Robust Nuclei Segmentation in Cytohistopathological Images Using Statistical Level Set Approach with Topology Preserving Constraint

Computerized assessments of cyto-histological specimens have drawn increased attention in the field of digital pathology as the result of developments in digital whole slide scanners and computer hardwares. Due to the essential role of nucleus in cellular functionality, automatic segmentation of cell nuclei is a fundamental prerequisite for all cyto-histological automated systems. In 2D projection images, nuclei commonly appear to overlap each other, and the separation of severely overlapping regions is one of the most challenging tasks in computer vision. In this thesis, we will present a novel segmentation technique which effectively addresses the problem of segmenting touching or overlapping cell nuclei in cyto-histological images. The proposed framework is mainly based upon a statistical level-set approach along with a topology preserving criteria that successfully carries out the task of segmentation and separation of nuclei at the same time. The proposed method is evaluated qualitatively on Hematoxylin and Eosin stained images, and quantitatively and qualitatively on fluorescent stained images. The results indicate that the method outperforms the conventional nuclei segmentation approaches, e.g. thresholding and watershed segmentation

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Medical Image Segmentation by Deep Convolutional Neural Networks

Medical image segmentation is a fundamental and critical step for medical image analysis. Due to the complexity and diversity of medical images, the segmentation of medical images continues to be a challenging problem. Recently, deep learning techniques, especially Convolution Neural Networks (CNNs) have received extensive research and achieve great success in many vision tasks. Specifically, with the advent of Fully Convolutional Networks (FCNs), automatic medical image segmentation based on FCNs is a promising research field. This thesis focuses on two medical image segmentation tasks: lung segmentation in chest X-ray images and nuclei segmentation in histopathological images. For the lung segmentation task, we investigate several FCNs that have been successful in semantic and medical image segmentation. We evaluate the performance of these different FCNs on three publicly available chest X-ray image datasets. For the nuclei segmentation task, since the challenges of this task are difficulty in segmenting the small, overlapping and touching nuclei, and limited ability of generalization to nuclei in different organs and tissue types, we propose a novel nuclei segmentation approach based on a two-stage learning framework and Deep Layer Aggregation (DLA). We convert the original binary segmentation task into a two-step task by adding nuclei-boundary prediction (3-classes) as an intermediate step. To solve our two-step task, we design a two-stage learning framework by stacking two U-Nets. The first stage estimates nuclei and their coarse boundaries while the second stage outputs the final fine-grained segmentation map. Furthermore, we also extend the U-Nets with DLA by iteratively merging features across different levels. We evaluate our proposed method on two public diverse nuclei datasets. The experimental results show that our proposed approach outperforms many standard segmentation architectures and recently proposed nuclei segmentation methods, and can be easily generalized across different cell types in various organs

Histopathological image analysis: a review,”

Abstract-Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Topology and attention in computational pathology

Histopathology serves as the gold standard in the process of cancer diagnosis and unravelling the disease heterogeneity. In routine practice, a trained histopathologist performs visual examination of tissue glass slides under the microscope. The objective of the visual examination is to observe the morphological appearance of tissue sections, analyse the density of tumour rich areas, spatial arrangement, and architecture of diferent types of cells. However, careful visual examination of tissue slides is a demanding task especially when workloads are high, and the subjective nature of the histological grading inevitably leads to inter- and even intra-observer variability. Attaining high accuracy and objective quantification of tissue specimens in cancer diagnosis are some of the ongoing challenges in modern histopathology. With the recent advent of digital pathology, tissue glass slides can now be scanned with digital slides scanners to produce whole slide images (WSIs). A WSI contains a high-resolution pixel representation of tissue slide, stored in a pyramidal structure and typically containing 1010 pixels. Automated algorithms are generally based on the concepts of digital image analysis which can analyse WSIs to improve the precision and reproducibility in cancer diagnostics. The reliability of the results of an algorithm can be objectively measured and improved against an objective standard. In this thesis, we focus on developing automated methods for quantitative assessment of histology WSIs with the aim of improving the precision and reproducibility of cancer diagnosis. More specifically, the designed automated computational pathology algorithms are based on deep learning models in conjunction with algebraic topology and visual attention mechanisms. To the best of our knowledge, the applicability of attention and topology based methods have not been explored in the domain of computational pathology. In this regard, we propose an algorithm for computing persistent homology profiles (topological features) and propose two variants for effective and reliable tumour segmentation of colorectal cancer WSIs. We show that incorporation of deep features along with topological features improves the overall performance for tumour segmentation. We then present the first-ever systematic study (contest) for scoring the human epidermal growth factor receptor 2 (HER2) biomarker on breast cancer histology WSIs. Further, we devise a reinforcement learning based attention mechanism for HER2 scoring that sequentially identifies and analyses the diagnostically relevant regions within a given image, mimicking the histopathologist who would not usually analyse every part of the slide at the highest magnification. We demonstrate the proposed model outperforms other methods participated in our systematic study, most of them were using state-of-the-art deep convolutional networks. Finally, we propose a multi-task learning framework for simultaneous cell detection and classifi- cation, which we named as Hydra-Net. We then compute an image based biomarker which we refer as digital proximity signature (DPS), to predict overall survival in diffuse large B-cell lymphoma (DLBCL) patients. Our results suggest that patients with high collagen-tumour proximity are likely to experience better overall survival

Computer aided diagnosis algorithms for digital microscopy

Automatic analysis and information extraction from an image is still a highly chal- lenging research problem in the computer vision area, attempting to describe the image content with computational and mathematical techniques. Moreover the in- formation extracted from the image should be meaningful and as most discrimi- natory as possible, since it will be used to categorize its content according to the analysed problem. In the Medical Imaging domain this issue is even more felt because many important decisions that affect the patient care, depend on the use- fulness of the information extracted from the image. Manage medical image is even more complicated not only due to the importance of the problem, but also because it needs a fair amount of prior medical knowledge to be able to represent with data the visual information to which pathologist refer. Today medical decisions that impact patient care rely on the results of laboratory tests to a greater extent than ever before, due to the marked expansion in the number and complexity of offered tests. These developments promise to improve the care of patients, but the more increase the number and complexity of the tests, the more increases the possibility to misapply and misinterpret the test themselves, leading to inappropriate diagnosis and therapies. Moreover, with the increased number of tests also the amount of data to be analysed increases, forcing pathologists to devote much time to the analysis of the tests themselves rather than to patient care and the prescription of the right therapy, especially considering that most of the tests performed are just check up tests and most of the analysed samples come from healthy patients. Then, a quantitative evaluation of medical images is really essential to overcome uncertainty and subjectivity, but also to greatly reduce the amount of data and the timing for the analysis. In the last few years, many computer assisted diagno- sis systems have been developed, attempting to mimic pathologists by extracting features from the images. Image analysis involves complex algorithms to identify and characterize cells or tissues using image pattern recognition technology. This thesis addresses the main problems associated to the digital microscopy analysis in histology and haematology diagnosis, with the development of algorithms for the extraction of useful information from different digital images, but able to distinguish different biological structures in the images themselves. The proposed methods not only aim to improve the degree of accuracy of the analysis, and reducing time, if used as the only means of diagnoses, but also they can be used as intermediate tools for skimming the number of samples to be analysed directly from the pathologist, or as double check systems to verify the correct results of the automated facilities used today

Registration of histology and magnetic resonance imaging of the brain

Combining histology and non-invasive imaging has been attracting the attention of the medical imaging community for a long time, due to its potential to correlate macroscopic information with the underlying microscopic properties of tissues. Histology is an invasive procedure that disrupts the spatial arrangement of the tissue components but enables visualisation and characterisation at a cellular level. In contrast, macroscopic imaging allows non-invasive acquisition of volumetric information but does not provide any microscopic details. Through the establishment of spatial correspondences obtained via image registration, it is possible to compare micro- and macroscopic information and to recover the original histological arrangement in three dimensions. In this thesis, I present: (i) a survey of the literature relative to methods for histology reconstruction with and without the help of 3D medical imaging; (ii) a graph-theoretic method for histology volume reconstruction from sets of 2D sections, without external information; (iii) a method for multimodal 2D linear registration between histology and MRI based on partial matching of shape-informative boundaries

Computer aided diagnosis algorithms for digital microscopy

Automatic analysis and information extraction from an image is still a highly chal- lenging research problem in the computer vision area, attempting to describe the image content with computational and mathematical techniques. Moreover the in- formation extracted from the image should be meaningful and as most discrimi- natory as possible, since it will be used to categorize its content according to the analysed problem. In the Medical Imaging domain this issue is even more felt because many important decisions that affect the patient care, depend on the use- fulness of the information extracted from the image. Manage medical image is even more complicated not only due to the importance of the problem, but also because it needs a fair amount of prior medical knowledge to be able to represent with data the visual information to which pathologist refer. Today medical decisions that impact patient care rely on the results of laboratory tests to a greater extent than ever before, due to the marked expansion in the number and complexity of offered tests. These developments promise to improve the care of patients, but the more increase the number and complexity of the tests, the more increases the possibility to misapply and misinterpret the test themselves, leading to inappropriate diagnosis and therapies. Moreover, with the increased number of tests also the amount of data to be analysed increases, forcing pathologists to devote much time to the analysis of the tests themselves rather than to patient care and the prescription of the right therapy, especially considering that most of the tests performed are just check up tests and most of the analysed samples come from healthy patients. Then, a quantitative evaluation of medical images is really essential to overcome uncertainty and subjectivity, but also to greatly reduce the amount of data and the timing for the analysis. In the last few years, many computer assisted diagno- sis systems have been developed, attempting to mimic pathologists by extracting features from the images. Image analysis involves complex algorithms to identify and characterize cells or tissues using image pattern recognition technology. This thesis addresses the main problems associated to the digital microscopy analysis in histology and haematology diagnosis, with the development of algorithms for the extraction of useful information from different digital images, but able to distinguish different biological structures in the images themselves. The proposed methods not only aim to improve the degree of accuracy of the analysis, and reducing time, if used as the only means of diagnoses, but also they can be used as intermediate tools for skimming the number of samples to be analysed directly from the pathologist, or as double check systems to verify the correct results of the automated facilities used today

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