Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer

BACKGROUND: Tumor classification is inexact and largely dependent on the qualitative pathological examination of the images of the tumor tissue slides. In this study, our aim was to develop an automated computational method to classify Hematoxylin and Eosin (H&E) stained tissue sections based on cancer tissue texture features. METHODS: Image processing of histology slide images was used to detect and identify adipose tissue, extracellular matrix, morphologically distinct cell nuclei types, and the tubular architecture. The texture parameters derived from image analysis were then applied to classify images in a supervised classification scheme using histologic grade of a testing set as guidance. RESULTS: The histologic grade assigned by pathologists to invasive breast carcinoma images strongly correlated with both the presence and extent of cell nuclei with dispersed chromatin and the architecture, specifically the extent of presence of tubular cross sections. The two parameters that differentiated tumor grade found in this study were (1) the number density of cell nuclei with dispersed chromatin and (2) the number density of tubular cross sections identified through image processing as white blobs that were surrounded by a continuous string of cell nuclei. Classification based on subdivisions of a whole slide image containing a high concentration of cancer cell nuclei consistently agreed with the grade classification of the entire slide. CONCLUSION: The automated image analysis and classification presented in this study demonstrate the feasibility of developing clinically relevant classification of histology images based on micro- texture. This method provides pathologists an invaluable quantitative tool for evaluation of the components of the Nottingham system for breast tumor grading and avoid intra-observer variability thus increasing the consistency of the decision-making process

Automated Nuclei Segmentation of Breast Cancer Histopathology

Automated detection and segmentation of cell nuclei is an essential step in breast cancer histopathology, so that there is improved accuracy, speed, level of automation and adaptability to new application. The goal of this paper is to develop efficient and accurate algorithms for detecting and segmenting cell nuclei in 2-D histological images. In this paper we will implement the utility of our nuclear segmentation algorithm in accurate extraction of nuclear features for automated grading of (a) breast cancer, and (b) distinguishing between cancerous and benign breast histology specimens. In order to address the issue the scheme integrates image information across three different scales: (1) low level information based on pixel values, (2) high-level information based on relationships between pixels for object detection, and(3)domain-specific information based on relationships between histological structures. Low-level information is utilized by a Bayesian Classifier to generate likelihood that each pixel belongs to an object of interest. High-level information is extracted in two ways: (i) by a level-set algorithm, where a contour is evolved in the likelihood scenes generated by the Bayesian classifier to identify object boundaries, and (ii) by a template matching algorithm, where shape models are used to identify glands and nuclei from the low-level likelihood scenes. Structural constraints are imposed via domain specific knowledge in order to verify whether the detected objects do indeed belong to structures of interest. The efficiency of our segmentation algorithm is evaluated by comparing breast cancer grading and benign vs. cancer discrimination accuracies with corresponding accuracies obtained via manual detection and segmentation of glands and nuclei

Label-free cell nuclear imaging by Grüneisen relaxation photoacoustic microscopy

Photoacoustic microscopy (PAM) with ultraviolet (UV) laser illumination has recently been demonstrated as a promising tool that provides fast, label-free, and multilayered histologic imaging of human breast tissue. Thus far, the axial resolution has been determined ultrasonically. To enable optically defined axial resolution, we exploit the Grüneisen relaxation (GR) effect. By imaging mouse brain slices, we show that GRUV-PAM reveals detailed information about three-dimensional cell nuclear distributions and internal structures, which are important diagnostic features for cancers. Due to the nonlinear effect, GRUV-PAM also provides better contrast in images of cell nuclei

КАРТИРОВАНИЕ ХАРАКТЕРИСТИК СВЕРХБОЛЬШИХ ГИСТОЛОГИЧЕСКИХ ИЗОБРАЖЕНИЙ РАКОВОЙ ТКАНИ

Предлагается метод цифровой обработки гистологических изображений сверхбольшого размерапорядка 100 000×100 000 пикселов. Приводится способ построения карт распределения различных параметров по изучаемому образцу ткани. Получаемые карты пригодны как для количественного, так и для визуального анализа специалистами-патологами. Проводится корреляционный анализ между вычисленными параметрами изображений опухолевых тканей и клиническими данными по пациентам

INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY A PATH FOR HORIZING YOUR INNOVATIVE WORK INTENSITY BASED NUCLEI SEGMENTATION OF CANCER CELL

Abstract Automated detection and segmentation of cell nuclei is an essential step in breast cancer cell for improved accuracy, speed, level of automation and adaptability to new application. The goal of this paper is to develop efficient and accurate algorithms for detecting and segmenting cell nuclei in 2-D pathological images. In this paper we will implement the utility of our nuclear segmentation algorithm in accurate extraction of nuclear features for automated grading of (a) breast cancer, and (b) distinguishing between cancerous and benign breast histology specimens. In order to address the issue the scheme integrates image information across three different scales: (1) low level information based on pixel values, (2) highlevel information based on relationships between pixels for object detection, and (3)Intensity-specific information based on relationships between pathological sample. Low-level information is utilized to generate likelihood that each pixel belongs to an object of interest. Highlevel information is extracted by a level-set algorithm, where a contour is evolved in the likelihood scenes generated by the Low-level information to identify object boundaries, and to identify nuclei from the low-level likelihood scenes. Structural limitations are imposed via intensity based specific knowledge in order to verify whether the detected objects do indeed belong to structures of interest. The efficiency of our segmentation algorithm is evaluated by comparing breast cancer grading and automated benign cancer detection of nuclei with corresponding accuracies obtained via manual detection and segmentation of nuclei

TriResNet: A Deep Triple-stream Residual Network for Histopathology Grading

While microscopic analysis of histopathological slides is generally considered as the gold standard method for performing cancer diagnosis and grading, the current method for analysis is extremely time consuming and labour intensive as it requires pathologists to visually inspect tissue samples in a detailed fashion for the presence of cancer. As such, there has been significant recent interest in computer aided diagnosis systems for analysing histopathological slides for cancer grading to aid pathologists to perform cancer diagnosis and grading in a more efficient, accurate, and consistent manner. In this work, we investigate and explore a deep triple-stream residual network (TriResNet) architecture for the purpose of tile-level histopathology grading, which is the critical first step to computer-aided whole-slide histopathology grading. In particular, the design mentality behind the proposed TriResNet network architecture is to facilitate for the learning of a more diverse set of quantitative features to better characterize the complex tissue characteristics found in histopathology samples. Experimental results on two widely-used computer-aided histopathology benchmark datasets (CAMELYON16 dataset and Invasive Ductal Carcinoma (IDC) dataset) demonstrated that the proposed TriResNet network architecture was able to achieve noticeably improved accuracies when compared with two other state-of-the-art deep convolutional neural network architectures. Based on these promising results, the hope is that the proposed TriResNet network architecture could become a useful tool to aiding pathologists increase the consistency, speed, and accuracy of the histopathology grading process.Comment: 9 page

Label-free cell nuclear imaging by Grüneisen relaxation photoacoustic microscopy

Photoacoustic microscopy (PAM) with ultraviolet (UV) laser illumination has recently been demonstrated as a promising tool that provides fast, label-free, and multilayered histologic imaging of human breast tissue. Thus far, the axial resolution has been determined ultrasonically. To enable optically defined axial resolution, we exploit the Grüneisen relaxation (GR) effect. By imaging mouse brain slices, we show that GRUV-PAM reveals detailed information about three-dimensional cell nuclear distributions and internal structures, which are important diagnostic features for cancers. Due to the nonlinear effect, GRUV-PAM also provides better contrast in images of cell nuclei