced sensitivity for the samples at a distance of ∼6  mm from the zero delay point.

Significance: Rehabilitation through facial prostheses’ main goal is to aid individual’s social reintegration as well as improving their quality of life. However, this treatment is not yet widely available in Brazil due to the lack of specialized clinics and the cost associated with the high number of necessary medical appointments until the final result. One of the steps in the process consists of measuring skin color, which is observer-dependent and may suffer from the effect of metamerism.

Aim: The methodology of our work aims to obtain a standard between different devices and greater fidelity to the color seen in person in order to reduce face-to-face iterations, reduce costs, and ensure better final results.

Approach: A physical device and a computer program were improved from previous projects. The changes included implementing the Thin-Plate Spline 3D algorithm for color calibration, in addition to an optional non-uniform illumination correction in the process. We also aim to improve the project’s accessibility using a colorimeter. The methodology and the algorithms were both compared to readings from direct skin measurements as well as color references.

Results: After processing, the ΔEab* metric between images from the same segments is taken with different cameras and conditions of illumination decreased from 18.81  ±  4.85 to 4.85  ±  1.72. In addition, when the images were compared to colorimetric readings of the skin, the difference went from 14.93  ±  4.11 to 5.85  ±  1.61. It was also observed that using a less expensive device did not impact the readings. The project is open source and available at Github.

Conclusions: The results demonstrate the possibility of applying the methodology to assist in the manufacturing of facial prostheses to decrease the total number of consultations, in addition to providing greater reliability of the final result.

Significance: The measurement of human vital signs based on photoplethysmography imaging (PPGI) can be severely affected by the interference of various factors in the measurement process; therefore, a lot of complex signal processing techniques are used to remove the influence of the interference.

Aim: We comprehensively analyze several methods for color channel combination in the color spaces currently used in PPGI and determine the combination method that can improve the quality of the pulse signal, which results in a modified plane-orthogonal-to-skin based method (POS).

Approach: Based on the analysis of the previous studies, 13 methods for color channel combination in the different color spaces, which can be seen as having potential abilities in measuring vital signs, were compared by employing the average value of signal-to-noise ratio (SNR) and the box-plot in the public databases UBFC-RPPG and PURE. In addition, the pulse signal was extracted through the dual-color space transformation (sRGB → intensity normalized RGB → YCbCr) and fine-tuning on the CbCr plane.

Results: Among the 13 methods for color channel combination, the signal extracted by the Cb+Cr combination in the YCbCr color space includes the most pulse information. Furthermore, the average SNR of the modified POS for all the used databases is improved by 69.3% compared to POS.

Conclusions: The methods using prior knowledge are not only simple to calculate but can significantly increase the SNR, which will provide a great help in the practical use of vital sign measurements based on PPGI.

Significance: We recently developed a model for the reflectance measured with (multi-diameter) single-fiber reflectance (SFR) spectroscopy as a function of the reduced scattering coefficient μs′, the absorption coefficient μ_a, and the phase function parameter p_sb. We validated this model with simulations.

Aim: We validate our model experimentally. To prevent overfitting, we investigate the wavelength-dependence of p_sb and propose a parametrization with only three parameters. We also investigate whether this parametrization enables measurements with a single fiber, as opposed to multiple fibers used in multi-diameter SFR (MDSFR).

Approach: We validate our model on 16 phantoms with two concentrations of Intralipid-20% (μs′=13 and 21  cm^−  1 at 500 nm) and eight concentrations of Evans Blue (μ_a  =  1 to 20  cm^−  1 at 605 nm). We parametrize p_sb as 10^−  5  ·    (  p₁  (  λ  /  650  )    +  p₂(λ/650)²  +  p₃(λ/650)³  )  .

Results: Average errors were 7% for μs′, 11% for μ_a, and 16% with the parametrization of p_sb; and 7%, 17%, and 16%, respectively, without. The parametrization of p_sb improved the fit speed 25 times (94 s to <4  s). Average errors for only one fiber were 50%, 33%, and 186%, respectively.

Conclusions: Our recently developed model provides accurate results for MDSFR measurements but not for a single fiber. The p_sb parametrization prevents overfitting and speeds up the fit.

Significance: Spatial frequency domain imaging (SFDI), a noncontact wide-field imaging technique using patterned illumination with multiple wavelengths, has been used to quantitatively measure structural and functional parameters of in vivo tissue. Using SFDI in a porcine model, we previously found that scattering changes in skin could potentially be used to noninvasively assess burn severity and monitor wound healing. Translating these findings to human subjects necessitates a better understanding of the variation in “baseline” human skin scattering properties across skin types and anatomical locations.

Aim: Using SFDI, we aim to characterize the variation in the reduced scattering coefficient (μs′) for skin across a range of pigmentation and anatomic sites (including common burn locations) for normal human subjects. These measurements are expected to characterize baseline human skin properties to inform our use of SFDI for clinical burn severity and wound healing assessments.

Approach: SFDI was used to measure μs′ in the visible- and near-infrared regime (471 to 851 nm) in 15 subjects at 10 anatomical locations. Subjects varied in age, gender, and Fitzpatrick skin type.

Results: For all anatomical locations, the coefficient of variation in measured μs′ decreased with increasing wavelength. High intersubject variation in μs′ at visible wavelengths coincided with large values of the melanin extinction coefficient at those wavelengths. At 851 nm, where intersubject variation in μs′ was smallest for all anatomical locations and absorption from melanin is minimal, significant intrasubject differences in μs′ were observed at the different anatomical locations.

Conclusions: Our study is the first report of wide-field mapping of human skin scattering properties across multiple skin types and anatomical locations using SFDI. Measured μs′ values varied notably between skin types at wavelengths where absorption from melanin was prominent. Additionally, μs′ varied considerably across different anatomical locations at 851 nm, where the confounding effects from melanin absorption are minimized.

Significance: Hemoglobin oxygen saturation and red blood cell (RBC) tissue fraction are important parameters when assessing microvascular status. Functional information can be attained using temporally resolved measurements performed during stimulus–response protocols. Pointwise assessments can currently be conducted with probe-based systems. However, snapshot multispectral imaging (MSI) can be used for spatial–temporal measurements.

Aim: To validate if hemoglobin oxygen saturation and RBC tissue fraction can be quantified using a snapshot MSI system and an inverse Monte Carlo algorithm.

Approach: Skin tissue measurements from the MSI system were compared to those from a validated probe-based system during arterial and venous occlusion provocation on 24 subjects in the wavelength interval 450 to 650 nm, to evaluate a wide range of hemoglobin oxygen saturation and RBC tissue fraction levels.

Results: Arterial occlusion results show a mean linear regression R²  =  0.958 for hemoglobin oxygen saturation. Comparing relative RBC tissue fraction during venous occlusion results in R²  =  0.925. The MSI system shows larger dynamic changes than the reference system, which might be explained by a deeper sampling including more capacitance vessels.

Conclusions: The snapshot MSI system estimates hemoglobin oxygen saturation and RBC tissue fraction in skin microcirculation showing a high correlation (R²  >  0.9 in most subjects) with those measured by the reference method.

Significance: Dry or moist skin-contact thermal stimulation for vein puncture (VP) and vein cannulation (VC) may not be feasible for sensitive skin. For a damaged, burned, or dark skin, near-infrared (NIR) imaging is preferred to visualize a vein. Postprocessing of NIR images is always required because the skin is a reflective material and veins need segmentation for quantitative analysis.

Aim: Our pilot study aims to observe the effect of noncontact local heating on the superficial metacarpal veins in the dorsal surface of the hand and to visualize vein dynamics using an NIR imaging system.

Approach: Our experiment consists of studies A and B at two ambient temperatures, 19°C and 25°C. A simple reflection-based NIR imaging system was installed to acquire sequential vein images for 5 min before and after applying 10 min of radiant thermal stimulation. To measure the vein diameter (VD), we trained a convolutional neural network (CNN) on sequential raw images to predict vein-segmentation masks as output images. Later these masked images were postprocessed for the VD measurements.

Results: The average VD was significantly increased after thermal stimulation in study A. The maximum increments in VD were 39.3% and 9.19%, 1 min after thermal stimulation in studies A and B, respectively. Both the VD and skin temperature (T_skin) follow negative exponentials in time, and the VD is proportional to T_skin. A multiple linear-regression model was made to predict the final VD. A significant difference was observed in the change of the VD.

Conclusions: NIR imaging with CNN can be used for quantitative analyses of vein dynamics. This finding can be further extended to develop real-time, image-guided medical devices by integrating them with a radiant heater and to assist medical practitioners in achieving high success rates for VP or VC.

Significance: Current imaging paradigms for differential diagnosis of suspicious breast lesions suffer from high false positive rates that force patients to undergo unnecessary biopsies. Diffuse optical spectroscopic imaging (DOSI) noninvasively probes functional hemodynamic and compositional parameters in deep tissue and has been shown to be sensitive to contrast between normal and malignant tissues.

Aim: DOSI methods are under investigation as an adjunct to mammography and ultrasound that could reduce false positive rates and unnecessary biopsies, particularly in radiographically dense breasts.

Methods: We performed a retrospective analysis of 212 subjects with suspicious breast lesions who underwent DOSI imaging. Physiological tissue parameters were z-score normalized to the patient’s contralateral breast tissue and input to univariate logistic regression models to discriminate between malignant tumors and the surrounding normal tissue. The models were then used to differentiate malignant lesions from benign lesions.

Results: Models incorporating several individual hemodynamic parameters were able to accurately distinguish malignant tumors from both the surrounding background tissue and benign lesions with area under the curve (AUC) ≥0.85. Z-score normalization improved the discriminatory ability and calibration of these predictive models relative to unnormalized or ratio-normalized data.

Conclusions: Findings from a large subject population study show how DOSI data normalization that accounts for normal tissue heterogeneity and quantitative statistical regression approaches can be combined to improve the ability of DOSI to diagnose malignant lesions. This improved diagnostic accuracy, combined with the modality’s inherent logistical advantages of portability, low cost, and nonionizing radiation, could position DOSI as an effective adjunct modality that could be used to reduce the number of unnecessary invasive biopsies.

Significance: Single-molecule localization-based super-resolution microscopy has enabled the imaging of microscopic objects beyond the diffraction limit. However, this technique is limited by the requirements of imaging an extremely large number of frames of biological samples to generate a super-resolution image, thus requiring a longer acquisition time. Additionally, the processing of such a large image sequence leads to longer data processing time. Therefore, accelerating image acquisition and processing in single-molecule localization microscopy (SMLM) has been of perennial interest.

Aim: To accelerate three-dimensional (3D) SMLM imaging by leveraging a computational approach without compromising the resolution.

Approach: We used blind sparse inpainting to reconstruct high-density 3D images from low-density ones. The low-density images are generated using much fewer frames than usually needed, thus requiring a shorter acquisition and processing time. Therefore, our technique will accelerate 3D SMLM without changing the existing standard SMLM hardware system and labeling protocol.

Results: The performance of the blind sparse inpainting was evaluated on both simulation and experimental datasets. Superior reconstruction results of 3D SMLM images using up to 10-fold fewer frames in simulation and up to 50-fold fewer frames in experimental data were achieved.

Conclusions: We demonstrate the feasibility of fast 3D SMLM imaging leveraging a computational approach to reduce the number of acquired frames. We anticipate our technique will enable future real-time live-cell 3D imaging to investigate complex nanoscopic biological structures and their functions.