Automatic Annotation of Histopathological Images Using a Latent Topic Model Based On Non-negative Matrix Factorization (original) (raw)
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Histopathological Image Analysis: A Review
IEEE Reviews in Biomedical Engineering, 2009
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.
A Brief Study on Histopathological Images
INTERNATIONAL JOURNAL OF RECENT TRENDS IN ENGINEERING & RESEARCH
The process of examining biological tissue under a microscope for detecting the severity of the disease is called histology, it is an essential technique in biomedical research and clinical practice. While slide preparation and imaging is increasingly becoming automated, but the analysis of histology images still require the trained eyes of a pathologist to examine under a microscope. Processing tissues from histopathological images has become now fully computerized, significantly increasing the speed, the labs can produce tissue slides for viewing images digitally. Digitizing these slides, allows pathologist to view these slides on a computer rather than on microscope. routine analysis of tissues selection will be very difficult, manual task that can be completed only by trained pathologists at a huge cost. In the clinical domain, these methods could improve the accuracy and consistency of diagnoses and hence pathologists can focus on the most difficult cases. This research domain could complete the tasks that are time-consuming for humans, and discover new diseases from millions of whole-slide images (WSIs) or precisely delineating tissues within a tumor, allowing for a quantitative comparison of tumours grown under different conditions.
Micro???structural tissue analysis for automatic histopathological image annotation
Microscopy Research and Technique, 2012
This article presents a new approach for extracting high level semantic concepts from digital histopathological images. This strategy provides not only annotation of several biological concepts, but also a coarse location of these concepts. The proposed approach is composed of five main steps: (1) a stain decomposition stage, which separates the contribution of hematoxylin and eosin dyes, (2) a color standardization that corrects color image differences, (3) a part-based representation, which describes the image in terms of the conditional probability of relevant local patches, selected by their stain contributions, (4) a discriminative classification model, which bridges out the found patterns and the biological concepts, (5) a block-based annotation strategy that identifies the multiple biological concepts within an image. A set of 655 skin images, containing 10 biological concepts of skin tissues were used for assessing the proposed approach, obtaining a sensitivity of 84% and a specificity of 67% when annotating images with multiple concepts. Microsc. Res. Tech. 00:000-000,
BioVision: An application for the automated image analysis of histological sections
Neurobiology of Aging, 2006
We describe a computer application, "BioVision", that can be trained to quickly and effectively classify and quantify user definable histological objects (e.g., senile plaques, neurofibrillary tangles) within single or double-labeled immunocytochemically stained sections. For a given image population, BioVision is interactively trained (in Independent User Mode) by an investigator to perform the desired classifications. This training yields a statistical model of the different types of objects occurring in the target image population. The resulting model can then be used (in Automated User Mode) to classify all objects in any image or images from the target population. BioVision simplifies the quantification of complex visual objects and improves inter-rater reliability. The program accomplishes classification in two major stages: pixel classification and blob classification. In pixel classification, each pixel is assigned to one of some number of substance classes, based on its chromatic properties and local context, reflecting basic histological distinctions of interest. In the blob classification phase, the image's pixels are first partitioned into "blobs": maximal connected sets of pixels assigned to the same substance class. Then, based on its size, shape, textural and contextual properties, each blob is assigned to a histological object class. A Bayesian classifier is used in each of the pixel and blob classification stages. We report several tests of BioVision. First, we applied BioVision to classify senile plaques and neurofibrillary tangles in several test cases of Alzheimer's brain immunostained for beta-amyloid and PHF-tau and compared the results to those produced by experienced investigators. BioVision was trained to classify Plaque-type blobs as either plaques or plaque-type nonentities, and tangle-type blobs as either tangles or tangle-type nonentities. BioVision classified the objects with an accuracy comparable to the trained investigator. Next, we applied BioVision to the task of counting all the tangles in hippocampal images from 22 Alzheimer's disease (AD) cases selected to span a broad range of dementia levels from the tissue repository of UC Irvine's Center for the study of Brain Aging and Dementia. The tangle counts produced by BioVision proved to be significantly better predictors of the cases' adjusted MMSE scores than any of tangle load, age at death, post mortem interval or the interval between the last MMSE score and death.
Deep Learning for Histopathological Image Analysis
Deep Learning for Biomedical Data Analysis, 2021
Anatomical Pathology dates back to the 19th century when Rudolf Virchow introduced his concept of cellular pathology and when the technical improvements of light microscopy enabled widespread use of structural criteria to define diseases. Since then, the quality of optical instruments has been constantly evolving. However the central element of the diagnostic process remains the knowledge and experience of pathologists visually classifying observations according to internationally agreed guidelines (e.g., World Health Organisation (WHO) classification), and much of the pre-analytical steps of specimen preparation (e.g., fixation, embedding, sectioning, staining) is only partially automated and still requires many manual steps. Thanks to the recent advent and costeffectiveness of digital scanners, tissue histopathology slides can now be fully digitized and stored as Whole Slide Images (WSI). With the availability and analysis of a much larger set of variables combined with sophisticated imaging and analytic techniques, the traditional paradigm of pathology based on visually descriptive microscopy can be complemented and substantially improved by digital pathology, utilizing screen-based visualization of digital tissue sections and novel analysis tools potentially combining the conventional evaluation by pathologists with a computerbased diagnostic aid system. A central element of such evolving medical utilities and decision support systems will be image analysis, a field in which Deep Learning (DL) has recently made immense progress, notably the work of Lecun et al. [33] on Convolutional Neural Networks (CNNs) and especially the development of very large Artificial Neural Networks (ANNs) that are revolutionizing the field. Indeed, they have surpassed all existing image processing methods in most fields (segmentation, object detection, classification, etc.). All current methods applied to histopathological image analysis will be presented as well as the future technological issues and challenges of this discipline.
A review of graph-based methods for image analysis in digital histopathology
Digital image analysis of histological datasets is a currently expanding field of research. With different stains, magnifications and types of tissues, histological images are inherently complex in nature and contain a wide variety of visual information. Several image analysis techniques are being explored in this direction. However, graph-based methods have recently gained immense popularity, as these methods can effectively describe tissue architecture and provide adequate numeric information for subsequent computer-based analysis. Graphs have the ability to represent spatial arrangements and neighborhood relationships of different tissue components, which are essential characteristics observed visually by pathologists during investigation of specimens. In this paper, we present a comprehensive review of the graph-based methods explored so far in digital histopathology. We also discuss the current limitations and suggest future directions in graph-based tissue image analysis.
AI (artificial intelligence) in histopathology--from image analysis to automated diagnosis
Folia Histochemica et Cytobiologica, 2010
The technological progress in digitalization of complete histological glass slides has opened a new door in tissue -based diagnosis. The presentation of microscopic images as a whole in a digital matrix is called virtual slide. A virtual slide allows calculation and related presentation of image information that otherwise can only be seen by individual human performance. The digital world permits attachments of several (if not all) fields of view and the contemporary visualization on a screen. The presentation of all microscopic magnifications is possible if the basic pixel resolution is less than 0.25 microns. To introduce digital tissue -based diagnosis into the daily routine work of a surgical pathologist requires a new setup of workflow arrangement and procedures. The quality of digitized images is sufficient for diagnostic purposes; however, the time needed for viewing virtual slides exceeds that of viewing original glass slides by far. The reason lies in a slower and more difficult sampling procedure, which is the selection of information containing fields of view. By application of artificial intelligence, tissue -based diagnosis in routine work can be managed automatically in steps as follows: 1. The individual image quality has to be measured, and corrected, if necessary. 2. A diagnostic algorithm has to be applied. An algorithm has be developed, that includes both object based (object features, structures) and pixel based (texture) measures. 3. These measures serve for diagnosis classification and feedback to order additional information, for example in virtual immunohistochemical slides. 4. The measures can serve for automated image classification and detection of relevant image information by themselves without any labeling. 5. The pathologists' duty will not be released by such a system; to the contrary, it will manage and supervise the system, i.e., just working at a "higher level". Virtual slides are already in use for teaching and continuous education in anatomy and pathology. First attempts to introduce them into routine work have been reported. Application of AI has been established by automated immunohistochemical measurement systems (EAMUS, www.diag-nomX.eu). The performance of automated diagnosis has been reported for a broad variety of organs at sensitivity and specificity levels >85%). The implementation of a complete connected AI supported system is in its childhood. Application of AI in digital tissue -based diagnosis will allow the pathologists to work as supervisors and no longer as primary "water carriers". Its accurate use will give them the time needed to concentrating on difficult cases for the benefit of their patients.
Diagnostics
In modern clinical practice, digital pathology has an essential role, being a technological necessity for the activity in the pathological anatomy laboratories. The development of information technology has majorly facilitated the management of digital images and their sharing for clinical use; the methods to analyze digital histopathological images, based on artificial intelligence techniques and specific models, quantify the required information with significantly higher consistency and precision compared to that provided by optical microscopy. In parallel, the unprecedented advances in machine learning facilitate, through the synergy of artificial intelligence and digital pathology, the possibility of diagnosis based on image analysis, previously limited only to certain specialties. Therefore, the integration of digital images into the study of pathology, combined with advanced algorithms and computer-assisted diagnostic techniques, extends the boundaries of the pathologist’s vis...
Automated histopathological image analysis: a review on ROI extraction
This review paper deals with the latest technology developed on computer assisted analysis for histopathology images. The process for locating, analyzing and classification of lethal diseases like cancer, using a microscope by the pathologists is termed as histopathology. Since from decade, this is done through a manual process by the pathologists, which is entirely dependent on the level of expertise of the examiner. The analyzations of digital slides are based on the structure of tissue, cell distribution, and cell shape regularities. The whole process is more prone to internal as well as external observer. In this paper, the computerized image analysis process is reviewed for quantitative tissue diagnosis from histopathology images. A summary is presented, for digital image processing techniques, which is applicable to the area of histopathology digital slide analysis. Automatic extraction of discriminative area from histopathology digital slides is a significant research area. This paper describes the current state of art for extraction of discriminative area or region of interest from histology digital slides and various classification methods for analyzing these digital slides.