Combined flat-field and frequency filter approach to correcting artifacts of multichannel two-photon microscopy.

Significance: Multiphoton 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. Aim: We 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. Approach: Image 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. Results: Consideration 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. Conclusions: While 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.

Wide field-of-view fluorescence imaging for organ-level lineage tracing of rare intestinal stem cell populations.

Significance: Lineage 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. Aim: We 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. Approach: We 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. Results: Quantification 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. Conclusions: We 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.

Design, fabrication, and preclinical testing of a miniaturized, multispectral, chip-on-tip, imaging probe for intraluminal fluorescence imaging of the gastrointestinal tract.

Gastrointestinal cancers continue to account for a disproportionately large percentage of annual cancer deaths in the US. Advancements in miniature imaging technology combined with a need for precise and thorough tumor detection in gastrointestinal cancer screenings fuel the demand for new, small-scale, and low-cost methods of localization and margin identification with improved accuracy. Here, we report the development of a miniaturized, chip-on-tip, multispectral, fluorescence imaging probe designed to port through a gastroscope working channel with the aim of detecting cancerous lesions in point-of-care endoscopy of the gastrointestinal lumen. Preclinical testing has confirmed fluorescence sensitivity and supports that this miniature probe can locate structures of interest via detection of fluorescence emission from exogenous contrast agents. This work demonstrates the design and preliminary performance evaluation of a miniaturized, single-use, chip-on-tip fluorescence imaging system, capable of detecting multiple fluorochromes, and devised for deployment via the accessory channel of a standard gastroscope.

Mobile-based oral cancer classification for point-of-care screening.

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.