Quantitative Analysis and Monte Carlo Modeling of Fat-Mediated MRI Relaxation

Hepatic steatosis is the accumulation of fat in the liver, affecting about 25% of the world population. Steatosis can cause lipo-toxicity and eventually lead to fibrosis, cirrhosis and ultimately liver failure if timely interventions are not provided. So, early diagnosis and disease monitoring of steatosis is crucial to reduce morbidity and mortality.Chemical shift based Magnetic Resonance Imaging (MRI) techniques using single and dual R2*(transverse relaxation rate) models have been reported to quantify fat fraction (FF) for assessment of steatosis. However, there is no common consensus between these two models and current data is limited for which model is accurate to quantify FF. Fully characterizing the behavior of the modelsover the entire clinical range of hepatic steatosis is essential to determine the limits of each of the models. However, performing a systematic investigation of the R2*models in patient population is infeasible. This thesis presents a computational approach by building a Monte Carlo based model as an alternative way to examine the R2*-MRI models. A 3D liver volume with impenetrable fat spheres was simulated to mimic hepatic steatosis. The simulation of steatosis was done using realistic data obtained from automatic segmentation and characterization of fat droplets using liver biopsy images. MRI signals were synthesized in the virtual liver volume using Monte Carlo modeling approach. Finally, the R2*behavior was analyzed using both the single and dual R2*models and they were compared against in-vivo calibration to determine their accuracy. Predicted R2*values were within confidence bounds of the published in vivo calibration and single R2*modelshowed higher accuracy than dual R2*model to estimate FF. In conclusion, this research developed a computational framework for creating realistic hepatic steatosis model and synthesizing MRI signal and analyzing R2*behavior in the presence of fat. The developed computational methods will also be generalizable to create other tissue-specific models and study R2*behavior at higher field strengths, for testing new MRI pulse sequences and in presence of other co-existing pathologies such as hepatic iron overload

Detection and Spatial Analysis of Hepatic Steatosis in Histopathology Images using Sparse Linear Models

International audienceHepatic steatosis is a defining feature of nonalco-holic fatty liver disease, emerging with the increasing incidence of obesity and metabolic syndrome. The research in image-based analysis of hepatic steatosis mostly focuses on the quantification of fat in biopsy images. This work furthers the image-based analysis of hepatic steatosis by exploring the spatial characteristics of fat globules in whole slide biopsy images after performing fat detection. An algorithm based on morphological filtering and sparse linear models is presented for fat detection. Then the spatial properties of detected fat globules in relation to the hepatic anatomical structures of central veins and portal tracts are explored. The test dataset consists of 38 high resolution images from 21 patients. The experimental results provide an insight into the size distributions of fat globules and their location with respect to the anatomical structures

Automated analysis of necrosis and steatosis in histological images : Practical solutions for coping with heterogeneity and variability

Pathological examination of histological tissue sections is essential for the diagnosis of many life-threatening diseases. Demographic change and the growing importance of precision medicine require pathology to become more efficient, reproducible and quantitative. Automated histological image analysis is an important tool to meet these demands. This thesis is based on five research papers that consider specific problems in histological image analysis. The problems are related either to the quantification of necrosis or to the quantification of steatosis in histological sections of liver tissue. Both are typical applications in which tissue structures or cellular structures must be identified and quantitatively analyzed. In this context, the papers address important general challenges in histological image analysis and present broadly applicable solutions. One challenge is spatial heterogeneity of tissue properties, which can make their quantification sensitive to tissue sampling and image analysis errors. As a solution, the papers present novel scores that enable reliable measurement of heterogeneously distributed tissue properties. Another challenge is the huge variability of histological images, which can make machine learning-based analysis methods require large amounts of training data to work robustly. As a solution, the papers show how interactive training can produce accurate results with little training effort. Finally, a practical challenge is achieving a good trade-off between accuracy, efficiency, and simplicity. In this regard, the papers describe pragmatic approaches to enable accurate and fast analysis of gigapixel images on standard computers

Biophysical basis of skin cancer detection using Raman spectroscopy

The goal of this dissertation is to study the potential of Raman spectroscopy in improving the clinical diagnosis of skin cancer, including two main applications: noninvasive screening of melanoma skin cancer and surgical margin detection of nonmelanoma skin cancer. Skin cancer is the most common type of malignancy, accounting for over 5.4 million cases and 10 thousand deaths per year in the United States alone. Like most cancers, the current “gold standard” diagnosis relies on biopsy and histopathology, which is invasive, time-consuming, and costly. Moreover, large numbers of benign lesions are biopsied for melanoma diagnosis, resulting in substantial financial burden and patient discomfort. Therefore, an urgent need exists to develop a noninvasive, fast, and accurate method for skin cancer detection. The first part of the dissertation focuses on exploring the biophysical origin of in vivo melanoma detection. Our group has previously reported on the development of a clinical Raman spectroscopy system towards spectral biopsy of skin; however, the biochemical changes that Raman spectroscopy relies on for accurate melanoma diagnosis remained unclear. As a result, we proposed a biophysical inverse model to address this issue. To build the model, we established a custom confocal Raman microscope to extract in situ human skin constituents spanning normal and various diseased states. Our results indicate collagen, elastin, keratin, cell nucleus, triolein, ceramide, melanin, and water are the most important model components. Furthermore, collagen and triolein are the most relevant markers to discriminate malignant melanoma from benign nevi. The second part of the dissertation discusses the biophysical basis of nonmelanoma skin cancer margin delineation. We discovered the diagnostic markers to accurately differentiate tumor from normal skin, which is critical to maximize positive patient outcomes in skin cancer surgery. The biochemical changes derived from our model were highly correlated with histopathological diagnosis. We further demonstrated the feasibility of a superpixel acquisition approach for rapid classification of tumor boundaries in skin biopsies. Our results suggest Raman spectroscopy will be a powerful tool for intraoperative surgical guidanceBiomedical Engineerin

Imaging biomarkers for steatohepatitis and fibrosis detection in non-alcoholic fatty liver disease

There is a need, in NAFLD management, to develop non-invasive methods to detect steatohepatitis (NASH) and to predict advanced fibrosis stages. We evaluated a tool based on optical analysis of liver magnetic resonance images (MRI) as biomarkers for NASH and fibrosis detection by investigating patients with biopsy-proven NAFLD who underwent magnetic resonance (MR) protocols using 1.5T General Electric (GE) or Philips devices. Two imaging biomarkers (NASHMRI and FibroMRI) were developed, standardised and validated using area under the receiver operating characteristic curve (AUROC) analysis. The results indicated NASHMRI diagnostic accuracy for steatohepatitis detection was 0.83 (95% CI: 0.73–0.93) and FibroMRI diagnostic accuracy for significant fibrosis determination was 0.85 (95% CI: 0.77–0.94). These findings were independent of the MR system used. We conclude that optical analysis of MRI has high potential to define non-invasive imaging biomarkers for the detection of steatohepatitis (NASHMRI) and the prediction of significant fibrosis (FibroMRI) in NAFLD patients.Comisión Europea, 7º Programa Marco FP7/2007–2013 HEALTH-F2-2009-241762 Project Fatty Liver Inhibition of Progression (FLIP)Junta de Andalucía, Consejería de Salud PI-0488-2012/201

Automated assessment of steatosis in murine fatty liver

Although mice are commonly used to study different aspects of fatty liver disease, currently there are no validated fully automated methods to assess steatosis in mice. Accurate detection of macro- and microsteatosis in murine models of fatty liver disease is important in studying disease pathogenesis and detecting potential hepatotoxic signature during drug development. Further, precise quantification of macrosteatosis is essential for quantifying effects of therapies. Here, we develop and validate the performance of automated classifiers built using image processing and machine learning methods for detection of macro- and microsteatosis in murine fatty liver disease and study the correlation of automated quantification of macrosteatosis with expert pathologist’s semi-quantitative grades. The analysis is performed on digital images of 27 Hematoxylin & Eosin stained murine liver biopsy samples. An expert liver pathologist scored the amount of macrosteatosis and also annotated macro- and microsteatosis lesions on the biopsy images using a web-application. Using these annotations, supervised machine learning and image processing techniques, we created classifiers to detect macro- and microsteatosis. For macrosteatosis prediction, the model’s precision, sensitivity and area under the receiver operator characteristic (AUROC) were 94.2%, 95%, 99.1% respectively. When correlated with pathologist’s semi-quantitative grade of steatosis, the model fits with a coefficient of determination value of 0.905. For microsteatosis prediction, the model has precision, sensitivity and AUROC of 79.2%, 77%, 78.1% respectively. Validation by the expert pathologist of classifier’s predictions made on unseen images of biopsy samples showed 100% and 63% accuracy for macro- and microsteatosis, respectively. This novel work demonstrates that fully automated assessment of steatosis is feasible in murine liver biopsies images. Our classifier has excellent sensitivity and accuracy for detection of macrosteatosis in murine fatty liver disease

Towards a Rapid Screening of Liver Grafts at the Operating Room Using Mid-Infrared Spectroscopy

The estimation of steatosis in a liver graft is mandatory prior to liver transplantation, as the risk of graft failure increases with the level of infiltrated fat. However, the assessment of liver steatosis before transplantation is typically based on a qualitative or semiquantitative characterization by visual inspection and palpation and histological analysis. Thus, there is an unmet need for transplantation surgeons to have access to a diagnostic tool enabling an in situ fast classification of grafts prior to extraction. In this study, we have assessed an attenuated total reflection−Fourier transform infrared (ATR−FTIR) spectroscopic method compatible with the requirements of an operation room for the evaluation of the lipid content in human livers. A set of human liver biopsies obtained from organs intended for transplantation were analyzed by expert pathologists, ATR−FTIR spectroscopy, lipid biochemical analysis, and UPLC−ESI(+/−)TOFMS for lipidomic profiling. Comparative analysis of multisource data showed strong correlations between ATR−FTIR, clinical, and lipidomic information. Results show that ATR−FTIR captures a global picture of the lipid composition of the liver, along with information for the quantification of the triradylglycerol content in liver biopsies. Although the methodology performance needs to be further validated, results support the applicability of ATR−FTIR for the in situ determination of the grade of liver steatosis at the operation room as a fast, quantitative method, as an alternative to the qualitative and subjective pathological examination

Ensemble convolutional neural network classification for pancreatic steatosis assessment in biopsy images

Non-alcoholic fatty pancreas disease (NAFPD) is a common and at the same time not extensively examined pathological condition that is significantly associated with obesity, metabolic syndrome, and insulin resistance. These factors can lead to the development of critical pathogens such as type-2 diabetes mellitus (T2DM), atherosclerosis, acute pancreatitis, and pancreatic cancer. Until recently, the diagnosis of NAFPD was based on noninvasive medical imaging methods and visual evaluations of microscopic histological samples. The present study focuses on the quantification of steatosis prevalence in pancreatic biopsy specimens with varying degrees of NAFPD. All quantification results are extracted using a methodology consisting of digital image processing and transfer learning in pretrained convolutional neural networks for the detection of histological fat structures. The proposed method is applied to 20 digitized histological samples, producing an 0.08% mean fat quantification error thanks to an ensemble CNN voting system and 83.3% mean Dice fat segmentation similarity compared to the semi-quantitative estimates of specialist physicians