Quantitative diagnosis of cervical neoplasia using fluorescence lifetime imaging on haematoxylin and eosin stained tissue sections

An oil-in-gel type of organohydrogel loaded with methylprednisolone for the treatment of secondary injuries following spinal cord traumas

The secondary injuries following traumatic spinal cord injury (SCI) is a multiphasic and complex process that is difficult to treat. Although methylprednisolone (MP) is the only available pharmacological regime for SCI treatment, its efficacy remains controversial due to its very narrow therapeutic time window and safety concerns associated with high dosage. In this study, we have developed an oil-in-gel type of organohydrogel (OHG) in which the binary oleic-water phases coexist, for the local delivery of MP. This new OHG is fabricated by a glycol chitosan/oxidized hyaluronic acid hydrophilic network that is uniformly embedded with a biocompatible oil phase, and it can be effectively loaded with MP or other hydrophobic compounds. In addition to spatiotemporally control MP release, this biodegradable OHG also provides a brain tissue-mimicking scaffold that can promote tissue regeneration. OHG remarkably decreases the therapeutic dose of MP in animals and extends its treatment course over 21 d, thereby timely manipulating microglia/macrophages and their associated with signaling molecules to restore immune homeostasis, leading to a long-term functional improvement in a complete transection SCI rat model. Thus, this OHG represents a new type of gel for clinical treatment of secondary injuries in SCI.

Applications of machine learning in time-domain fluorescence lifetime imaging: a review

Many medical imaging modalities have benefited from recent advances in Machine Learning (ML), specifically in deep learning, such as neural networks. Computers can be trained to investigate and enhance medical imaging methods without using valuable human resources. In recent years, Fluorescence Lifetime Imaging (FLIm) has received increasing attention from the ML community. FLIm goes beyond conventional spectral imaging, providing additional lifetime information, and could lead to optical histopathology supporting real-time diagnostics. However, most current studies do not use the full potential of machine/deep learning models. As a developing image modality, FLIm data are not easily obtainable, which, coupled with an absence of standardisation, is pushing back the research to develop models which could advance automated diagnosis and help promote FLIm. In this paper, we describe recent developments that improve FLIm image quality, specifically time-domain systems, and we summarise sensing, signal-to-noise analysis and the advances in registration and low-level tracking. We review the two main applications of ML for FLIm: lifetime estimation and image analysis through classification and segmentation. We suggest a course of action to improve the quality of ML studies applied to FLIm. Our final goal is to promote FLIm and attract more ML practitioners to explore the potential of lifetime imaging.

Machine Learning Enhanced Optical Spectroscopy for Disease Detection

Optical spectroscopy plays an important role in disease detection. Improving the sensitivity and specificity of spectral detection has great importance in the development of accurate diagnosis. The development of artificial intelligence technology provides a great opportunity to improve the detection accuracy through machine learning methods. In this Perspective, we focus on the combination of machine learning methods with the optical spectroscopy methods widely used for disease detection, including absorbance, fluorescence, scattering, FTIR, terahertz, etc. By comparing the spectral analysis with different machine learning methods, we illustrate that the support vector machine and convolutional neural network are most effective, which have potential to further improve the classification accuracy to distinguish disease subtypes if these machine learning methods are used. This Perspective broadens the scope of optical spectroscopy enhanced by machine learning and will be useful for the development of disease detection.

A layer-level multi-scale architecture for lung cancer classification with fluorescence lifetime imaging endomicroscopy

In this paper, we introduce our unique dataset of fluorescence lifetime imaging endo/microscopy (FLIM), containing over 100,000 different FLIM images collected from 18 pairs of cancer/non-cancer human lung tissues of 18 patients by our custom fibre-based FLIM system. The aim of providing this dataset is that more researchers from relevant fields can push forward this particular area of research. Afterwards, we describe the best practice of image post-processing suitable per the dataset. In addition, we propose a novel hierarchically aggregated multi-scale architecture to improve the binary classification performance of classic CNNs. The proposed model integrates the advantages of multi-scale feature extraction at different levels, where layer-wise global information is aggregated with branch-wise local information. We integrate the proposal, namely ResNetZ, into ResNet, and appraise it on the FLIM dataset. Since ResNetZ can be configured with a shortcut connection and the aggregations by Addition or Concatenation, we first evaluate the impact of different configurations on the performance. We thoroughly examine various ResNetZ variants to demonstrate the superiority. We also compare our model with a feature-level multi-scale model to illustrate the advantages and disadvantages of multi-scale architectures at different levels.

Special Series Guest Editorial: Artificial Intelligence and Machine Learning in Biomedical Optics

Guest editors Behrouz Shabestri, Mark Anastasio, Baowei Fei, and Frédéric Leblond provide an overview of the JBO Special Series on Artificial Intelligence Machine Learning in Biomedical Optics.

Deep Learning in ex-vivo Lung Cancer Discrimination using Fluorescence Lifetime Endomicroscopic Images

Fluorescence lifetime is effective in discriminating cancerous tissue from normal tissue, but conventional discrimination methods are primarily based on statistical approaches in collaboration with prior knowledge. This paper investigates the application of deep convolutional neural networks (CNNs) for automatic differentiation of ex-vivo human lung cancer via fluorescence lifetime imaging. Around 70,000 fluorescence images from ex-vivo lung tissue of 14 patients were collected by a custom fibre-based fluorescence lifetime imaging endomicroscope. Five state-of-the-art CNN models, namely ResNet, ResNeXt, Inception, Xception, and DenseNet, were trained and tested to derive quantitative results using accuracy, precision, recall, and the area under receiver operating characteristic curve (AUC) as the metrics. The CNNs were firstly evaluated on lifetime images. Since fluorescence lifetime is independent of intensity, further experiments were conducted by stacking intensity and lifetime images together as the input to the CNNs. As the original CNNs were implemented for RGB images, two strategies were applied. One was retaining the CNNs by putting intensity and lifetime images in two different channels and leaving the remaining channel blank. The other was adapting the CNNs for two-channel input. Quantitative results demonstrate that the selected CNNs are considerably superior to conventional machine learning algorithms. Combining intensity and lifetime images introduces noticeable performance gain compared with using lifetime images alone. In addition, the CNNs with intensity-lifetime RGB image is comparable to the modified two-channel CNNs with intensity-lifetime two-channel input for accuracy and AUC, but significantly better for precision and recall.

Deep learning a boon for biophotonics?

This review covers original articles using deep learning in the biophotonic field published in the last years. In these years deep learning, which is a subset of machine learning mostly based on artificial neural network geometries, was applied to a number of biophotonic tasks and has achieved state-of-the-art performances. Therefore, deep learning in the biophotonic field is rapidly growing and it will be utilized in the next years to obtain real-time biophotonic decision-making systems and to analyze biophotonic data in general. In this contribution, we discuss the possibilities of deep learning in the biophotonic field including image classification, segmentation, registration, pseudostaining and resolution enhancement. Additionally, we discuss the potential use of deep learning for spectroscopic data including spectral data preprocessing and spectral classification. We conclude this review by addressing the potential applications and challenges of using deep learning for biophotonic data.

Discriminating different grades of cervical intraepithelial neoplasia based on label-free phasor fluorescence lifetime imaging microscopy

This study proposed label-free fluorescence lifetime imaging and phasor analysis methods to discriminate different grades of cervical intraepithelial neoplasia (CIN). The human cervical tissue lesions associated with cellular metabolic abnormalities were detected by the status changes of important coenzymes in cells and tissues, reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavin adenine dinucleotide (FAD). Fluorescence lifetime imaging microscopy (FLIM) was used to study human cervical tissues, human cervical epithelial cells, and standard samples. Phasor analysis was applied to reveal the interrelation between the metabolic changes and cancer development, which can distinguish among different stages of cervical lesions from low risk to high risk. This approach also possessed high sensitivity, especially for healthy sites of CIN3 tissues, and indicated the dominance of the glycolytic pathway over oxidative phosphorylation in high-grade cervical lesions. This highly adaptive, sensitive, and rapid diagnostic tool exhibits a great potential for cervical precancer diagnosis.

Dual-mode emission and transmission microscopy for virtual histochemistry using hematoxylin- and eosin-stained tissue sections

In the clinical practice of pathology, trichrome stains are commonly used to highlight collagen and to help evaluate fibrosis. Such stains do delineate collagen deposits but are not molecularly specific and can suffer from staining inconsistencies. Moreover, performing histochemical stain evaluation requires the preparation of additional sections beyond the original hematoxylin- and eosin-stained slides, as well as additional staining steps, which together add cost, time, and workflow complications. We have developed a new microscopy approach, termed DUET (DUal-mode Emission and Transmission) that can be used to extract signals that would typically require special stains or advanced optical methods. Our preliminary analysis demonstrates the potential of using the resulting signals to generate virtual histochemical images that resemble trichrome-stained slides and can support clinical evaluation. We demonstrate advantages of this approach over images acquired from conventional trichrome-stained slides and compare them with images created using second harmonic generation microscopy.

Second-harmonic generation imaging analysis can help distinguish sarcoidosis from tuberculoid leprosy

Sarcoidosis and tuberculoid leprosy (TL) are prototypes of granulomatous inflammation in dermatology, which embody one of the histopathology limitations in distinguishing some diseases. Recent advances in the use of nonlinear optical microscopy in skin have enabled techniques, such as second-harmonic generation (SHG), to become powerful tools to study the physical and biochemical properties of skin. We use SHG images to analyze the collagen network, to distinguish differences between sarcoidosis and TL granulomas. SHG images obtained from skin biopsies of 33 patients with TL and 24 with sarcoidosis retrospectively were analyzed using first-order statistics (FOS) and second-order statistics, such as gray-level co-occurrence matrix (GLCM). Among the four parameters evaluated (optical density, entropy, contrast, and second angular moment), only contrast demonstrated statistical significance, being higher in sarcoidosis (p  =  0.02; 4908.31 versus 2822.17). The results may indicate insufficient differentiating power for most tested FOS and GLCM parameters in classifying sarcoidosis and TL granulomas, when used individually. But in combination with histopathology (H&E and complementary stains, such as silver and fast acid stains), SHG analysis, like contrast, can contribute to distinguishing between these diseases. This study can provide a way to evaluate collagen distribution in granulomatous diseases.

Fluorescence microscopy of H&E stained cervical biopsies to assist the diagnosis and grading of CIN

BACKGROUND

Prevention of cervical cancer is based upon the accurate diagnosis and grading of cervical lesions identified during screening. The pathological classification of cervical intraepithelial neoplasia (CIN) is problematic, as it relies on subjective criteria and is known to have high interobserver variability and low reproducibility. These limitations can result in either over or under treatment of patients. Biomarkers to improve CIN diagnosis have not overcome all these challenges.

MAIN BODY

Here we review the use of a promising optical imaging method using eosin-based fluorescence spectroscopy. This technique is able to perform fluorescent analysis of cervical biopsies directly from hematoxylin and eosin (H&E) stained tissues. Eosin is a brominated derivative of fluorescein. Fluorescence characteristics of protein-eosin complexes can demonstrate tissue changes associated with dysplasia and cancer. In this article we review the progress made towards developing eosin-based fluorescence spectroscopy. We describe the various morphologies seen among the CIN grades with this optical method and highlight the progress made to quantitate the spectral image characteristics.

CONCLUSION

Eosin-based fluorescence spectroscopy can be used to directly examine H&E stained tissue slides. Relevant areas can be imaged and spectral analysis done to obtain objective data to identify and grade cervical lesions.

Phasor-FLIM as a Screening Tool for the Differential Diagnosis of Actinic Keratosis, Bowen's Disease, and Basal Cell Carcinoma

The aim of this study was to distinguish basal cell carcinoma (BCC) from actinic keratosis (AK) and Bowen's disease (BD) by fluorescence lifetimes of hematoxylin and eosin (H&E) and phasor analysis. Pseudocolor images of average fluorescence lifetime (τm) exhibited more contrast than conventional bright field and/or fluorescence images of H&E-stained sections. The mean values (μ) of τm distribution (τm_μ) in three layers of skin were first explored for comparison with the corresponding layers of AK, BD, and BCC. Moreover, analysis of the H&E fluorescence lifetimes in the phasor space was performed by observing clusters in specific regions of the phasor plot. Various structures in the skin were distinguished. Comparisons of phase distributions from the corresponding layers of skin resulted in quantitative separation and calculation of distinctive parameters including coordinate values, diagonal slopes, and phasor areas. The combination of fluorescence lifetime imaging microscopy (FLIM) and phasor approach (phasor-FLIM) provides a simple method for histopathology analysis and can significantly improve the accuracy of bright field H&E diagnosis. We therefore believe that phasor-FLIM is an aided tool with the potential to provide rapid confirmation of diagnostic criteria and classification of histological types of skin neoplasms.

Optical techniques for cervical neoplasia detection

This paper provides an overview of the current research in the field of optical techniques for cervical neoplasia detection and covers a wide range of the existing and emerging technologies. Using colposcopy, a visual inspection of the uterine cervix with a colposcope (a binocular microscope with 3- to 15-fold magnification), has proven to be an efficient approach for the detection of invasive cancer. Nevertheless, the development of a reliable and cost-effective technique for the identification of precancerous lesions, confined to the epithelium (cervical intraepithelial neoplasia) still remains a challenging problem. It is known that even at early stages the neoplastic transformations of cervical tissue induce complex changes and modify both structural and biochemical properties of tissues. The different methods, including spectroscopic (diffuse reflectance spectroscopy, induced fluorescence and autofluorescence spectroscopy, Raman spectroscopy) and imaging techniques (confocal microscopy, optical coherence tomography, Mueller matrix imaging polarimetry, photoacoustic imaging), probe different tissue properties that may serve as optical biomarkers for diagnosis. Both the advantages and drawbacks of these techniques for the diagnosis of cervical precancerous lesions are discussed and compared.

Modern trends in biophotonics for clinical diagnosis and therapy to solve unmet clinical needs

This contribution covers recent original research papers in the biophotonics field. The content is organized into main techniques such as multiphoton microscopy, Raman spectroscopy, infrared spectroscopy, optical coherence tomography and photoacoustic tomography, and their applications in the context of fluid, cell, tissue and skin diagnostics. Special attention is paid to vascular and blood flow diagnostics, photothermal and photodynamic therapy, tissue therapy, cell characterization, and biosensors for biomarker detection.

Enabling Multiphoton and Second Harmonic Generation Imaging in Paraffin-Embedded and Histologically Stained Sections

Nonlinear microscopy, namely multiphoton imaging and second harmonic generation (SHG), is an established noninvasive technique useful for the imaging of extracellular matrix (ECM). Typically, measurements are performed in vivo on freshly excised tissues or biopsies. In this article, we describe the effect of rehydrating paraffin-embedded sections on multiphoton and SHG emission signals and the acquisition of nonlinear images from hematoxylin and eosin (H&E)-stained sections before and after a destaining protocol. Our results reveal that bringing tissue sections to a physiological state yields a significant improvement in nonlinear signals, particularly in SHG. Additionally, the destaining of sections previously processed with H&E staining significantly improves their SHG emission signals during imaging, thereby allowing sufficient analysis of collagen in these sections. These results are important for researchers and pathologists to obtain additional information from paraffin-embedded tissues and archived samples to perform retrospective analysis of the ECM or gain additional information from rare samples.

Enhancement of early cervical cancer diagnosis with epithelial layer analysis of fluorescence lifetime images

This work reports the use of layer analysis to aid the fluorescence lifetime diagnosis of cervical intraepithelial neoplasia (CIN) from H&E stained cervical tissue sections. The mean and standard deviation of lifetimes in single region of interest (ROI) of cervical epithelium were previously shown to correlate to the gold standard histopathological classification of early cervical cancer. These previously defined single ROIs were evenly divided into layers for analysis. A 10-layer model revealed a steady increase in fluorescence lifetime from the inner to the outer epithelial layers of healthy tissue sections, suggesting a close association with cellular maturity. The shorter lifetime and minimal lifetime increase towards the epithelial surface of CIN-affected regions are in good agreement with the absence of cellular maturation in CIN. Mean layer lifetimes in the top-half cervical epithelium were used as feature vectors for extreme learning machine (ELM) classifier discriminations. It was found that the proposed layer analysis technique greatly improves the sensitivity and specificity to 94.6% and 84.3%, respectively, which can better supplement the traditional gold standard cervical histopathological examinations.