Karpinski Score under Digital Investigation: A Fully Automated Segmentation Algorithm to Identify Vascular and Stromal Injury of Donors’ Kidneys

In kidney transplantations, the evaluation of the vascular structures and stromal areas is crucial for determining kidney acceptance, which is currently based on the pathologist’s visual evaluation. In this context, an accurate assessment of the vascular and stromal injury is fundamental to assessing the nephron status. In the present paper, the authors present a fully automated algorithm, called RENFAST (Rapid EvaluatioN of Fibrosis And vesselS Thickness), for the segmentation of kidney blood vessels and fibrosis in histopathological images. The proposed method employs a novel strategy based on deep learning to accurately segment blood vessels, while interstitial fibrosis is assessed using an adaptive stain separation method. The RENFAST algorithm is developed and tested on 350 periodic acid–Schiff (PAS) images for blood vessel segmentation and on 300 Massone’s trichrome (TRIC) stained images for the detection of renal fibrosis. In the TEST set, the algorithm exhibits excellent segmentation performance in both blood vessels (accuracy: 0.8936) and fibrosis (accuracy: 0.9227) and outperforms all the compared methods. To the best of our knowledge, the RENFAST algorithm is the first fully automated method capable of detecting both blood vessels and fibrosis in digital histological images. Being very fast (average computational time 2.91 s), this algorithm paves the way for automated, quantitative, and real-time kidney graft assessments

An Investigation into Glomeruli Detection in Kidney H&E and PAS Images using YOLO

Context: Analyzing digital pathology images is necessary to draw diagnostic conclusions by investigating tissue patterns and cellular morphology. However, manual evaluation can be time-consuming, expensive, and prone to inter- and intra-observer variability. Objective: To assist pathologists using computerized solutions, automated tissue structure detection and segmentation must be proposed. Furthermore, generating pixel-level object annotations for histopathology images is expensive and time-consuming. As a result, detection models with bounding box labels may be a feasible solution. Design: This paper studies. YOLO-v4 (You-Only-Look-Once), a real-time object detector for microscopic images. YOLO uses a single neural network to predict several bounding boxes and class probabilities for objects of interest. YOLO can enhance detection performance by training on whole slide images. YOLO-v4 has been used in this paper. for glomeruli detection in human kidney images. Multiple experiments have been designed and conducted based on different training data of two public datasets and a private dataset from the University of Michigan for fine-tuning the model. The model was tested on the private dataset from the University of Michigan, serving as an external validation of two different stains, namely hematoxylin and eosin (H&E) and periodic acid-Schiff (PAS). Results: Average specificity and sensitivity for all experiments, and comparison of existing segmentation methods on the same datasets are discussed. Conclusions: Automated glomeruli detection in human kidney images is possible using modern AI models. The design and validation for different stains still depends on variability of public multi-stain datasets

Karpinski Score under Digital Investigation: A Fully Automated Segmentation Algorithm to Identify Vascular and Stromal Injury of Donors’ Kidneys

In kidney transplantations, the evaluation of the vascular structures and stromal areas is crucial for determining kidney acceptance, which is currently based on the pathologist's visual evaluation. In this context, an accurate assessment of the vascular and stromal injury is fundamental to assessing the nephron status. In the present paper, the authors present a fully automated algorithm, called RENFAST (Rapid EvaluatioN of Fibrosis And vesselS Thickness), for the segmentation of kidney blood vessels and fibrosis in histopathological images. The proposed method employs a novel strategy based on deep learning to accurately segment blood vessels, while interstitial fibrosis is assessed using an adaptive stain separation method. The RENFAST algorithm is developed and tested on 350 periodic acid-Schiff (PAS) images for blood vessel segmentation and on 300 Massone's trichrome (TRIC) stained images for the detection of renal fibrosis. In the TEST set, the algorithm exhibits excellent segmentation performance in both blood vessels (accuracy: 0.8936) and fibrosis (accuracy: 0.9227) and outperforms all the compared methods. To the best of our knowledge, the RENFAST algorithm is the first fully automated method capable of detecting both blood vessels and fibrosis in digital histological images. Being very fast (average computational time 2.91 s), this algorithm paves the way for automated, quantitative, and real-time kidney graft assessments

Biomedical Image Processing and Classification

Biomedical image processing is an interdisciplinary field involving a variety of disciplines, e.g., electronics, computer science, physics, mathematics, physiology, and medicine. Several imaging techniques have been developed, providing many approaches to the study of the human body. Biomedical image processing is finding an increasing number of important applications in, for example, the study of the internal structure or function of an organ and the diagnosis or treatment of a disease. If associated with classification methods, it can support the development of computer-aided diagnosis (CAD) systems, which could help medical doctors in refining their clinical picture

Role of machine learning in early diagnosis of kidney diseases.

Machine learning (ML) and deep learning (DL) approaches have been used as indispensable tools in modern artificial intelligence-based computer-aided diagnostic (AIbased CAD) systems that can provide non-invasive, early, and accurate diagnosis of a given medical condition. These AI-based CAD systems have proven themselves to be reproducible and have the generalization ability to diagnose new unseen cases with several diseases and medical conditions in different organs (e.g., kidneys, prostate, brain, liver, lung, breast, and bladder). In this dissertation, we will focus on the role of such AI-based CAD systems in early diagnosis of two kidney diseases, namely: acute rejection (AR) post kidney transplantation and renal cancer (RC). A new renal computer-assisted diagnostic (Renal-CAD) system was developed to precisely diagnose AR post kidney transplantation at an early stage. The developed Renal-CAD system perform the following main steps: (1) auto-segmentation of the renal allograft from surrounding tissues from diffusion weighted magnetic resonance imaging (DW-MRI) and blood oxygen level-dependent MRI (BOLD-MRI), (2) extraction of image markers, namely: voxel-wise apparent diffusion coefficients (ADCs) are calculated from DW-MRI scans at 11 different low and high b-values and then represented as cumulative distribution functions (CDFs) and extraction of the transverse relaxation rate (R2*) values from the segmented kidneys using BOLD-MRI scans at different echotimes, (3) integration of multimodal image markers with the associated clinical biomarkers, serum creatinine (SCr) and creatinine clearance (CrCl), and (4) diagnosing renal allograft status as nonrejection (NR) or AR by utilizing these integrated biomarkers and the developed deep learning classification model built on stacked auto-encoders (SAEs). Using a leaveone- subject-out cross-validation approach along with SAEs on a total of 30 patients with transplanted kidney (AR = 10 and NR = 20), the Renal-CAD system demonstrated 93.3% accuracy, 90.0% sensitivity, and 95.0% specificity in differentiating AR from NR. Robustness of the Renal-CAD system was also confirmed by the area under the curve value of 0.92. Using a stratified 10-fold cross-validation approach, the Renal-CAD system demonstrated its reproduciblity and robustness with a diagnostic accuracy of 86.7%, sensitivity of 80.0%, specificity of 90.0%, and AUC of 0.88. In addition, a new renal cancer CAD (RC-CAD) system for precise diagnosis of RC at an early stage was developed, which incorporates the following main steps: (1) estimating the morphological features by applying a new parametric spherical harmonic technique, (2) extracting appearance-based features, namely: first order textural features are calculated and second order textural features are extracted after constructing the graylevel co-occurrence matrix (GLCM), (3) estimating the functional features by constructing wash-in/wash-out slopes to quantify the enhancement variations across different contrast enhanced computed tomography (CE-CT) phases, (4) integrating all the aforementioned features and modeling a two-stage multilayer perceptron artificial neural network (MLPANN) classifier to classify the renal tumor as benign or malignant and identify the malignancy subtype. On a total of 140 RC patients (malignant = 70 patients (ccRCC = 40 and nccRCC = 30) and benign angiomyolipoma tumors = 70), the developed RC-CAD system was validated using a leave-one-subject-out cross-validation approach. The developed RC-CAD system achieved a sensitivity of 95.3% ± 2.0%, a specificity of 99.9% ± 0.4%, and Dice similarity coefficient of 0.98 ± 0.01 in differentiating malignant from benign renal tumors, as well as an overall accuracy of 89.6% ± 5.0% in the sub-typing of RCC. The diagnostic abilities of the developed RC-CAD system were further validated using a randomly stratified 10-fold cross-validation approach. The results obtained using the proposed MLP-ANN classification model outperformed other machine learning classifiers (e.g., support vector machine, random forests, and relational functional gradient boosting) as well as other different approaches from the literature. In summary, machine and deep learning approaches have shown potential abilities to be utilized to build AI-based CAD systems. This is evidenced by the promising diagnostic performance obtained by both Renal-CAD and RC-CAD systems. For the Renal- CAD, the integration of functional markers extracted from multimodal MRIs with clinical biomarkers using SAEs classification model, potentially improved the final diagnostic results evidenced by high accuracy, sensitivity, and specificity. The developed Renal-CAD demonstrated high feasibility and efficacy for early, accurate, and non-invasive identification of AR. For the RC-CAD, integrating morphological, textural, and functional features extracted from CE-CT images using a MLP-ANN classification model eventually enhanced the final results in terms of accuracy, sensitivity, and specificity, making the proposed RC-CAD a reliable noninvasive diagnostic tool for RC. The early and accurate diagnosis of AR or RC will help physicians to provide early intervention with the appropriate treatment plan to prolong the life span of the diseased kidney, increase the survival chance of the patient, and thus improve the healthcare outcome in the U.S. and worldwide

Development of Optical Devices for Digital Medicine

Department of Biomedical EngineeringAdvances of technology have made a revolution that interconnects industrial devices and fuses the boundaries of digital, physical and biological spaces. These technologies such as cloud computing, 3D printing technology, big data, internet of things (IOT), artificial intelligence (AI), and maturity of system integrations have been improved every year, changing our daily life quickly in intelligent and convenient ways. In this days, these explosions of technology, changing the way we live and think, is referred to 4th industrial revolution. As we know, every industry is affected by the new waves of technologies, digitalization and connectivity, and the biomedical or medical field is no exception. Healthcare fields have benefited mostly from recent technical improvements, revolutionizing the medical systems in many terms in cost-effective ways. Particularly, ???digital medicine??? has been recently came into the limelight as one of the uprising fields. In digital medicine, traditional medical devices and diagnostic programs have become miniaturized, digitalized, and automated. As taking advantages of digital medicine, specific fields related to digital pathology, point-of-care (POC) diagnostics, and application of deep learning or machine learning technologies have shown the great potentials not only in biomedical academia but also in the revenues of their markets. It allows to connect devices, hospital equipment, and to accelerate efficiencies in health service such as diagnosis, and to reduce the cost of services. Moreover, interconnection between advanced technologies has been improved the access of healthcare to the places where hospital or medical services are limited. Furthermore, artificial intelligence has shown promising results related to disease screening especially using medical images. Although fields in digital medicine are prospering, still there are limitations that needs to be overcome in order to provide further advanced health services to patients in the various situations. In digital pathology, improvements of microscopic technologies, internets, and storage capabilities have reduced the time-consuming processes. The simple transformation of microscopic image to digital have successfully alternated many limitations in the analogue histopathology workflow to efficient and cost saving ways. However, tissue staining is currently referred as one of the bottleneck that makes workflow still lengthy, labor-intensive, and costly. In the POC diagnostic fields, various digitalized portable smartphone-based diagnostic devices have been introduced as alternatives to conventional medical services. These devices have provided the quality assurance of diagnostics by taking advantages of sharing, and quantitative analysis of digital information. However, most of these works have been focused on replacing diagnostic process which mostly done in laboratory settings. As medical imaging devices and trained clinicians or practitioners are limited, there are also high demands on clinical imaging-based diagnostics in developing countries. In this thesis, computational microscope using patterned NIR illumination was developed for label-free quantitative differential phase tissue imaging to bypass the staining process of the pathology workflow. This system overcame the limitations found in the conventional quantitative differential phase contrast in a LED array microscope, allowing to captured light scattering and absorbing specimen while maintaining weak object approximation. Moreover, portable endoscope system was developed integrating the additive production technologies (3D printing), ICT, and optics for POC diagnostics. This innovative POC endoscope demonstrated comparable imaging capability to that of commercialized clinical endoscope system. Furthermore, deep learning and machine learning models have been trained and applied to each devices, respectively. Generative adversarial network (GAN) was applied to our NIR-based QPI system to virtually stain the label-free QPI which look comparable to image that is captured from bright field microscope using labeled tissue. Lastly, POC automated cervical cancer screening system was developed utilizing smartphone-based endoscope system as well as training the machine learning algorithm. 3-5% of acetic acid was applied to the suspicious lesion and its reaction was captured before and after application using smartphone endoscope. This screening system enables to extract the features of cancers and informs the possibility of cancer from endoscopic images.clos

Domain Generalization in Computational Pathology: Survey and Guidelines

Deep learning models have exhibited exceptional effectiveness in Computational Pathology (CPath) by tackling intricate tasks across an array of histology image analysis applications. Nevertheless, the presence of out-of-distribution data (stemming from a multitude of sources such as disparate imaging devices and diverse tissue preparation methods) can cause \emph{domain shift} (DS). DS decreases the generalization of trained models to unseen datasets with slightly different data distributions, prompting the need for innovative \emph{domain generalization} (DG) solutions. Recognizing the potential of DG methods to significantly influence diagnostic and prognostic models in cancer studies and clinical practice, we present this survey along with guidelines on achieving DG in CPath. We rigorously define various DS types, systematically review and categorize existing DG approaches and resources in CPath, and provide insights into their advantages, limitations, and applicability. We also conduct thorough benchmarking experiments with 28 cutting-edge DG algorithms to address a complex DG problem. Our findings suggest that careful experiment design and CPath-specific Stain Augmentation technique can be very effective. However, there is no one-size-fits-all solution for DG in CPath. Therefore, we establish clear guidelines for detecting and managing DS depending on different scenarios. While most of the concepts, guidelines, and recommendations are given for applications in CPath, we believe that they are applicable to most medical image analysis tasks as well.Comment: Extended Versio

A novel image analysis approach to characterise the effects of dietary components on intestinal morphology and immune system in Atlantic salmon

The intestinal tract of salmonids provides a dynamic interface that not only mediates nutrient uptake but also functions as the first line of defence against ingested pathogens. Exposure of the immune system to beneficial microorganisms and different dietary immunostimulants via the intestine has been shown to prime the immune system and help in the development of immune competence. Furthermore, the morphology and function of teleostean intestines are known to respond to feed components and to ingested and resident bacterial communities. Histological appraisal is still generally considered to be the gold standard for sensitive assessment of the effects of such dietary modulation. The aim of the present study was to improve understanding of salmonid intestinal function, structure and dynamics and to use the knowledge gained to develop a model for analysis, which would allow intestinal health to be assessed with respect to different intestinal communities and feed components. Virtual histology, the process of assessing digital images of histological slides, is gaining momentum as an approach to supplement traditional histological evaluation methodologies and at the same time, image analysis of digitised histological sections provides a practical means for quantifiable assessment of structural and functional changes in tissues, being both objective and reproducible. This project focused on the development of a rapid, practical analytical methodology based on advanced image analysis, that was able to measure and characterise a range of features of the intestinal histology of Atlantic salmon in a quantitative manner. In the first research chapter, the development of a novel histological assessment system based upon advanced image analysis was described, this being developed with the help of a soybean feed model known to induce enteropathy in Atlantic salmon. This tool targeted the evaluation of the extent of morphological changes occurring in the distal intestine of Atlantic salmon following dietary modulation. The final analytical methodology arrived at, could be conducted with minimal user-interaction, allowing rapid and objective assessment of 12 continuous variables per histological frame analysed. The processing time required for each histological frame was roughly 20-25 min, which greatly improved the efficiency of conducting such a quantitative assessment with respect to the time taken for a subjective semi-quantitative alternative approach. Significant agreement between the fully automated and the manual morphometric image segmentation was achieved, however, the strength of this quantitative approach was enhanced by the employment of interactive procedures, which enabled the operator / observer to rectify preceding automated segmentation steps, and account for the specimen’s variations. Results indicated that image analysis provided a viable alternative to a pathologist’s manual scoring, being more practical and time-efficient. In the second research chapter, feeding Atlantic salmon a high inclusion level of unrefined SBM (25 %) produced an inflammatory response in the distal intestine as previously described by other authors. The model feed trial successfully generated differentiable states, although these were not, for the most part, systemically differentiable through the majority of standard immunological procedures used, being only detectable morphologically. Quantitation of morphometric parameters associated with histological sections using the newly developed image analysis tool successfully allowed identification of major morphological changes. Image analysis was thus shown to provide a powerful tool for describing the histomorphological structure of Atlantic salmon distal intestine. In turn, the semi-automated image analysis methods were able to distinguish normal intestinal mucosa from those affected by enteritis. While individual parameters were less discriminatory, use of multivariate techniques allowed better discrimination of states and is likely to prove the most productive approach in further studies. Work described in the third research chapter sought to validate the semi-automated image analysis system to establish that it was measuring the parameters it was purported to be measuring, and to provide reassurance that it could reliably measure pre-determined features. This study, using the same sections for semi-quantitative and quantitative analyses, demonstrated that the quantitative indices performed well when compared to analogous semi-quantitative descriptive parameters of assessment for enteritis prognosis. The excellent reproducibility and accuracy performance levels indicated that the image analysis system was a useful and reliable morphometric method for the quantification of SB-induced enteritis in salmon. Other characteristics such as rapidity, simplicity and adaptability favour this method for image analysis, and are particularly useful where less experienced interpreters are performing the analysis. The work described in the fourth research chapter characterised changes in the morphology of the intestinal epithelial cells occurring as a result of dietary modulation and aspects of inflammatory infiltration, using a selected panel of enzyme and IHC markers. To accomplish this, image analysis techniques were used to evaluate and systematically optimise a quantitative immunolabelling assessment protocol. Digital computer-assisted quantification of labelling for cell proliferation and regeneration; programmed cell death or apoptosis; EGCs and t-cell like infiltrates; mobilisation of stress-related protein regenerative processes and facilitation of nutrient uptake and ion transport provided encouraging results. Through the description of the intestinal cellular responses at a molecular level, such IHC expression profiling further characterised the inflammatory reaction generated by the enteropathic diet. In addition, a number of potential diagnostic parameters were described for fish intestinal health e.g. the relative levels of antigenicity and the spatial distribution of antigens in tissues. Work described in the final research chapter focused on detailed characterisation of intestinal MCs / EGCs in order to try to elucidate their functional role in the intestinal immune responses. Through an understanding of their distribution, composition and ultrastructure, the intention was to better characterise these cells and their functional properties. The general morphology, histochemical characteristics and tissue distribution of these cells were explored in detail using histochemical, IHC and immunogold staining / labelling, visualised using light, confocal and TEM microscopy. Despite these extensive investigations, their physiological function and the content of their granules still remain somewhat obscure, although a role as immunodulatory cells reacting to various exogeneous signals through a finely regulated process and comparable to that causing the degranulation of mammalian MCs is suggested. The histochemical staining properties demonstrated for salmonid MCs / EGCs seem to resemble those of mammalian mucosal mast cells, with both acidophilic and basophilic components in their granules, and a granule content containing neuromodulator / neurotransmitter-peptides such as serotonin, met-enkephalin and substance-p. Consequently, distinguishable bio-chromogenic markers have been identified that are of utility in generating a discriminatory profile for image analysis of such cells