Multiple Adaptive Neuro-Fuzzy Inference System with Automatic Features Extraction Algorithm for Cervical Cancer Recognition

To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC)) to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE) algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS) is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy

FTIR imaging: a route toward automated histopathology

The focus of this study is the potential use of FTIR imaging as a tool for objective automated histopathology. The Thesis also reports the use of multivariate statistical techniques to analyse the FTIR imaging data. These include Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), Multivariate Curve Resolution (MCR) and Fuzzy C-Means Clustering (FCM). The development of a new PCA-FCM Clustering hybrid that can automatically detect the optimum clustering structure is also reported. Chapter 1 provides a brief introduction to the use of vibrational spectroscopy to characterise biomolecules in tissues and cells for medical diagnosis. Chapter 2 details the basic histology of a lymph node before proceeding to present imaging results gained from the analysis of both healthy and diseased lymph node tissue sections. The ability of each multivariate technique to discriminate different tissue types is discussed. In addition, the spectral features that are characteristic for each tissue type are reported. The development and application of a new PCA-FCM Clustering algorithm that can automatically determine the best clustering structure is also described in full. The results indicate that cellular abnormality provides changes to both the protein and nucleic acid vibrations. However, similar spectral profiles were identified for highly proliferating cells that were contained within reactive germinal centres of the lymph node. Chapter 3 provides a short introduction to the histology of the cervlx before presenting imaging results that were gained from the analysis of both healthy and diseased cervical tissue sections. The ability of each multivariate technique to discriminate different tissue types is discussed. In addition, the spectral features that are characteristic for each tissue type are described in detail. Novel imaging experiments upon exfoliated cervical cells are also presented. It would appear that cellular abnormality in cervical tissues and cells affects both the protein and nucleic acid features of the spectra. Glycogen and glycoprotein contributions that are prevalent in healthy tissues are also absent. Chapter 4 details sample preparation methods, the instrumentation and procedures used for data acquisition, and the subsequent data processing and multivariate techniques applied to analyse the collected spectral datasets

FTIR imaging: a route toward automated histopathology

The focus of this study is the potential use of FTIR imaging as a tool for objective automated histopathology. The Thesis also reports the use of multivariate statistical techniques to analyse the FTIR imaging data. These include Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), Multivariate Curve Resolution (MCR) and Fuzzy C-Means Clustering (FCM). The development of a new PCA-FCM Clustering hybrid that can automatically detect the optimum clustering structure is also reported. Chapter 1 provides a brief introduction to the use of vibrational spectroscopy to characterise biomolecules in tissues and cells for medical diagnosis. Chapter 2 details the basic histology of a lymph node before proceeding to present imaging results gained from the analysis of both healthy and diseased lymph node tissue sections. The ability of each multivariate technique to discriminate different tissue types is discussed. In addition, the spectral features that are characteristic for each tissue type are reported. The development and application of a new PCA-FCM Clustering algorithm that can automatically determine the best clustering structure is also described in full. The results indicate that cellular abnormality provides changes to both the protein and nucleic acid vibrations. However, similar spectral profiles were identified for highly proliferating cells that were contained within reactive germinal centres of the lymph node. Chapter 3 provides a short introduction to the histology of the cervlx before presenting imaging results that were gained from the analysis of both healthy and diseased cervical tissue sections. The ability of each multivariate technique to discriminate different tissue types is discussed. In addition, the spectral features that are characteristic for each tissue type are described in detail. Novel imaging experiments upon exfoliated cervical cells are also presented. It would appear that cellular abnormality in cervical tissues and cells affects both the protein and nucleic acid features of the spectra. Glycogen and glycoprotein contributions that are prevalent in healthy tissues are also absent. Chapter 4 details sample preparation methods, the instrumentation and procedures used for data acquisition, and the subsequent data processing and multivariate techniques applied to analyse the collected spectral datasets

Deep learning for digitized histology image analysis

“Cervical cancer is the fourth most frequent cancer that affects women worldwide. Assessment of cervical intraepithelial neoplasia (CIN) through histopathology remains as the standard for absolute determination of cancer. The examination of tissue samples under a microscope requires considerable time and effort from expert pathologists. There is a need to design an automated tool to assist pathologists for digitized histology slide analysis. Pre-cervical cancer is generally determined by examining the CIN which is the growth of atypical cells from the basement membrane (bottom) to the top of the epithelium. It has four grades, including: Normal, CIN1, CIN2, and CIN3. In this research, different facets of an automated digitized histology epithelium assessment pipeline have been explored to mimic the pathologist diagnostic approach. The entire pipeline from slide to epithelium CIN grade has been designed and developed using deep learning models and imaging techniques to analyze the whole slide image (WSI). The process is as follows: 1) identification of epithelium by filtering the regions extracted from a low-resolution image with a binary classifier network; 2) epithelium segmentation; 3) deep regression for pixel-wise segmentation of epithelium by patch-based image analysis; 4) attention-based CIN classification with localized sequential feature modeling. Deep learning-based nuclei detection by superpixels was performed as an extension of our research. Results from this research indicate an improved performance of CIN assessment over state-of-the-art methods for nuclei segmentation, epithelium segmentation, and CIN classification, as well as the development of a prototype WSI-level tool”--Abstract, page iv

Otimização de descritores usados nos estudos de cambios associadas à malignidade em imagens digitais de células cervicais

Orientadores: Marco Antonio Garcia de Carvalho, Guilherme Palermo CoelhoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de TecnologiaResumo: O Câncer de Colo de Útero (CCU) é um problema de saúde coletiva em todo o mundo, nesse sentido foram feitos grandes avanços para sua detecção e prevenção. Apesar dos esforços feitos pelos países da América Latina para reduzir os indicadores de mortes por essa doença, eles ainda não são suficientes em comparação com o progresso de outros países europeus.Uma das razões, é que os sistemas de saúde pública em vários países da América têm limitações importantes em seus programas de acompanhamento e prevenção.O vírus do papanicolau está associado a 95 % dos cânceres cervicais, embora as instituições de saúde pública em todo o mundo invistam esforços técnicos, humanos e econômicos para reduzir o impacto da CCU em suas comunidades. Desde 1960, são realizadas pesquisas a respeito ao exame do Papanicolau, considerado este como um dos mecanismos mais utilizados pelo mundo para controlar e diagnosticar esta doença. Alterações Associadas à Malignidade (MAC), são pequenas alterações na morfologia e textura da cromatina, predizendo possíveis lesões malignas associadas ao CCU, tornando-se uma investigação interessante na aplicação do exame do panicolau. A identificação de MAC¿s em imagens de células cervicais é um problema accessível a possíveis investigações, devido às complexidades da identificação visual de estruturas nucleares. A partir das técnicas de Processamento Digital de Imagens (PDI), tem se conseguido grandes avanços, especialmente na obtenção de 400 descritores para o estudo de MAC's, no entanto a pequena quantidade de imagens focadas no estudo MAC, assim como a limitação técnica do equipamento e poucos profissionais que trabalham nesses estudos limitam o progresso nesta área. Esta tese tem como objetivo, otimizar descritores propostos na literatura para o estudo do MAC utilizando PDI. Para atingir este objetivo, foi criado em conjunto com a Fundação Universitária de Ciências para a Saúde da Colômbia (FUCS), um Data set de imagens de células cervicais que possibilitará o estudo de MAC's. Para adquirir imagens para o estudo, foram digitalizadas 6 folhas de pacientes com diferentes patologias que foram diagnosticadas e marcadas por uma cito-técnica especializada. As imagens foram pré-processadas empregando filtros espaciais e núcleos segmentados usando o algoritmo k-means e watershed. Os canais de cor foram separados pela sua contribuição de hematoxilina e corante Orange G6 dos núcleos segmentados; se extraíram 800 descritores morfológicos, de textura, densidade óptica e iluminação dos núcleos para sua posterior classificação. Contribuímos com a criação de um conjunto de dados para o estudo do MAC em imagens de CCU de exames de citologia convencional. Comparamos três classificadores supervisionados, treinados com 795 descritores, 412 descritores, 200 descritores e 962 instâncias. Calculamos e ordenamos os descritores extraídos pela informação obtida de cada um deles. Com um grupo de descritores, a precisão da classificação é 95,3 %. A segmentação dos núcleos mostrou uma precisão de 85,6 %. A otimização dos descritores foi de 4,3% melhor que a dos descritores propostos pela literatura, sendo composta por 30% de descritores de textura, 27% de descritores morfológicos, 11,5% de descritores de densidade óptica e 17% de descritores associados à concordância de níveis de cinzaAbstract: Cervical cancer (CCU) is a collective health problem worldwide, in that sense great advances have been made for its detection and prevention. Despite the efforts made by Latin American countries to reduce the indicators of deaths from this disease, they are still not sufficient compared to the progress of other European countries. One of the reasons is that the public health systems of several countries in the Americas present important limitations in their monitoring and prevention programs. The Human Papilloma Virus is associated with 95% of cervical cancers. Public health institutions around the world invest technical, human, and economic efforts to lessen the impact of the CCU on their communities. The mechanism most used by the world to control and diagnose this disease is the examination of the Human Papilloma. Research on this test has been conducted since 1960. The Malignancy Associated Changes MAC, are slight alterations in the morphology and texture of chromatin predicting possible malignant lesions associated to CCU, becoming one of the promising researches to be applied in the examination of the human papilloma. The identification of MAC's in cervical cell images is an open problem, due to the complexities of visual identification of nuclear structures. From Digital Image Processing (DIP) techniques great advances have been made especially in obtaining 400 descriptors for the study of MAC's, however the small amount of images focused on MAC's study, as well as the technical limitation of the equipment and few professionals who worked to these studies has limited progress in this area. The objective of this thesis is to optimize the descriptors proposed in the literature for the study of MAC using DIP. In order to achieve this objective, a set of cervical cell images was created for the study of MAC's, in conjunction with the Fundación Universitaria de Ciencias para la Salud-Colombia (FUCS). With the purpose of acquiring images for the study, 6 slides of patients with different pathologies were digitalized, which were diagnosed and labeled by a specialized cyto-technique. The images were pre-processed using spatial filters and segmented nuclei using the k-means and watershed algorithm. The color channels were separated by contribution of Hematoxylin and Orange G6 dye from the segmented nuclei; 800 morphological, texture, optical density and illumination descriptors were extracted from the nuclei for later classification. We contributed with the creation of a Data Set for the study of MAC in CCU images of conventional cytology examinations. We compared three supervised classifiers with 795 descriptors, 412 descriptors, 200 descriptors and 962 instances. We calculated and sorted the extracted descriptors by the information gain of each one of them. The optimization of the descriptors was 4.3% better than the descriptors proposed in the literature, consisting of 30% texture descriptors, 27% morphological descriptors, 11.5% optical density descriptors and 17% descriptors associated with the agreement of gray levelsDoutoradoSistemas de Informação e ComunicaçãoDoutor em TecnologiaCAPE

Medical image segmentation using edge-based active contours.

The main purpose of image segmentation using active contours is to extract the object of interest in images based on textural or boundary information. Active contour methods have been widely used in image segmentation applications due to their good boundary detection accuracy. In the context of medical image segmentation, weak edges and inhomogeneities remain important issues that may limit the accuracy of any segmentation method formulated using active contour models. This thesis develops new methods for segmentation of medical images based on the active contour models. Three different approaches are pursued: The first chapter proposes a novel external force that integrates gradient vector flow (GVF) field forces and balloon forces based on a weighting factor computed according to local image features. The proposed external force reduces noise sensitivity, improves performance over weak edges and allows initialization with a single manually selected point. The next chapter proposes a level set method that is based on the minimization of an objective energy functional whose energy terms are weighted according to their relative importance in detecting boundaries. This relative importance is computed based on local edge features collected from the adjacent region inside and outside of the evolving contour. The local edge features employed are the edge intensity and the degree of alignment between the images gradient vector flow field and the evolving contours normal. Finally, chapter 5 presents a framework that is capable of segmenting the cytoplasm of each individual cell and can address the problem of segmenting overlapping cervical cells using edge-based active contours. The main goal of our methodology is to provide significantly fully segmented cells with high accuracy segmentation results. All of the proposed methods are then evaluated for segmentation of various regions in real MRI and CT slices, X-ray images and cervical cell images. Evaluation results show that the proposed method leads to more accurate boundary detection results than other edge-based active contour methods (snake and level-set), particularly around weak edges

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Evaluation of PD-L1 expression in various formalin-fixed paraffin embedded tumour tissue samples using SP263, SP142 and QR1 antibody clones

Background & objectives: Cancer cells can avoid immune destruction through the inhibitory ligand PD-L1. PD-1 is a surface cell receptor, part of the immunoglobulin family. Its ligand PD-L1 is expressed by tumour cells and stromal tumour infltrating lymphocytes (TIL). Methods: Forty-four cancer cases were included in this study (24 triple-negative breast cancers (TNBC), 10 non-small cell lung cancer (NSCLC) and 10 malignant melanoma cases). Three clones of monoclonal primary antibodies were compared: QR1 (Quartett), SP 142 and SP263 (Ventana). For visualization, ultraView Universal DAB Detection Kit from Ventana was used on an automated platform for immunohistochemical staining Ventana BenchMark GX. Results: Comparing the sensitivity of two different clones on same tissue samples from TNBC, we found that the QR1 clone gave higher percentage of positive cells than clone SP142, but there was no statistically significant difference. Comparing the sensitivity of two different clones on same tissue samples from malignant melanoma, the SP263 clone gave higher percentage of positive cells than the QR1 clone, but again the difference was not statistically significant. Comparing the sensitivity of two different clones on same tissue samples from NSCLC, we found higher percentage of positive cells using the QR1 clone in comparison with the SP142 clone, but once again, the difference was not statistically significant. Conclusion: The three different antibody clones from two manufacturers Ventana and Quartett, gave comparable results with no statistically significant difference in staining intensity/ percentage of positive tumour and/or immune cells. Therefore, different PD-L1 clones from different manufacturers can potentially be used to evaluate the PD- L1 status in different tumour tissues. Due to the serious implications of the PD-L1 analysis in further treatment decisions for cancer patients, every antibody clone, staining protocol and evaluation process should be carefully and meticulously validated

Recent Advances in HTLV Research 2015

The human T-cell leukemia virus types 1 and 2 (HTLV-1 and HTLV-2) were both discovered over three decades ago and infect millions people worldwide. HTLV-1 is associated with the adult T-cell leukemia/lymphoma (ATLL) in about 2% of individuals infected, and another 2 to 3% of individuals develop a neurologic disorder called HTLV-associated myelopathy (HAM). HTLV-2 causes HAM in approximately 1 to 2% of infected individuals, but does not cause ATLL. HTLV-1 and HTLV-2 have served as excellent models for the study of the epidemiology and pathogenesis of virus-associated cancers as well as autoimmune conditions such as multiple sclerosis. Recently, two new members—HTLV-3 and HTLV-4—have been discovered in bushmeat hunters from central Africa, which emphasizes the urgent need for continual surveillance for new human retroviruses and their capacity to cause disease. Important public health issues remain open issues to be addressed in spite of the basic epidemiology of HTLV-1 and HTLV-2 being reasonably well defined. Clinical research is needed in developing potential HTLV-1 and HTLV-2 vaccines, as well as development of treatment options for ATLL and HAM. This ‘Recent Advances Issue’ contains both reviews and updates on research that encompasses these areas