SignificanceInformation about the spatial organization of fibers within a nerve is crucial to our understanding of nerve anatomy and its response to neuromodulation therapies. A serial block-face microscopy method [three-dimensional microscopy with ultraviolet surface excitation (3D-MUSE)] has been developed to image nerves over extended depths ex vivo. To routinely visualize and track nerve fibers in these datasets, a dedicated and customizable software tool is required.AimOur objective was to develop custom software that includes image processing and visualization methods to perform microscopic tractography along the length of a peripheral nerve sample.ApproachWe modified common computer vision algorithms (optic flow and structure tensor) to track groups of peripheral nerve fibers along the length of the nerve. Interactive streamline visualization and manual editing tools are provided. Optionally, deep learning segmentation of fascicles (fiber bundles) can be applied to constrain the tracts from inadvertently crossing into the epineurium. As an example, we performed tractography on vagus and tibial nerve datasets and assessed accuracy by comparing the resulting nerve tracts with segmentations of fascicles as they split and merge with each other in the nerve sample stack.ResultsWe found that a normalized Dice overlap (Dicenorm) metric had a mean value above 0.75 across several millimeters along the nerve. We also found that the tractograms were robust to changes in certain image properties (e.g., downsampling in-plane and out-of-plane), which resulted in only a 2% to 9% change to the mean Dicenorm values. In a vagus nerve sample, tractography allowed us to readily identify that subsets of fibers from four distinct fascicles merge into a single fascicle as we move ∼5 mm along the nerve’s length.ConclusionsOverall, we demonstrated the feasibility of performing automated microscopic tractography on 3D-MUSE datasets of peripheral nerves. The software should be applicable to other imaging approaches. The code is available at https://github.com/ckolluru/NerveTracker.
Collagen and elastin are prominent components in both normal and abnormal tissues, and their presence and distribution have great significance for fibrosis- and cancer-related processes. Collagen and elastin quantification in the context of fibrosis, often associated with irreparable organ injury, can predict the disease severity and patient prognosis. In the context of cancer, specific spatial collagen signatures are known to influence tumor microenvironments while identification of elastin is important in the context of treatment of metastatic cancers. Traditional methods to quantify collagen and elastin vary in accuracy, cost, and ease of use. Using DUET microscopy on H&E slides, high-resolution collagen and elastin mapping is possible without added staining steps or expensive optical instrumentation. We demonstrate this approach in chronic kidney disease (CKD), coronary artery disease (CAD), and for identifying vascular elastin in colon cancers.
Accurate quantification of renal fibrosis has profound importance in the assessment of chronic kidney disease (CKD). Visual analysis of a biopsy stained with trichrome under the microscope by a pathologist is the gold standard for evaluation of fibrosis. Trichrome helps to highlight collagen and ultimately interstitial fibrosis. However, trichrome stains are not always reproducible, can underestimate collagen content and are not sensitive to subtle fibrotic patterns. Using the Dual-mode emission and transmission (DUET) microscopy approach, it is possible to capture both brightfield and fluorescence images from the same area of a tissue stained with hematoxylin and eosin (H&E) enabling reproducible extraction of collagen with high sensitivity and specificity. Manual extraction of spectrally overlapping collagen signals from tubular epithelial cells and red blood cells is still an intensive task. We employed a UNet++ architecture for pixel-level segmentation and quantification of collagen using 760 whole slide image (WSI) patches from six cases of varying stages of fibrosis. Our trained model (Deep-DUET) used the supervised extracted collagen mask as ground truth and was able to predict the extent of collagen signal with a MSE of 0.05 in a holdout testing set while achieving an average AUC of 0.94 for predicting regions of collagen deposits. Expanding this work to the level of the WSI can greatly improve the ability of pathologists and machine learning (ML) tools to quantify the extent of renal fibrosis reproducibly and reliably.
Herein, we present an anatomy-specific classification model using FLIm to differentiate between benign tissue, dysplasia, and cancer within the oral cavity and oropharynx. A total of 54 features, comprising both time-resolved and spectral intensity features, were used to train and test the classification model. This anatomy-specific classifier improves on our previous classification approach, now yielding an overall ROC-AUC of 0.94 during binary benign vs. cancer classification, and 0.92 while discriminating between healthy, cancer, and dysplasia. The proposed classification model demonstrates that FLIm has the potential to be used as an adjunctive diagnostic tool to facilitate head and neck cancer surgical guidance.
Anatomic pathology is still the gold standard for tissue-based disease diagnosis, is centered on interpretation of H&E-stained sections on glass slides, a process requires labor-intensive and time-consuming specimen processing. We propose a simple, clinically relevant solution, termed FIBI (fluorescence imitating brightfield imaging), for directly creating diagnostic-quality images from unsectioned, fresh or fixed tissue specimens. FIBI can generate full-color histology-grade images within minutes.
We have demonstrated the validity of this method by collecting 50 tissue samples from various organs and pathologies and comparing the diagnosis obtained using FIBI images with those determined from adjacent, conventionally prepared H&E-stained slides.
Hematoxylin- and eosin-stained tissue sections are central to patient diagnosis and management guidance. Direct viewing of these slides using a microscope is slowly but seemingly inevitably giving way to the new world of digital pathology. Having the histology images in digital format facilitates deployment of digital analysis tools, ranging from basic image enhancement to straightforward assessment of quantifiable metrics (area, intensity, molecular marker intensity and distribution) all the way to applications of novel machine learning and artificial intelligence tools. However, to date, virtually all such H&E whole-slide scans have been accomplished only in brightfield mode.
We have observed that simple fluorescence imaging of H&E-stained slides can provide a great deal of useful histology content. We have developed a scanner that can acquire pixel-matched brightfield and fluorescence images in area-scanning mode, a process we termed DUET, for DUal-mode Emission and Transmission, i
Significance: 5-aminolevulinic acid (5-ALA)-induced protoporphyrin IX (PpIX) fluorescence is currently used for image-guided glioma resection. Typically, this widefield imaging method highlights the bulk of high-grade gliomas, but it underperforms at the infiltrating edge where PpIX fluorescence is not visible to the eyes. Fluorescence lifetime imaging (FLIm) has the potential to detect PpIX fluorescence below the visible detection threshold. Moreover, simultaneous acquisition of time-resolved nicotinamide adenine (phosphate) dinucleotide [NAD(P)H] fluorescence may provide metabolic information from the tumor environment to further improve overall tumor detection.
Aim: We investigate the ability of pulse sampling, fiber-based FLIm to simultaneously image PpIX and NAD(P)H fluorescence of glioma infiltrative margins in patients.
Approach: A mesoscopic fiber-based point-scanning FLIm device (355 nm pulses) was used to simultaneously resolve the fluorescence decay of PpIX (629/53 nm) and NAD(P)H (470/28 nm). The FLIm device enabled data acquisition at room light and rapid (<33 ms) augmentation of FLIm parameters on the surgical field-of-view. FLIm measurements from superficial tumors and tissue areas around the resection margins were performed on three glioblastoma patients in vivo following inspection of PpIX visible fluorescence with a conventional neurosurgical microscope. Microbiopsies were collected from FLIm imaged areas for histopathological evaluation.
Results: The average lifetime from PpIX and NAD(P)H fluorescence distinguished between tumor and surrounding tissue. FLIm measurements of resection margins presented a range of PpIX and NAD(P)H lifetime values (τPpIX ∼ 3 to 14 ns, τNAD(P)H = 3 to 6 ns) associated with unaffected tissue and areas of low-density tumor infiltration.
Conclusions: Intraoperative FLIm could simultaneously detect the emission of PpIX and NAD(P)H from patients in vivo during craniotomy procedures. This approach doubles as a clinical tool to identify tumor areas while performing tissue resection and as a research tool to study tumor microenvironmental changes in vivo. Intraoperative FLIm of 5-ALA-induced PpIX and tissue autofluorescence makes a promising surgical adjunct to guide tumor resection surgery.
Vagus nerve stimulation (VNS) is a method to treat drug-resistant epilepsy and depression, but therapeutic outcomes are often not ideal. Newer electrode designs such as intra-fascicular electrodes offer potential improvements in reducing off-target effects but require a detailed understanding of the fascicular anatomy of the vagus nerve. We have adapted a section-and-image technique, cryo-imaging, with UV excitation to visualize fascicles along the length of the vagus nerve. In addition to offering optical sectioning at the surface via reduced penetration depth, UV illumination also produces sufficient contrast between fascicular structures and connective tissue. Here we demonstrate the utility of this approach in pilot experiments. We imaged fixed, cadaver vagus nerve samples, segmented fascicles, and demonstrated 3D tracking of fascicles. Such data can serve as input for computer models of vagus nerve stimulation.
MUSE (Microscopy with UV Surface Excitation) is a simple new approach to microscopy, being a straightforward and inexpensive method that can provide diagnostic-quality images, with enhanced spatial and color information, directly and quickly from fresh or fixed tissue. The process is non-destructive, permitting downstream molecular analyses.
Samples are briefly stained with common fluorescent dyes, followed by 280-nm UV light excitation that generates highly surface-weighted images due to limited penetration depth of light at this wavelength. The method also takes advantage of the "USELESS" phenomenon (UV stain excitation with long emission Stokes shift) for broad-spectrum image generation in the visible range.
MUSE readily provides surface topography information even in single snapshots, and while not fully 3-dimensional, the images are easy to acquire, and easy to interpret, providing more insight into tissue structure.
However, working with samples with intrinsic depth information can pose problems with respect to determining appropriate focal points as well as capturing extended depth-of-field images. We demonstrate an accelerated and efficient variant approach for extending depth of field by employing swept-focus acquisition techniques. We have also developed a novel method for rapid autofocus. Together, these capabilities contribute to MUSE functionality and ease of use.
Widely used methods for preparing and viewing tissue specimens at microscopic resolution have not changed for over a century. They provide high-quality images but can involve time-frames of hours or even weeks, depending on logistics. There is increasing interest in slide-free methods for rapid tissue analysis that can both decrease turn-around times and reduce costs. One new approach is MUSE (microscopy with UV surface excitation), which exploits the shallow penetration of UV light to excite fluorescent signals from only the most superficial tissue elements. The method is non-destructive, and eliminates requirement for conventional histology processing, formalin fixation, paraffin embedding, or thin sectioning. It requires no lasers, confocal, multiphoton or optical coherence tomography optics.
MUSE generates diagnostic-quality histological images that can be rendered to resemble conventional hematoxylin- and eosin-stained samples, with enhanced topographical information, from fresh or fixed, but unsectioned tissue, rapidly, with high resolution, simply and inexpensively. We anticipate that there could be widespread adoption in research facilities, hospital-based and stand-alone clinical settings, in local or regional pathology labs, as well as in low-resource environments.
A novel microscopy method that takes advantage of shallow photon penetration using ultraviolet-range excitation and
exogenous fluorescent stains is described. This approach exploits the intrinsic optical sectioning function when exciting
tissue fluorescence from superficial layers to generate images similar to those obtainable from a physically thinsectioned
tissue specimen. UV light in the spectral range from roughly 240-275 nm penetrates only a few microns into
the surface of biological specimens, thus eliminating out-of-focus signals that would otherwise arise from deeper tissue
layers. Furthermore, UV excitation can be used to simultaneously excite fluorophores emitting across a wide spectral
range. The sectioning property of the UV light (as opposed to more conventional illumination in the visible range)
removes the need for physical or more elaborate optical sectioning approaches, such as confocal, nonlinear or coherent
tomographic methods, to generate acceptable axial resolution. Using a tunable laser, we investigated the effect of
excitation wavelength in the 230-350 nm spectral range on excitation depth. The results reveal an optimal wavelength
range and suggest that this method can be a fast and reliable approach for rapid imaging of tissue specimens. Some of
this range is addressable by currently available and relatively inexpensive LED light sources. MUSE may prove to be a
good alternative to conventional, time-consuming, histopathology procedures.
Lifetime and spectral imaging are complementary techniques that offer a non-invasive solution for monitoring metabolic processes, identifying biochemical compounds, and characterizing their interactions in biological tissues, among other tasks. Newly developed instruments that perform time-resolved spectral imaging can provide even more information and reach higher sensitivity than either modality alone. Here we report a multispectral lifetime imaging system based on a field-programmable gate array (FPGA), capable of operating at high photon count rates (12 MHz) per spectral detection channel, and with time resolution of 200 ps. We performed error analyses to investigate the effect of gate width and spectral-channel width on the accuracy of estimated lifetimes and spectral widths. Temporal and spectral phasors were used for analysis of recorded data, and we demonstrated blind un-mixing of the fluorescent components using information from both modalities. Fractional intensities, spectra, and decay curves of components were extracted without need for prior information. We further tested this approach with fluorescently doubly-labeled DNA, and demonstrated its suitability for accurately estimating FRET efficiency in the presence of either non-interacting or interacting donor molecules.
A method, is presented for blind unmixing spectrally resolved fluorescence lifetime images. The method is based on the combined analysis of spectral and lifetime phasors and allows unmixing of up to three components without any prior knowledge. Fractional intensities, spectra and decay curves of the individual components can be extracted with this new technique. The reliability and sensitivity are investigated and the possibility of extending the method to unmix more components is discussed. The method is evaluated on mixtures of fluorescent dyes and labeled cells.
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