SignificanceMultispectral fluorescence imaging (MSFI) is a technique that measures endogenous and exogenous tissue fluorescence to reveal crucial insights into underlying biological mechanisms. Developing well-characterized high-performance fluorescence imaging equipment is crucial to any clinical or research application. Considering the diverse range of clinical scenarios and ongoing research areas in biomedicine leveraging MSFI instrumentation, there is an evident and pressing need for comprehensive resources that detail the development of such tools.AimThis study provides a template for developing an MSFI instrument by highlighting the development and verification of our instrumentation. We present the design roadmap alongside the development of a large-area MSFI instrument to measure tissue fluorescence and diffuse reflectance properties.ApproachWe divide our design approach into four subsections, highlighting the important physical milestones: illumination, imaging, detection, and computation. Each subsection includes design considerations, the methods used to validate the performance, and finally, the results and discussion of the validation process.ResultsWe present the validation of the instrument across the illumination, imaging, detection, and computation subsystems. Two fluorophores are used to validate the instrument through serial dilution to establish a detection threshold and spectral capabilities limit. A mouse model expressing multi-colored fluorescent proteins is used to verify the multispectral performance.ConclusionsOur study lays out the groundwork for researchers to design and validate their own MSFI instrument. We present this alongside our instrument design to study fluorescence and diffuse reflectance properties of large-area tissues, such as murine or resected surgical specimens. Findings from the application of MSFI instrumentation can be translated to motivate and guide the design of fluorescence-based medical tools for the clinic or research environment.
Esophageal cancer has seen an increase in incidence in recent decades; early intervention is key in improving patient outcomes. As a result screening techniques must be improved in order to detect early cancer and dysplasia endoscopically. Polarized light imaging (PLI) and optical coherence tomography (OCT) are of interest due to their capabilities to probe microstructural tissue properties.
In this study healthy and cancerous esophageal tissue samples are imaged with PLI and OCT systems. Classification algorithms are developed to identify polarimetric properties from PLI and Haralick texture features from OCT which are key in distinguishing between the healthy and diseased tissue.
Esophageal cancer’s increasing prevalence coupled with a 5-year average survival rate below 20% due largely to late detection indicates a significant need for improved imaging tools that can detect and localize early, unseen lesions and be incorporated into endoscopy for screening and evaluation of early symptoms. While white light imaging or virtual chromoendoscopy contrast-enhancement techniques like narrow-band imaging have largely seen commercialization, there remain emerging label-free imaging-based techniques that show promise for improving diagnosis and biopsy guidance. Among them we investigate the clinical potential of hyperspectral (HSI) and autofluorescence imaging (AFI) which lend themselves well to implementation in an endoscopic system. We performed ex-vivo imaging on esophageal biopsies suspicious for carcinoma (N=11) and/or Barrett’s esophagus (N=6) and adjacent normal appearing squamous mucosa in the same patient as controls. Our results indicate AFI and HSI are both promising imaging modalities for detecting and localizing morphological and metabolic changes associated with esophageal cancer.
Paraformaldehyde (PFA) is one of the most common fixatives in biological and biomedical research. It is used to preserve tissue or cell morphology while preventing contamination by crosslinking proteins and other biological molecules. Although fixation is required for histology, it has been documented that chemical fixation can cause alterations in the fluorescence properties of exogenous and endogenous fluorophores, which are valuable markers for understanding biological processes, ultimately reducing the accuracy and reliability of quantitative fluorescence measurements. Therefore, there is a need for understanding the behavior of tissue fluorescence during PFA fixation. Multispectral fluorescence imaging (MFSI) is an imaging technique used in biological and biomedical research to visualize and quantify the fluorescence properties of tissue over several wavelength bands, enabling measurement of several fluorophores simultaneously.
To evaluate the effects of PFA on tissue fluorescence, we imaged brain tissue samples using MSFI from two cohorts of mice: the SOX10 Cre; R26R-Brainbow 2.1/Confetti mice (expressing four exogenous fluorophores), and wild type Cre-negative controls. Specimens from each were immersed in 10 ml of PFA or phosphate buffer saline (PBS) as a control. The fluorescence intensity was captured using MFSI every 15 minutes over three hours. Analysis was performed on the resulting images to produce quantitative metrics of the resulting fluorescence signal. The results show that exogenous fluorophores are dramatically quenched within the first half hour when fixed in PFA, whereas endogenous fluorescence increased slightly in the same time period. These results are valuable to understand how fixation can influence fluorescence properties and can inform optimal fixation protocols.
SignificanceMultiphoton microscopy (MPM) is a useful biomedical imaging tool for its ability to probe labeled and unlabeled depth-resolved tissue biomarkers at high resolution. Automated MPM tile scanning allows for whole-slide image acquisition but can suffer from tile-stitching artifacts that prevent accurate quantitative data analysis.AimWe have investigated postprocessing artifact correction methods using ImageJ macros and custom Python code. Quantitative and qualitative comparisons of these methods were made using whole-slide MPM autofluorescence and second-harmonic generation images of human duodenal tissue.ApproachImage quality after artifact removal is assessed by evaluating the processed image and its unprocessed counterpart using the root mean square error, structural similarity index, and image histogram measurements.ResultsConsideration of both quantitative and qualitative results suggest that a combination of a custom flat-field-based correction and frequency filtering processing step provide improved artifact correction when compared with each method used independently to correct for tiling artifacts of tile-scan MPM images.ConclusionsWhile some image artifacts remain with these methods, further optimization of these processing steps may result in computational-efficient methods for removing these artifacts that are ubiquitous in large-scale MPM imaging. Removal of these artifacts with retention of the original image information would facilitate the use of this imaging modality in both research and clinical settings, where it is highly useful in collecting detailed morphologic and optical properties of tissue.
SignificanceLineage tracing using fluorescent reporters is a common tool for monitoring the expression of genes and transcription factors in stem cell populations and their progeny. The zinc-binding protein 89 (ZBP-89/Zfp148 mouse gene) is a transcription factor that plays a role in gastrointestinal (GI) stem cell maintenance and cellular differentiation and has been linked to the progression of colon cancer. While lineage tracing is a useful tool, it is commonly performed with high-magnification microscopy on a small field of view within tissue sections, thereby limiting the ability to resolve reporter expression at the organ level. Furthermore, this technique requires extensive tissue processing, which is time consuming and requires euthanizing the animal. Further knowledge could be elucidated by measuring the expression of fluorescent reporters across entire organs with minimal tissue processing.AimWe present the application of wide-field fluorescence imaging for whole-organ lineage tracing of an inducible Zfp148-tdTomato-expressing transgenic mouse line to assess the expression of ZBP-89/Zfp148 in the GI tract.ApproachWe measured tdTomato fluorescence in ex vivo organs at time points between 24 h and 6 months post-induction. Fluctuations in tdTomato expression were validated by fluorescence microscopy of tissue sections.ResultsQuantification of the wide field-of-view images showed a statistically significant increase in fluorescent signal across the GI tract between transgenic mice and littermate controls. The results also showed a gradient of decreasing reporter expression from proximal to distal intestine, suggesting a higher abundance of ZBP-89 expressing stem cells, or higher expression of ZBP-89 within the stem cells, in the proximal intestine.ConclusionsWe demonstrate that wide-field fluorescence imaging is a valuable tool for monitoring whole-organ expression of fluorescent reporters. This technique could potentially be applied in vivo for longitudinal assessment of a single animal, further enhancing our ability to resolve rare stem cell lineages spatially and temporally.
Multispectral fluorescence imaging (MSFI) is a powerful imaging modality for tissue analysis and diagnostic imaging. By illuminating with distinct wavelengths of light, intrinsic biological fluorophores and labeled markers can be measured, providing information about tissue metabolism and function. MSFI has shown promise in the scope of gastrointestinal (GI) cancers such as colon and gastric cancers. Before MSFI can be used as an endoscopic diagnostic tool there requires extensive characterization of tissue properties to identify biomarker variations that occur with the onset of disease. A robust, whole organ imaging instrument to characterize autofluorescence properties would greatly inform the development of diagnostic imaging platforms. This paper reviews the design and validation of a multispectral fluorescence imaging system for characterizing whole organ tissue fluorescence and reflectance properties. We present a detailed discussion on design considerations and demonstrate excellent performance suitable to detect tissue autofluorescence.
Multi-photon microscopy (MPM) is a useful biomedical imaging tool due, in part, to its capabilities of probing tissue biomarkers at high resolution and with depth-resolved capabilities. Automated MPM tile scanning allows for whole-slide image acquisition but suffers from tile-stitching artifacts that prevent accurate quantitative data analysis. We have investigated a variety of post-processing artifact correction methods using ImageJ macros and custom Python/ MATLAB code and present a quantitative and qualitative comparison of these methods using whole-slide MPM autofluorescence images of human duodenal tissue. Image quality is assessed via evaluation of artifact removal compared to the calculated mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) of the processed image and its raw counterpart. Consideration of both quantitative and qualitative results suggest a combination of flat-field based correction and frequency filtering processing steps provide improved artifact correction when compared to each method used independently to correct for tiling artifacts of tile-scan MPM images.
Significance: Oral cancer is among the most common cancers globally, especially in low- and middle-income countries. Early detection is the most effective way to reduce the mortality rate. Deep learning-based cancer image classification models usually need to be hosted on a computing server. However, internet connection is unreliable for screening in low-resource settings.
Aim: To develop a mobile-based dual-mode image classification method and customized Android application for point-of-care oral cancer detection.
Approach: The dataset used in our study was captured among 5025 patients with our customized dual-modality mobile oral screening devices. We trained an efficient network MobileNet with focal loss and converted the model into TensorFlow Lite format. The finalized lite format model is ∼16.3 MB and ideal for smartphone platform operation. We have developed an Android smartphone application in an easy-to-use format that implements the mobile-based dual-modality image classification approach to distinguish oral potentially malignant and malignant images from normal/benign images.
Results: We investigated the accuracy and running speed on a cost-effective smartphone computing platform. It takes ∼300 ms to process one image pair with the Moto G5 Android smartphone. We tested the proposed method on a standalone dataset and achieved 81% accuracy for distinguishing normal/benign lesions from clinically suspicious lesions, using a gold standard of clinical impression based on the review of images by oral specialists.
Conclusions: Our study demonstrates the effectiveness of a mobile-based approach for oral cancer screening in low-resource settings.
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