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

Deep Learning Techniques for Cervical Cancer Diagnosis based on Pathology and Colposcopy Images

Cervical cancer is a prevalent disease affecting millions of women worldwide every year. It requires significant attention, as early detection during the precancerous stage provides an opportunity for a cure. The screening and diagnosis of cervical cancer rely on cytology and colposcopy methods. Deep learning, a promising technology in computer vision, has emerged as a potential solution to improve the accuracy and efficiency of cervical cancer screening compared to traditional clinical inspection methods that are prone to human error. This review article discusses cervical cancer and its screening processes, followed by the Deep Learning training process and the classification, segmentation, and detection tasks for cervical cancer diagnosis. Additionally, we explored the most common public datasets used in both cytology and colposcopy and highlighted the popular and most utilized architectures that researchers have applied to both cytology and colposcopy. We reviewed 24 selected practical papers in this study and summarized them. This article highlights the remarkable efficiency in enhancing the precision and speed of cervical cancer analysis by Deep Learning, bringing us closer to early diagnosis and saving lives

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

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

The likelihood of timely treatment for cervical cancer increases with timely detection of abnormal cervical cells. Automated methods of detecting abnormal cervical cells were established because manual identification requires skilled pathologists and is time consuming and prone to error. The purpose of this systematic review is to evaluate the diagnostic performance of artificial intelligence (AI) technologies for the prediction, screening, and diagnosis of cervical cancer and pre-cancerous lesions

Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review

Instance segmentation of nuclei and glands in the histology images is an important step in computational pathology workflow for cancer diagnosis, treatment planning and survival analysis. With the advent of modern hardware, the recent availability of large-scale quality public datasets and the community organized grand challenges have seen a surge in automated methods focusing on domain specific challenges, which is pivotal for technology advancements and clinical translation. In this survey, 126 papers illustrating the AI based methods for nuclei and glands instance segmentation published in the last five years (2017-2022) are deeply analyzed, the limitations of current approaches and the open challenges are discussed. Moreover, the potential future research direction is presented and the contribution of state-of-the-art methods is summarized. Further, a generalized summary of publicly available datasets and a detailed insights on the grand challenges illustrating the top performing methods specific to each challenge is also provided. Besides, we intended to give the reader current state of existing research and pointers to the future directions in developing methods that can be used in clinical practice enabling improved diagnosis, grading, prognosis, and treatment planning of cancer. To the best of our knowledge, no previous work has reviewed the instance segmentation in histology images focusing towards this direction.Comment: 60 pages, 14 figure

An Optical Machine Vision System for Applications in Cytopathology

This paper discusses a new approach to the processes of object detection, recognition and classification in a digital image focusing on problem in Cytopathology. A unique self learning procedure is presented in order to incorporate expert knowledge. The classification method is based on the application of a set of features which includes fractal parameters such as the Lacunarity and Fourier dimension. Thus, the approach includes the characterisation of an object in terms of its fractal properties and texture characteristics. The principal issues associated with object recognition are presented which include the basic model and segmentation algorithms. The self-learning procedure for designing a decision making engine using fuzzy logic and membership function theory is also presented and a novel technique for the creation and extraction of information from a membership function considered. The methods discussed and the algorithms developed have a range of applications and in this work, we focus the engineering of a system for automating a Papanicolaou screening test

Automated Detection of Cervical Pre-Cancerous Lesions Using Regional-Based Convolutional Neural Network

The Cervical Colposcopy image is an image of woman’s cervix taken with a digital colposcope after application of acetic acid. The captured cervical images must be understood for diagnosis, prognosis and treatment planning of the anomalies. This Cervix image understanding is generally performed by skilled medical professionals. However, the scarcity of human medical experts and the fatigue and rough estimate procedures involved with them limit the effectiveness of image understanding performed by skilled medical professionals. This paper, the model uses Regional Based Convolutional Neural Network (R-CNN) to effectively visualize of pre-cancerous lesions and to aid in diagnosis of the disease. The model was trained, on a dataset comprising of 10,383 cervical images samples. The datasets were derived from public dataset repositories. The training samples comprised of type class 1, 2 and 3 traits of cervical precancerous traits. The performance was evaluated using K-nearest -neighbor model over R-CNN. With an accuracy rate of 86%, this approach heralds a promising development in the detection of cervical precancerous lesions. This study findings established that the proposed model in provision of the better accuracy and misclassifications performance than various testing algorithms

Combining machine learning and deep learning approaches to detect cervical cancer in cytology images

This dissertation is centred around the implementation and optimization of a hybrid pipeline for the identification and stratification of abnormal cell regions in cytology images, combining state of the art deep learning (DL) approaches and conventional machine learning (ML) models.Cervical cancer is the fourth most common cancer in women. When diagnosed early on, it is one of the most successfully treatable types of cancer. As such, screening tests are very effective as a prevention measure. These tests involve the analysis of microscopic fields of cytology samples which, when performed manually, is a very demanding task, requiring highly specialized laboratory technologists (cytotechs). Due to this, there has been a great interest in automating the overall screening process. Most of these computer-aided diagnosis systems subject the images from each sample to a set of steps, more notably focus and adequacy assessment, region of interest identification and respective classification. This work is focused on the last two stages, more specifically, the detection of abnormal regions and the classification of their abnormality level. The main approaches can be divided into two types: deep learning architectures and conventional machine learning models, both presenting their own set of advantages and disadvantages. This work explores the combination of both of these approaches in hybrid pipelines to minimize the problems of each one whilst taking advantage of the best they have to offer, ultimately contributing to a decision support system for cervical cancer diagnosis. More specifically, it is proposed a deep-learning approach for the detection of the regions of interest and respective bounding-box generation, followed by a simpler machine-learning model for their classification. Furthermore, a comparative analysis of different hybrid pipelines and algorithms will also be performed, aiming to support future research of similar solutions