SignificanceLabel-free, two-photon excited fluorescence (TPEF) imaging captures morphological and functional metabolic tissue changes and enables enhanced understanding of numerous diseases. However, noise and other artifacts present in these images severely complicate the extraction of biologically useful information.AimWe aim to employ deep neural architectures in the synthesis of a multiscale denoising algorithm optimized for restoring metrics of metabolic activity from low-signal-to-noise ratio (SNR), TPEF images.ApproachTPEF images of reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavoproteins (FAD) from freshly excised human cervical tissues are used to assess the impact of various denoising models, preprocessing methods, and data on metrics of image quality and the recovery of six metrics of metabolic function from the images relative to ground truth images.ResultsOptimized recovery of the redox ratio and mitochondrial organization is achieved using a novel algorithm based on deep denoising in the wavelet transform domain. This algorithm also leads to significant improvements in peak-SNR (PSNR) and structural similarity index measure (SSIM) for all images. Interestingly, other models yield even higher PSNR and SSIM improvements, but they are not optimal for recovery of metabolic function metrics.ConclusionsDenoising algorithms can recover diagnostically useful information from low SNR label-free TPEF images and will be useful for the clinical translation of such imaging.
Circulating tumor cell clusters (CTCCs) are extremely rare events (<4 events per 7.5 mL of blood) found in the bloodstream of metastatic cancer patients. Despite their scarcity, they represent an increased risk for metastasis. Detection and isolation of CTCCs remain a priority for oncologists to improve cancer patients' diagnosis, stratification, and treatment. This study aims to demonstrate that confocal backscatter and fluorescence flow cytometry (BSFC) with deep learning-based signal analysis can enable sensitive detection of CTCCs in whole blood in vitro and in vivo without using exogenous labeling.
The potential to differentiate between diseased and healthy tissue has been demonstrated through the extraction of morphological and functional metrics from label-free, two-photon images. Acquiring such images as fast as possible without compromising their diagnostic and functional content is critical for clinical translation of two-photon imaging. Computational restoration methods have demonstrated impressive recovery of image quality and important biological information. However, access to large clinical datasets has hampered advancement of denoising algorithms. Here, we seek to demonstrate the application of denoising algorithms on depth-resolved two-photon excited fluorescence (TPEF) images with specific focus on recovery of functional metabolic metrics. Datasets were generated through the collection of images of reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavoproteins from freshly excised rat cheek epithelium. Image datasets were patched across depth, generating 1012, 256-by-256 patches. A well-known U-net architecture was trained on 6628 low-signal-to-noise-ratio (SNR) patches from a previously collected large dataset and later retrained on a smaller 620 low-SNR patches dataset before being validated and evaluated on 88 and 304 low-SNR patches, respectively, using a structural similarity index measure (SSIM) loss function. We demonstrate models trained on larger datasets of human cervical tissue could be used to successfully restore metabolic metrics with an improvement in image quality when applied to rat cheek epithelium images. These results motivate further exploration of weight transfer for denoising of small clinical two-photon microscopy datasets.
KEYWORDS: Blood, Tumors, Luminescence, Light scattering, Signal analysis, Signal detection, In vitro testing, Microfluidics, In vivo imaging, Green fluorescent protein
Circulating tumor cell clusters (CTCCs) are associated with high metastatic potential and poor patient prognosis. However, they are difficult to detect and isolate because of their extremely low numbers. Here, we report on the use of machine learning based analysis to achieve highly accurate detection of CTCCs in flowing whole blood samples relying on the confocal detection of endogenous light scattering and fluorescence signals. Our custom flow cytometer utilizes laser excitation at 405, 488, and 633 nm and confocal detection of the corresponding light scattering signals as well as fluorescence in the 525 25nm and 67020nm range. Samples consist of whole blood isolated from mice or rats spiked with varying concentrations of CTCCs consisting of 2-15 cell CTCCs, flowed through the channels of a microfluidic device. The CTCCs utilized in this initial study express GFP, so that we can detect the strong GFP signal using the 525 nm detector and use that signal as the ground truth for assessing the performance of algorithms relying on the endogenous signals detected by the other detectors. Our data is acquired during 18 independent experiments with data from 13 days used for training and five days used for testing. There are over 6,000 true positive and over 60,000 false negative peaks in this data set. We consider narrow neural network, fine k-nearest neighbors and ensemble bagged tree (EBT) models and we find that an EBT with gentle boost model yields optimal performance. Using the data from the three light scattering channels and the autofluorescence channel results in CTCC detection with purity, sensitivity, specificity and accuracy that exceed 90% in the test data. These promising results motivate further development of label-free flow cytometry using non-GFP expressing CTCCs for in vitro and in vivo applications.
KEYWORDS: Tumors, Confocal microscopy, Luminescence, Flow cytometry, Signal to noise ratio, In vitro testing, Blood, Sensors, Signal detection, Scattering
Rare circulating tumor cells (CTCs) and circulating tumor cells clusters (CTCCs) have been shown to increase the metastatic potential of tumors, with CTCCs increasing metastatic potential by 23 to 50 times. Due to the high metastatic potential of CTCCs, there is a growing interest in the detection and isolation of these clusters to improve diagnosis, stratification, and treatment of cancer patients. This study aims to utilize a custom confocal back scatter and fluorescence flow cytometer (CBSFFC) that will leverage the high sensitivity of flow cytometers for in vitro and in vivo detection of CTCCs without the use of exogenous contrast agents.
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