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 detection of regions of interest for tissue microarray experiments: an image texture analysis

BACKGROUND: Recent research with tissue microarrays led to a rapid progress toward quantifying the expressions of large sets of biomarkers in normal and diseased tissue. However, standard procedures for sampling tissue for molecular profiling have not yet been established. METHODS: This study presents a high throughput analysis of texture heterogeneity on breast tissue images for the purpose of identifying regions of interest in the tissue for molecular profiling via tissue microarray technology. Image texture of breast histology slides was described in terms of three parameters: the percentage of area occupied in an image block by chromatin (B), percentage occupied by stroma-like regions (P), and a statistical heterogeneity index H commonly used in image analysis. Texture parameters were defined and computed for each of the thousands of image blocks in our dataset using both the gray scale and color segmentation. The image blocks were then classified into three categories using the texture feature parameters in a novel statistical learning algorithm. These categories are as follows: image blocks specific to normal breast tissue, blocks specific to cancerous tissue, and those image blocks that are non-specific to normal and disease states. RESULTS: Gray scale and color segmentation techniques led to identification of same regions in histology slides as cancer-specific. Moreover the image blocks identified as cancer-specific belonged to those cell crowded regions in whole section image slides that were marked by two pathologists as regions of interest for further histological studies. CONCLUSION: These results indicate the high efficiency of our automated method for identifying pathologic regions of interest on histology slides. Automation of critical region identification will help minimize the inter-rater variability among different raters (pathologists) as hundreds of tumors that are used to develop an array have typically been evaluated (graded) by different pathologists. The region of interest information gathered from the whole section images will guide the excision of tissue for constructing tissue microarrays and for high throughput profiling of global gene expression

Comparative Analysis of Kidney Histomorphometry Utilizing Two Distinct Image Processing Software

ABSTRACTBackground: Histopathological examination is critical to evaluate tissue condition. An accurate assessment is necessary for diagnosis establishment. Nowadays, both quantitative and qualitative scoring are enhanced with computer-assisted image analysis to reduce bias. Various software was developed to assist in image analysis. The question of whether the measurement results from one software will be comparable to those from another software may come up, given the wide variety of software options. Nevertheless, this subject is only occasionally discussed.Objective: This study aimed to compare the measurement results from Fiji and QuPath software in kidney histomorphometry.Methods: Normal kidney histological slide was observed. Selected histological structures, including the renal corpuscle area, glomerular area, Bowman space area, inner diameter of proximal, distal, and Henle loop, were measured using QuPath and Fiji software. The measurement results from the two software were compared for value differences and agreement analysis.Results: The renal corpuscle means the area was 12.7x103 µm2 in QuPath and 12.5 x103 µm2 in Fiji. The glomerular area was 7.8 x103 µm2 for both software. The proximal tubule's inner diameters varied from 18.7 to 150.8 µm. Smaller inner diameters were observed in distal tubules (17.1-80.5 µm) and The Henle loop (15.5-69.6 µm). There was no significant difference in measurement results of particular structures between the compared software (P-value > 0.05). The further confirmational analysis supported the similarity between the two measurement results.Conclusion: the measurement result of kidney microstructures using QuPath and Fiji were identical

Diffusion-weighted Imaging of Lymph Node Tissue

Purpose: The study investigates the hypothesis of clinically observed decreased apparent diffusion coefficient (ADC) of cancerous lymph nodes can be attributed to increased cellularity. The study characterises the mean diffusivity (MD) of lymph node sub-structures and investigates correlation between MD and cellularity metrics. The study also investigates the theoretical information content of single and multi-biophysical models. Methods:. A 3 mm diameter core sample was extracted from a formalin fixed lymph node tissue post-surgery and imaged using 9.4T and 16.4T Bruker MRI system. Samples were sectioned and stained with haematoxylin and eosin (H&E). Diffusion tensor model was fitted voxelwise and MD values were computed using Matlab. Cellularity metrics includes measurement of nuclear count and nuclear area. Eleven models with combinations of isotropic, anisotropic, and restricted components were tested for diffusion modelling and ranked using the Akaike information criterion (AIC). Results: The findings showed distinct diffusivities of lymph node sub-structures (capsule and parenchyma). Parenchyma in normal lymph node tissues had higher MD (0.71 ± 0.17 µm2/ms) than metastatic parenchyma (0.52 ± 0.08 µm2/ms) and lymphoma (0.47 ± 0.19 µm2/ms). No correlation were observed between MD and nuclear count (r = 0.368) and nuclear area (r = 0.368) respectively at 95 % confidence intervals. The single biophysical models (ADC and DTI) were ranked lowest by AIC. Multi-biophysical models consist of anisotropic and restricted diffusion (Zeppelin-sphere, Ball-stick-sphere, and Ball-sphere) were ranked highest in the majority of voxels of the tissue samples. Conclusion: A distinct diffusivity value were found in lymph node sub-structures with no correlation to cellularity. Multi-biophysical models were ranked highest and extract more information from the measurement data than simple single biophysical models

Automated recognition of cell phenotypes in histology images based on membrane- and nuclei-targeting biomarkers

<p>Abstract</p> <p>Background</p> <p>Three-dimensional <it>in vitro </it>culture of cancer cells are used to predict the effects of prospective anti-cancer drugs <it>in vivo</it>. In this study, we present an automated image analysis protocol for detailed morphological protein marker profiling of tumoroid cross section images.</p> <p>Methods</p> <p>Histologic cross sections of breast tumoroids developed in co-culture suspensions of breast cancer cell lines, stained for E-cadherin and progesterone receptor, were digitized and pixels in these images were classified into five categories using <it>k</it>-means clustering. Automated segmentation was used to identify image regions composed of cells expressing a given biomarker. Synthesized images were created to check the accuracy of the image processing system.</p> <p>Results</p> <p>Accuracy of automated segmentation was over 95% in identifying regions of interest in synthesized images. Image analysis of adjacent histology slides stained, respectively, for Ecad and PR, accurately predicted regions of different cell phenotypes. Image analysis of tumoroid cross sections from different tumoroids obtained under the same co-culture conditions indicated the variation of cellular composition from one tumoroid to another. Variations in the compositions of cross sections obtained from the same tumoroid were established by parallel analysis of Ecad and PR-stained cross section images.</p> <p>Conclusion</p> <p>Proposed image analysis methods offer standardized high throughput profiling of molecular anatomy of tumoroids based on both membrane and nuclei markers that is suitable to rapid large scale investigations of anti-cancer compounds for drug development.</p

Automated human age at death estimation system from long bones histology

Human age estimation at death from bone histology is a frequent and important requirement in forensic anthropology. Usually human age at death is estimated manually from bone histology or morphology. Manual methods of age estimation from bone histology involve three main phases that includes, analysis of variations in microscopic characteristics of bone with age, developing age regression equation based on the variation analysis and estimation of age using regression equation. However manual age at death estimation is not only tedious and time consuming process but also prone to observation variability and produce subjective results. Furthermore, there exists no digital database that can store the information of bone samples of Malaysian population. Hence it is vital to develop a histological automated system for age at death estimation to eliminate the problems of manual methods. This study presents the development of automated system for human age at death estimation from bone histology. Six histological and two morphological parameters were analyzed in 44 samples of long bones (humerus, radius, ulna, tibia, fibula and femur). First, the measurements and analyses were carried out using manual methods and then an automated system was developed to eliminate the problems of the manual process. The system assists in automatic measurements and calculations of bone histological parameters, analysis of parameters with age, developing regression equation and estimation of age. The automatic system also provides a digital database capable of storing the information of all parameters. The results of the system shows that histological parameters specifically percentage area covered by Haversian canals and mean Haversian canal area possess the highest correlation with age. Morphological parameters do not show significant correlation with age in Malaysian population. Age regression equation is developed with SEE of 8.3 years. The automatic system estimates age within 10 years of the actual ages for 89% of the samples. The automatic system is evaluated by seven forensic anthropologists and is considered effortless and acceptable for automatic age at death estimation from bone histology

A Polarization-Imaging-Based Machine Learning Framework for Quantitative Pathological Diagnosis of Cervical Precancerous Lesions

Polarization images encode high resolution microstructural information even at low resolution. We propose a framework combining polarization imaging and traditional microscopy imaging, constructing a dual-modality machine learning framework that is not only accurate but also generalizable and interpretable. We demonstrate the viability of our proposed framework using the cervical intraepithelial neoplasia grading task, providing a polarimetry feature parameter to quantitatively characterize microstructural variations with lesion progression in hematoxylin-eosin-stained pathological sections of cervical precancerous tissues. By taking advantages of polarization imaging techniques and machine learning methods, the model enables interpretable and quantitative diagnosis of cervical precancerous lesion cases with improved sensitivity and accuracy in a low-resolution and wide-field system. The proposed framework applies routine image-analysis technology to identify the macro-structure and segment the target region in H&#x0026;E-stained pathological images, and then employs emerging polarization method to extract the micro-structure information of the target region, which intends to expand the boundary of the current image-heavy digital pathology, bringing new possibilities for quantitative medical diagnosis

Evaluation Of Cementochronology As An Aging Method For Inexperienced Researchers

There are numerous different methods of determining the age of an adult human skeleton from the auricular surface or pubic symphysis of the pelvis, attrition of the human dental enamel, and degeneration of other syndesmosis. Age-at-death estimates from cementum annulation counts are one of the most accurate methods available. Cementochronology can provide an estimate for age-at-death despite pathological conditions that affect the bones and teeth; in some cases, where remains are incomplete, fragmented, or damaged post-mortem, a count of cementum annulations might be the only technique possible to obtain an age estimate. This method is of course not without its limitations. Despite its potential accuracy and precision, over the past 20 years, there has been much debate over whether this method should be used for human skeletons, particularly in a forensic context. Concerns are primarily focused on the lack of a standard protocol and validation studies thereof. This thesis will address the question of why counting cementum annulations is potentially so valuable as an age estimation tool and concerns as to whether a recently developed sectioning protocol will make this method more accessible

Automated platform for histological race and sex comparison of human cortical bone

Research on histological bone variation in population is in its early stages in Malaysia and limited information is available about age graded race and sex comparison. This research performed race and sex comparison of histological cortical bone parameters in the Malaysian population and presented an automated system which could be used as assistance tool by forensic experts. Human bone specimen were collected from Hospital Universiti Kebangsaan Malaysia Medical Centre (UKMMC), Kuala Lumpur, Malaysia. Haversian canals were measured and five parameters were calculated for comparison. Comparison test (t-test/u-test) showed that the size of Haversian canals were significantly greater (p<0.05) in females (HCM fifth, sixth decade: 5955.8 μm2, 5788.0 μm2) than males (HCM fifth, sixth decade: 4117.6 μm2, 3965.1 μm2). In race comparison, total area covered by Haversian canals (bone porosity) was significantly greater (p<0.05) in Indian samples (HCA: 0.457mm2) compared to Chinese samples (HCA: 0.385mm2) in the second decade. However in fifth decade, total area covered by Chinese samples (HCA: 0.894mm2) was significantly greater (p<0.05) than Indian samples (HCA: 0.570mm2). Three main steps of histological comparison were focused for automation i.e. parameter calculation, data management and statistical comparisons. The system was designed with GUI which utilizes aforementioned automation step. Validation of the system was divided into two main parts. In first part, parameter measurement and calculation performed by the system were compared with existing tools in terms of percentage error in measurement (DinoCapture: 5.3%, L-measure: 5.1%, ImageJ: 4.7%, designed system: 4.0%) and consumed time for measurement (DinoCapture: 15-20min, L-measure: 15-20min, ImageJ: 20-25min, designed system: 1-2min). Similarly automated race and sex comparison performed by the system were compared with comparisons performed manually using SPSS software. Significance and t/z values showed no differences and did not change overall hypothesis of the comparison tests. Which implies that the automated system is efficient for histological race and sex comparisons

Color and morphological features extraction and nuclei classification in tissue samples of colorectal cancer

Cancer is an important public health problem and the third most leading cause of death in North America. Among the highest impact types of cancer are colorectal, breast, lung, and prostate. This thesis addresses the features extraction by using different artificial intelligence algorithms that provide distinct solutions for the purpose of Computer-AidedDiagnosis (CAD). For example, classification algorithms are employed in identifying histological structures, such as lymphocytes, cancer-cells nuclei and glands, from features like existence, extension or shape. The morphological aspect of these structures indicates the degree of severity of the related disease. In this paper, we use a large dataset of 5000 images to classify eight different tissue types in the case of colorectal cancer. We compare results with another dataset. We perform image segmentation and extract statistical information about the area, perimeter, circularity, eccentricity and solidity of the interest points in the image. Finally, we use and compare four popular machine learning techniques, i.e., Naive Bayes, Random Forest, Support Vector Machine and Multilayer Perceptron to classify and to improve the precision of category assignation. The performance of each algorithm was measured using 3 types of metrics: Precision, recall and F1-Score representing a huge contribution to the existing literature complementing it in a quantitative way. The large number of images has helped us to circumvent the overfitting and reproducibility problems. The main contribution is the use of new characteristics different from those already studied, this work researches about the color and morphological characteristics in the images that may be useful for performing tissue classification in colorectal cancer histology