Experimenting liver fibrosis diagnostic by two photon excitation microscopy and Bag-of-Features image classification

Suitability of Fourier transform infrared microscopy for the diagnosis of cystic echinococcosis in human tissue sections

Cystic echinococcosis (CE) is a global health concern caused by cestodes, posing diagnostic challenges due to nonspecific symptoms and inconclusive radiographic results. Diagnosis relies on histopathological evaluation of affected tissue, demanding comprehensive tools. In this retrospective case study, Fourier transform infrared microscopy was explored for detecting and identifying CE through biochemical changes in human tissue sections. Tissue samples from 11 confirmed CE patients were analyzed. Archived FFPE blocks were cut and stained, and then CE-positive unstained sections were examined using Fourier transform infrared microscopy post-deparaffinization. Results revealed the method's ability to distinguish echinococcus elements from human tissue, irrespective of organ type. This research showcases the potential of mid-infrared microscopy as a valuable diagnostic tool for CE, offering promise in enhancing diagnostic precision in the face of the disease's complexities.

SNR enhanced high-speed two-photon microscopy using a pulse picker and time gating detection

Two-photon microscopy (TPM) is an attractive biomedical imaging method due to its large penetration depth and optical sectioning capability. In particular, label-free autofluorescence imaging offers various advantages for imaging biological samples. However, relatively low intensity of autofluorescence leads to low signal-to-noise ratio (SNR), causing practical challenges for imaging biological samples. In this study, we present TPM using a pulse picker to utilize low pulse repetition rate of femtosecond pulsed laser to increase the pulse peak power of the excitation source leading to higher emission of two-photon fluorescence with the same average illumination power. Stronger autofluorescence emission allowed us to obtain higher SNR images of arterial and liver tissues. In addition, by applying the time gating detection method to the pulse signals obtained by TPM, we were able to significantly reduce the background noise of two-photon images. As a result, our TPM system using the pulsed light source with a 19 times lower repetition rate allowed us to obtain the same SNR image more than 19 times faster with the same average power. Although high pulse energy can increase the photobleaching, we also observed that high-speed imaging with low total illumination energy can mitigate the photobleaching effect to a level similar to that of conventional illumination with a high repetition rate. We anticipate that this simple approach will provide guidance for SNR enhancement with high-speed imaging in TPM as well as other nonlinear microscopy.

Label-free multiphoton microscopy enables histopathological assessment of colorectal liver metastases and supports automated classification of neoplastic tissue

As the state of resection margins is an important prognostic factor after extirpation of colorectal liver metastases, surgeons aim to obtain negative margins, sometimes elaborated by resections of the positive resection plane after intraoperative frozen sections. However, this is time consuming and results sometimes remain unclear during surgery. Label-free multimodal multiphoton microscopy (MPM) is an optical technique that retrieves morpho-chemical information avoiding all staining and that can potentially be performed in real-time. Here, we investigated colorectal liver metastases and hepatic tissue using a combination of three endogenous nonlinear signals, namely: coherent anti-Stokes Raman scattering (CARS) to visualize lipids, two-photon excited fluorescence (TPEF) to visualize cellular patterns, and second harmonic generation (SHG) to visualize collagen fibers. We acquired and analyzed over forty thousand MPM images of metastatic and normal liver tissue of 106 patients. The morphological information with biochemical specificity produced by MPM allowed discriminating normal liver from metastatic tissue and discerning the tumor borders on cryosections as well as formalin-fixed bulk tissue. Furthermore, automated tissue type classification with a correct rate close to 95% was possible using a simple approach based on discriminant analysis of texture parameters. Therefore, MPM has the potential to increase the precision of resection margins in hepatic surgery of metastases without prolonging surgical intervention.

Sparsity-based nonlinear reconstruction of optical parameters in two-photon photoacoustic computed tomography

We present a new nonlinear optimization approach for the sparse reconstruction of single-photon absorption and two-photon absorption coefficients in the photoacoustic computed tomography (PACT). This framework comprises of minimizing an objective functional involving a least squares fit of the interior pressure field data corresponding to two boundary source functions, where the absorption coefficients and the photon density are related through a semi-linear elliptic partial differential equation (PDE) arising in PAT. Further, the objective functional consists of an L ¹ regularization term that promotes sparsity patterns in absorption coefficients. The motivation for this framework primarily comes from some recent works related to solving inverse problems in acousto-electric tomography and current density impedance tomography. We provide a new proof of existence and uniqueness of a solution to the semi-linear PDE. Further, a proximal method, involving a Picard solver for the semi-linear PDE and its adjoint, is used to solve the optimization problem. Several numerical experiments are presented to demonstrate the effectiveness of the proposed framework.

SmartFit: Smartphone Application for Garment Fit Detection

The apparel e-commerce industry is growing day by day. In recent times, consumers are particularly interested in an easy and time-saving way of online apparel shopping. In addition, the COVID-19 pandemic has generated more need for an effective and convenient online shopping solution for consumers. However, online shopping, particularly online apparel shopping, has several challenges for consumers. These issues include sizing, fit, return, and cost concerns. Especially, the fit issue is one of the cardinal factors causing hesitance and drawback in online apparel purchases. The conventional method of clothing fit detection based on body shapes relies upon manual body measurements. Since no convenient and easy-to-use method has been proposed for body shape detection, we propose an interactive smartphone application, “SmartFit”, that will provide the optimal fitting clothing recommendation to the consumer by detecting their body shape. This optimal recommendation is provided by using image processing and machine learning that are solely dependent on smartphone images. Our preliminary assessment of the developed model shows an accuracy of 87.50% for body shape detection, producing a promising solution to the fit detection problem persisting in the digital apparel market.

Quantification of Liver Fibrosis—A Comparative Study

Liver disease has been targeted as the fifth most common cause of death worldwide and tends to steadily rise. In the last three decades, several publications focused on the quantification of liver fibrosis by means of the estimation of the collagen proportional area (CPA) in liver biopsies obtained from digital image analysis (DIA). In this paper, early and recent studies on this topic have been reviewed according to these research aims: the datasets used for the analysis, the employed image processing techniques, the obtained results, and the derived conclusions. The purpose is to identify the major strengths and “gray-areas” in the landscape of this topic.

Multiphoton microscopy of the dermoepidermal junction and automated identification of dysplastic tissues with deep learning

Histopathological image analysis performed by a trained expert is currently regarded as the gold-standard for the diagnostics of many pathologies, including cancers. However, such approaches are laborious, time consuming and contain a risk for bias or human error. There is thus a clear need for faster, less intrusive and more accurate diagnostic solutions, requiring also minimal human intervention. Multiphoton microscopy (MPM) can alleviate some of the drawbacks specific to traditional histopathology by exploiting various endogenous optical signals to provide virtual biopsies that reflect the architecture and composition of tissues, both in-vivo or ex-vivo. Here we show that MPM imaging of the dermoepidermal junction (DEJ) in unstained fixed tissues provides useful cues for a histopathologist to identify the onset of non-melanoma skin cancers. Furthermore, we show that MPM images collected on the DEJ, besides being easy to interpret by a trained specialist, can be automatically classified into healthy and dysplastic classes with high precision using a Deep Learning method and existing pre-trained convolutional neural networks. Our results suggest that deep learning enhanced MPM for in-vivo skin cancer screening could facilitate timely diagnosis and intervention, enabling thus more optimal therapeutic approaches.

Recent advances in multiphoton microscopy combined with nanomaterials in the field of disease evolution and clinical applications to liver cancer

Multiphoton microscopy (MPM) is expected to become a powerful clinical tool, with its unique advantages of being label-free, high resolution, deep imaging depth, low light photobleaching and low phototoxicity. Nanomaterials, with excellent physical and chemical properties, are biocompatible and easy to prepare and functionalize. The addition of nanomaterials exactly compensates for some defects of MPM, such as the weak endogenous signal strength, limited imaging materials, insufficient imaging depth and lack of therapeutic effects. Therefore, combining MPM with nanomaterials is a promising biomedical imaging method. Here, we mainly review the principle of MPM and its application in liver cancer, especially in disease evolution and clinical applications, including monitoring tumor progression, diagnosing tumor occurrence, detecting tumor metastasis, and evaluating cancer therapy response. Then, we introduce the latest advances in the combination of MPM with nanomaterials, including the MPM imaging of gold nanoparticles (AuNPs) and carbon dots (CDs). Finally, we also propose the main challenges and future research directions of MPM technology in HCC.

Whole-Section Tumor Micro-Architecture Analysis by a Two-Dimensional Phasor-Based Approach Applied to Polarization-Dependent Second Harmonic Imaging

Second Harmonic Generation (SHG) microscopy has gained much interest in the histopathology field since it allows label-free imaging of tissues simultaneously providing information on their morphology and on the collagen microarchitecture, thereby highlighting the onset of pathologies and diseases. A wide request of image analysis tools is growing, with the aim to increase the reliability of the analysis of the huge amount of acquired data and to assist pathologists in a user-independent way during their diagnosis. In this light, we exploit here a set of phasor-parameters that, coupled to a 2-dimensional phasor-based approach (μMAPPS, Microscopic Multiparametric Analysis by Phasor projection of Polarization-dependent SHG signal) and a clustering algorithm, allow to automatically recover different collagen microarchitectures in the tissues extracellular matrix. The collagen fibrils microscopic parameters (orientation and anisotropy) are analyzed at a mesoscopic level by quantifying their local spatial heterogeneity in histopathology sections (few mm in size) from two cancer xenografts in mice, in order to maximally discriminate different collagen organizations, allowing in this case to identify the tumor area with respect to the surrounding skin tissue. We show that the "fibril entropy" parameter, which describes the tissue order on a selected spatial scale, is the most effective in enlightening the tumor edges, opening the possibility of their automatic segmentation. Our method, therefore, combined with tissue morphology information, has the potential to become a support to standard histopathology in diseases diagnosis.

Tissue Intrinsic Fluorescence Spectra-Based Digital Pathology of Liver Fibrosis by Marker-Controlled Segmentation

Tissue intrinsic emission fluorescence provides useful diagnostic information for various diseases. Because of its unique feature of spectral profiles depending on tissue types, spectroscopic imaging is a promising tool for accurate evaluation of endogenous fluorophores. However, due to difficulties in discriminating those sources, quantitative analysis remains challenging. In this study, we quantitatively investigated spectral-spatial features of multi-photon excitation fluorescence in normal and diseased livers. For morphometrics of multi-photon excitation spectra, we examined a marker-controlled segmentation approach and its application to liver fibrosis assessment by employing a mouse model of carbon tetrachloride (CCl₄)-induced liver fibrosis. We formulated a procedure of internal marker selection where markers were chosen to reflect typical biochemical species in the liver, followed by image segmentation and local morphological feature extraction. Image segmentation enabled us to apply mathematical morphology analysis, and the local feature was applied to the automated classification test based on a machine learning framework, both demonstrating highly accurate classifications. Through the analyses, we showed that spectral imaging of native fluorescence from liver tissues have the capability of differentiating not only between normal and diseased, but also between progressive disease states. The proposed approach provides the basics of spectroscopy-based digital histopathology of chronic liver diseases, and can be applied to a range of diseases associated with autofluorescence alterations.

Deep learning enables automated scoring of liver fibrosis stages

Current liver fibrosis scoring by computer-assisted image analytics is not fully automated as it requires manual preprocessing (segmentation and feature extraction) typically based on domain knowledge in liver pathology. Deep learning-based algorithms can potentially classify these images without the need for preprocessing through learning from a large dataset of images. We investigated the performance of classification models built using a deep learning-based algorithm pre-trained using multiple sources of images to score liver fibrosis and compared them against conventional non-deep learning-based algorithms - artificial neural networks (ANN), multinomial logistic regression (MLR), support vector machines (SVM) and random forests (RF). Automated feature classification and fibrosis scoring were achieved by using a transfer learning-based deep learning network, AlexNet-Convolutional Neural Networks (CNN), with balanced area under receiver operating characteristic (AUROC) values of up to 0.85–0.95 versus ANN (AUROC of up to 0.87–1.00), MLR (AUROC of up to 0.73–1.00), SVM (AUROC of up to 0.69–0.99) and RF (AUROC of up to 0.94–0.99). Results indicate that a deep learning-based algorithm with transfer learning enables the construction of a fully automated and accurate prediction model for scoring liver fibrosis stages that is comparable to other conventional non-deep learning-based algorithms that are not fully automated.

Comparative study of multiclass classification methods on light microscopic images for hepatic schistosomiasis fibrosis diagnosis

Hepatic schistosomiasis is a prolonged disease resulting mainly from the solvable egg antigen of schistosomiasis infection due to the host's granulomatous cell-mediated immune. Irreversible fibrosis results from the progress of the schistosomal hepatopathy. Sensitive diagnosis of this disease is based on the investigation of the microscopy images, liver tissues, and egg identification. Early diagnosis of schistosomiasis at its initial infection stage is vital to avoid egg-induced irreparable pathological reactions. Typically, there are several classification approaches that can be used for liver fibrosis staging. However, it is unclear which approaches can achieve high accuracy for analyzing and intelligently classifying the liver microscopic images. Consequently, this work aims to study the performance of the different machine learning classifiers for accurate fibrosis level staging of granuloma, namely cellular, fibrocellular and fibrotic granulomas as well as the normal samples. The classifiers include a multi-layer perceptron neural network, a decision tree, discriminant analysis, support vector machine (SVM), nearest neighbor, and the ensemble of classifiers. The statistical features of the microscopic images are extracted from the different fibrosis levels of granuloma, namely cellular, fibrocellular and fibrotic granulomas as well as the normal samples. The results established that the maximum achieved classification accuracies of value 90% were achieved using the subspace discriminant ensemble, the quadratic SVM, the linear SVM, or the linear discriminant classifiers. However, the linear discriminant classifier can be considered the superior classifier as it realized the best area under the curve of value 0.96 during the classification of the cellular granuloma as well as the fibro-cellular granuloma fibrosis levels.

Quantitative second harmonic generation microscopy for the structural characterization of capsular collagen in thyroid neoplasms

Quantitative second harmonic generation microscopy was used to investigate collagen organization in the fibrillar capsules of human benign and malignant thyroid nodules. We demonstrate that the combination of texture analysis and second harmonic generation images of collagen can be used to differentiate between capsules surrounding the thyroid follicular adenoma and papillary carcinoma nodules. Our findings indicate that second harmonic generation microscopy can provide quantitative information about the collagenous capsule surrounding both the thyroid and thyroid nodules, which may complement traditional histopathological examination.

Quantitative Morphometry for Osteochondral Tissues Using Second Harmonic Generation Microscopy and Image Texture Information

Osteoarthritis (OA) is a chronic joint disorder involving degeneration of articular cartilage and subchondral bone in joints. We previously established a second harmonic generation (SHG) imaging technique for evaluating degenerative changes to articular cartilage in an OA mouse model. SHG imaging, an optical label-free technique, enabled observation of collagen fibrils, and characterized critical changes in the collagenous patterns of the joints. However, it still remains to be determined how morphological changes in the organization of tissue collagen fibrils should be quantified. In this study, we addressed this issue by employing an approach based on texture analysis. Image texture analysis using the gray level co-occurrence matrix was explored to extract image features. We investigated an image patch-based strategy, in which texture features were extracted on individual patches derived from original images to capture local structural patterns in them. We verified that this analysis enables discrimination of cartilaginous and osseous tissues in mouse joints. Moreover, we applied this method to OA cartilage pathology assessment, and observed improvements in the performance results compared with those obtained using an existing feature descriptor. The proposed approach can be applied to a wide range of conditions associated with collagen remodeling and diseases of cartilage and bone.

Hybrid feature extraction techniques for microscopic hepatic fibrosis classification

Chronic liver diseases' hallmark is the fibrosis that results in liver function failure in advanced stages. One of the serious parasitic diseases affecting the liver tissues is schistosomiasis. Immunologic reactions to Schistosoma eggs leads to accumulation of collagen in the hepatic parenchyma causing fibrosis. Thus, monitoring and reporting the staging of the histopathological information related to liver fibrosis are essential for accurate diagnosis and therapy of the chronic liver diseases. Automated assessment of the microscopic liver tissue images is an essential process. For accurate and timeless assessment, an automated image analysis and classification of different stages of fibrosis can be employed as an efficient procedure. In this work, granuloma stages, namely cellular, fibrocellular, and fibrotic granulomas along with normal liver samples were classified after features extraction. In this work, a new hybrid combination of statistical features with empirical mode decomposition (EMD) is proposed. These combined features are further classified using the back-propagation neural network (BPNN). A comparative study of the used classifier with the support vector machine is also conducted. The comparative results established that the BPNN achieved superior accuracy of 98.3% compared to the linear SVM, quadratic SVM, and cubic SVM that provided 85%, 84%, and 80%; respectively. In conclusion, this work is of special value that provides promising results for early prediction of the liver fibrosis in schistosomiais and other fibrotic liver diseases in no time with expected better prognosis after treatment.

Assessment of Fatty Liver in Models of Disease Programming

Nonalcoholic fatty liver disease (NAFLD) is currently the most common cause of chronic liver disease worldwide and is present in a third of the general population and the majority of individuals with obesity and type 2 diabetes. Importantly, NAFLD can progress to severe nonalcoholic steatohepatitis (NASH), associated with liver failure and hepatocellular carcinoma. Recent research efforts have extensively focused on identifying factors contributing to the additional "hit" required to promote NALFD disease progression. The maternal diet, and in particular a high-fat diet (HFD), may be one such hit "priming" the development of severe fatty liver disease, a notion supported by the increasing incidence of NAFLD among children and adolescents in Westernized countries. In recent years, a plethora of key studies have used murine models of maternal obesity to identify fundamental mechanisms such as lipogenesis, mitochondrial function, inflammation, and fibrosis that may underlie the developmental priming of NAFLD. In this chapter, we will address key considerations for constructing experimental models and both conventional and advanced methods of quantifying NAFLD disease status.

A Study on Image Quality in Polarization-Resolved Second Harmonic Generation Microscopy

Second harmonic generation (SHG) microscopy represents a very powerful tool for tissue characterization. Polarization-resolved SHG (PSHG) microscopy extends the potential of SHG, by exploiting the dependence of SHG signals on the polarization state of the excitation beam. Among others, this dependence translates to the fact that SHG images collected under different polarization configurations exhibit distinct characteristics in terms of content and appearance. These characteristics hold deep implications over image quality, as perceived by human observers or by image analysis methods custom designed to automatically extract a quality factor from digital images. Our work addresses this subject, by investigating how basic image properties and the outputs of no-reference image quality assessment methods correlate to human expert opinion in the case of PSHG micrographs. Our evaluation framework is based on SHG imaging of collagen-based ocular tissues under different linear and elliptical polarization states of the incident light.

Systematic quantification of histological patterns shows accuracy in reflecting cirrhotic remodeling

BACKGROUND AND AIM

There still lacks a tool for precisely evaluating cirrhotic remodeling. Histologic distortion characterized in cirrhosis (i.e. cirrhotic patterns) has a validated pathophysiological meaning and potential relevance to clinical complications. We aimed to establish a new tool to quantify the cirrhotic patterns and test it for reflecting the cirrhotic remodeling.

METHODS

We designed a computerized algorithm, named qCP, dedicated for the analysis of liver images acquired by second harmonic microscopy. We evaluated its measurement by using a cohort of 95 biopsies (Ishak staging F4/5/6 = 33/35/27) of chronic hepatitis B and a carbon tetrachloride-intoxicated rat model for simulating the bidirectional cirrhotic change.

RESULTS

QCP can characterize 14 histological cirrhosis parameters involving the nodules, septa, sinusoid, and vessels. For chronic hepatitis B biopsies, the mean overall intra-observer and inter-observer agreement was 0.94 ± 0.08 and 0.93 ± 0.09, respectively. The robustness in resisting sample adequacy-related scoring error was demonstrated. The proportionate areas of total (collagen proportionate area), septal (septal collagen proportionate area [SPA]), sinusoidal, and vessel collagen, nodule area, and nodule density (ND) were associated with Ishak staging (P < 0.01 for all). But only ND and SPA were independently associated (P ≤ 0.001 for both). A histological cirrhosis parameters-composed qCP-index demonstrated an excellent accuracy in quantitatively diagnosing evolving cirrhosis (areas under receiver operating characteristic curves 0.95-0.92; sensitivity 0.93-0.82; specificity 0.94-0.85). In the rat model, changes in collagen proportionate area, SPA, and ND had strong correlations with both cirrhosis progression and regression and faithfully characterized the histological evolution.

CONCLUSIONS

QCP preliminarily demonstrates potential for quantitating cirrhotic remodeling with high resolution and accuracy. Further validation with in-study cohorts and multiple-etiologies is warranted.

Longitudinal Analysis for Disease Progression via Simultaneous Multi-Relational Temporal-Fused Learning

It is highly desirable to predict the progression of Alzheimer's disease (AD) of patients [e.g., to predict conversion of mild cognitive impairment (MCI) to AD], especially longitudinal prediction of AD is important for its early diagnosis. Currently, most existing methods predict different clinical scores using different models, or separately predict multiple scores at different future time points. Such approaches prevent coordinated learning of multiple predictions that can be used to jointly predict clinical scores at multiple future time points. In this paper, we propose a joint learning method for predicting clinical scores of patients using multiple longitudinal prediction models for various future time points. Three important relationships among training samples, features, and clinical scores are explored. The relationship among different longitudinal prediction models is captured using a common feature set among the multiple prediction models at different time points. Our experimental results based on the Alzheimer's disease neuroimaging initiative (ADNI) database shows that our method achieves considerable improvement over competing methods in predicting multiple clinical scores.

Visualizing liver anatomy, physiology and pharmacology using multiphoton microscopy

Multiphoton microscopy (MPM) has become increasingly popular and widely used in both basic and clinical liver studies over the past few years. This technology provides insights into deep live tissues with less photobleaching and phototoxicity, which helps us to better understand the cellular morphology, microenvironment, immune responses and spatiotemporal dynamics of drugs and therapeutic cells in the healthy and diseased liver. This review summarizes the principles, opportunities, applications and limitations of MPM in hepatology. A key emphasis is on the use of fluorescence lifetime imaging (FLIM) to add additional quantification and specificity to the detection of endogenous fluorescent species in the liver as well as exogenous molecules and nanoparticles that are applied to the liver in vivo. We anticipate that in the near future MPM-FLIM will advance our understanding of the cellular and molecular mechanisms of liver diseases, and will be evaluated from bench to bedside, leading to real-time histology of human liver diseases.

Mining textural knowledge in biological images: Applications, methods and trends

Texture analysis is a major task in many areas of computer vision and pattern recognition, including biological imaging. Indeed, visual textures can be exploited to distinguish specific tissues or cells in a biological sample, to highlight chemical reactions between molecules, as well as to detect subcellular patterns that can be evidence of certain pathologies. This makes automated texture analysis fundamental in many applications of biomedicine, such as the accurate detection and grading of multiple types of cancer, the differential diagnosis of autoimmune diseases, or the study of physiological processes. Due to their specific characteristics and challenges, the design of texture analysis systems for biological images has attracted ever-growing attention in the last few years. In this paper, we perform a critical review of this important topic. First, we provide a general definition of texture analysis and discuss its role in the context of bioimaging, with examples of applications from the recent literature. Then, we review the main approaches to automated texture analysis, with special attention to the methods of feature extraction and encoding that can be successfully applied to microscopy images of cells or tissues. Our aim is to provide an overview of the state of the art, as well as a glimpse into the latest and future trends of research in this area.

Quantitative imaging of fibrotic and morphological changes in liver of non-alcoholic steatohepatitis (NASH) model mice by second harmonic generation (SHG) and auto-fluorescence (AF) imaging using two-photon excitation microscopy (TPEM)

Non-alcoholic steatohepatitis (NASH) is a common liver disorder caused by fatty liver. Because NASH is associated with fibrotic and morphological changes in liver tissue, a direct imaging technique is required for accurate staging of liver tissue. For this purpose, in this study we took advantage of two label-free optical imaging techniques, second harmonic generation (SHG) and auto-fluorescence (AF), using two-photon excitation microscopy (TPEM). Three-dimensional ex vivo imaging of tissues from NASH model mice, followed by image processing, revealed that SHG and AF are sufficient to quantitatively characterize the hepatic capsule at an early stage and parenchymal morphologies associated with liver disease progression, respectively.

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Combination of two label-free optical imaging techniques for the evaluation of liver disease is proposed.

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SHG is sufficient to quantitatively characterize the hepatic capsule at an early stage of NASH.

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Auto-fluorescence is useful to evaluate the parenchymal morphologies associated with liver disease progression.

Label-free fluorescence lifetime and second harmonic generation imaging microscopy improves quantification of experimental renal fibrosis

All forms of progressive renal diseases develop a final pathway of tubulointerstitial fibrosis and glomerulosclerosis. Renal fibrosis is usually quantified using histological staining, a process that is time-consuming and pathologist dependent. Here we develop a fast and operator-independent method to measure fibrosis utilizing the murine unilateral ureteral obstruction model which manifests a time-dependent fibrotic increase in obstructed kidneys while the contralateral kidneys are used as controls. After ureteral obstruction, kidneys were analyzed at 7, 14, and 21 days. Fibrosis was quantified using fluorescence lifetime imaging (FLIM) and second harmonic generation (SHG) in a Deep Imaging via Enhanced photon Recovery deep tissue imaging microscope. This microscope was developed for deep tissue along with second and third harmonic generation imaging and has extraordinary sensitivity toward harmonic generation. SHG data suggest the presence of more fibrillar collagen in the obstructed kidneys. The combination of short-wavelength FLIM and SHG analysis results in a robust assessment procedure independent of observer interpretation and let us create criteria to quantify the extent of fibrosis directly from the image. Thus, the FLIM-SHG technique shows remarkable improvement in quantification of renal fibrosis compared to standard histological techniques.

Discriminative Learning for Alzheimer's Disease Diagnosis via Canonical Correlation Analysis and Multimodal Fusion

To address the challenging task of diagnosing neurodegenerative brain disease, such as Alzheimer's disease (AD) and mild cognitive impairment (MCI), we propose a novel method using discriminative feature learning and canonical correlation analysis (CCA) in this paper. Specifically, multimodal features and their CCA projections are concatenated together to represent each subject, and hence both individual and shared information of AD disease are captured. A discriminative learning with multilayer feature hierarchy is designed to further improve performance. Also, hybrid representation is proposed to maximally explore data from multiple modalities. A novel normalization method is devised to tackle the intra- and inter-subject variations from the multimodal data. Based on our extensive experiments, our method achieves an accuracy of 96.93% [AD vs. normal control (NC)], 86.57 % (MCI vs. NC), and 82.75% [MCI converter (MCI-C) vs. MCI non-converter (MCI-NC)], respectively, which outperforms the state-of-the-art methods in the literature.

Quantification of liver fibrosis via second harmonic imaging of the Glisson's capsule from liver surface

Liver surface is covered by a collagenous layer called the Glisson's capsule. The structure of the Glisson's capsule is barely seen in the biopsy samples for histology assessment, thus the changes of the collagen network from the Glisson's capsule during the liver disease progression are not well studied. In this report, we investigated whether non-linear optical imaging of the Glisson's capsule at liver surface would yield sufficient information to allow quantitative staging of liver fibrosis. In contrast to conventional tissue sections whereby tissues are cut perpendicular to the liver surface and interior information from the liver biopsy samples were used, we have established a capsule index based on significant parameters extracted from the second harmonic generation (SHG) microscopy images of capsule collagen from anterior surface of rat livers. Thioacetamide (TAA) induced liver fibrosis animal models was used in this study. The capsule index is capable of differentiating different fibrosis stages, with area under receiver operating characteristics curve (AUC) up to 0.91, making it possible to quantitatively stage liver fibrosis via liver surface imaging potentially with endomicroscopy.

Conjugated Polymer-Based Hybrid Nanoparticles with Two-Photon Excitation and Near-Infrared Emission Features for Fluorescence Bioimaging within the Biological Window

Hybrid fluorescent nanoparticles (NPs) capable of fluorescing near-infrared (NIR) light (centered ∼730 nm) upon excitation of 800 nm laser light were constructed. A new type of conjugated polymer with two-photon excited fluorescence (TPEF) feature, P-F8-DPSB, was used as the NIR-light harvesting component and the energy donor while a NIR fluorescent dye, DPA-PR-PDI, was used as the energy acceptor and the NIR-light emitting component for the construction of the fluorescent NPs. The hybrid NPs possess δ value up to 2.3 × 10(6) GM per particle upon excitation of 800 nm pulse laser. The excellent two-photon absorption (TPA) property of the conjugated polymer component, together with its high fluorescence quantum yield (ϕ) up to 45% and the efficient energy transfer from the conjugated polymer to NIR-emitting fluorophore with efficiency up to 90%, imparted the hybrid NPs with TPEF-based NIR-input-NIR-output fluorescence imaging ability with penetration depth up to 1200 μm. The practicability of the hybrid NPs for fluorescence imaging in Hela cells was validated.

Discriminative Learning for Automatic Staging of Placental Maturity via Multi-layer Fisher Vector

Currently, placental maturity is performed using subjective evaluation, which can be unreliable as it is highly dependent on the observations and experiences of clinicians. To address this problem, this paper proposes a method to automatically stage placenta maturity from B-mode ultrasound (US) images based on dense sampling and novel feature descriptors. Specifically, our proposed method first densely extracts features with a regular grid based on dense sampling instead of a few unreliable interest points. Followed by, these features are clustered using generative Gaussian mixture model (GMM) to obtain high order statistics of the features. The clustering representatives (i.e., cluster means) are encoded by Fisher vector (FV) for staging accuracy enhancement. Differing from the previous studies, a multi-layer FV is investigated to exploit the spatial information rather than the single layer FV. Experimental results show that the proposed method with the dense FV has achieved an area under the receiver of characteristics (AUC) of 96.77%, sensitivity and specificity of 98.04% and 93.75% for the placental maturity staging, respectively. Our experimental results also demonstrate that the dense feature outperforms the traditional sparse feature for placental maturity staging.