Cells In Histological Images Research Articles

An Imaging Biomarker of Tumor-Infiltrating Lymphocytes to Risk-Stratify Patients With HPV-Associated Oropharyngeal Cancer.

BackgroundHuman papillomavirus (HPV)–associated oropharyngeal squamous cell carcinoma (OPSCC) has excellent control rates compared to nonvirally associated OPSCC. Multiple trials are actively testing whether de-escalation of treatment intensity for these patients can maintain oncologic equipoise while reducing treatment-related toxicity. We have developed OP-TIL, a biomarker that characterizes the spatial interplay between tumor-infiltrating lymphocytes (TILs) and surrounding cells in histology images. Herein, we sought to test whether OP-TIL can segregate stage I HPV-associated OPSCC patients into low-risk and high-risk groups and aid in patient selection for de-escalation clinical trials.MethodsAssociation between OP-TIL and patient outcome was explored on whole slide hematoxylin and eosin images from 439 stage I HPV-associated OPSCC patients across 6 institutional cohorts. One institutional cohort (n = 94) was used to identify the most prognostic features and train a Cox regression model to predict risk of recurrence and death. Survival analysis was used to validate the algorithm as a biomarker of recurrence or death in the remaining 5 cohorts (n = 345). All statistical tests were 2-sided.ResultsOP-TIL separated stage I HPV-associated OPSCC patients with 30 or less pack-year smoking history into low-risk (2-year disease-free survival [DFS] = 94.2%; 5-year DFS = 88.4%) and high-risk (2-year DFS = 82.5%; 5-year DFS = 74.2%) groups (hazard ratio = 2.56, 95% confidence interval = 1.52 to 4.32; P < .001), even after adjusting for age, smoking status, T and N classification, and treatment modality on multivariate analysis for DFS (hazard ratio = 2.27, 95% confidence interval = 1.32 to 3.94; P = .003).ConclusionsOP-TIL can identify stage I HPV-associated OPSCC patients likely to be poor candidates for treatment de-escalation. Following validation on previously completed multi-institutional clinical trials, OP-TIL has the potential to be a biomarker, beyond clinical stage and HPV status, that can be used clinically to optimize patient selection for de-escalation.

CGAT: Cell Graph ATtention Network for Grading of Pancreatic Disease Histology Images.

Early detection of Pancreatic Ductal Adenocarcinoma (PDAC), one of the most aggressive malignancies of the pancreas, is crucial to avoid metastatic spread to other body regions. Detection of pancreatic cancer is typically carried out by assessing the distribution and arrangement of tumor and immune cells in histology images. This is further complicated due to morphological similarities with chronic pancreatitis (CP), and the co-occurrence of precursor lesions in the same tissue. Most of the current automated methods for grading pancreatic cancers rely on extensive feature engineering involving accurate identification of cell features or utilising single number spatially informed indices for grading purposes. Moreover, sophisticated methods involving black-box approaches, such as neural networks, do not offer insights into the model’s ability to accurately identify the correct disease grade. In this paper, we develop a novel cell-graph based Cell-Graph Attention (CGAT) network for the precise classification of pancreatic cancer and its precursors from multiplexed immunofluorescence histology images into the six different types of pancreatic diseases. The issue of class imbalance is addressed through bootstrapping multiple CGAT-nets, while the self-attention mechanism facilitates visualization of cell-cell features that are likely responsible for the predictive capabilities of the model. It is also shown that the model significantly outperforms the decision tree classifiers built using spatially informed metric, such as the Morisita-Horn (MH) indices.

SRPN: similarity-based region proposal networks for nuclei and cells detection in histology images.

The detection of nuclei and cells in histology images is of great value in both clinical practice and pathological studies. However, multiple reasons such as morphological variations of nuclei or cells make it a challenging task where conventional object detection methods cannot obtain satisfactory performance in many cases. A detection task consists of two sub-tasks, classification and localization. Under the condition of dense object detection, classification is a key to boost the detection performance. Considering this, we propose similarity based region proposal networks (SRPN) for nuclei and cells detection in histology images. In particular, a customised convolution layer termed as embedding layer is designed for network building. The embedding layer is added into the region proposal networks, enabling the networks to learn discriminative features based on similarity learning. Features obtained by similarity learning can significantly boost the classification performance compared to conventional methods. SRPN can be easily integrated into standard convolutional neural networks architectures such as the Faster R-CNN and RetinaNet. We test the proposed approach on tasks of multi-organ nuclei detection and signet ring cells detection in histological images. Experimental results show that networks applying similarity learning achieved superior performance on both tasks when compared to their counterparts. In particular, the proposed SRPN achieve state-of-the-art performance on the MoNuSeg benchmark for nuclei segmentation and detection while compared to previous methods, and on the signet ring cell detection benchmark when compared with baselines. The sourcecode is publicly available at: https://github.com/sigma10010/nuclei_cells_det.

STERapp: Semiautomatic Software for Stereological Analysis. Application in the Estimation of Fish Fecundity

Stereology is the tridimensional interpretation of bidimensional sections of a structure, widely used in fields such as mineralogy, medicine, and biology. This paper proposes a general software to do stereological analysis, called STERapp, with a friendly graphical interface to enable expert supervision. It includes a module to estimate fish fecundity (number of mature oocytes in the ovary), which has been used by experts in fish biology in two Spanish marine research centers since 2020 to estimate the fecundity of five fish species with different reproductive strategies and oocytes characteristics. This module encloses advanced computer vision and machine learning techniques to automatically recognize and classify the cells in histological images of fish gonads. The automatic recognition algorithm achieved a sensitivity of 55.6%, a specificity of 64.8%, and an average precision of 43.1%. The accuracies achieved for oocyte classification were 84.5% for the maturity stages and 78.5% for the classification regarding presence/absence of the nucleus. This facilitates the analysis and saves experts’ time. Hence, the SUS questionnaire reported a mean score of 81.9, which means that the system was perceived from good to excellent to develop stereological analysis for the estimation of fish fecundity.

Multiclass histology image retrieval, classification using Riesz transform and local binary pattern features

ABSTRACT Structure and distribution of cells in histological images cause considerable variability among the tissue patterns and make the retrieving and classification as challenging tasks for this type of medical images. In this paper, we propose to extract four local features, namely local energetic information, local structural information, local geometric information, and local patterns to represent the huge texture variability of the histological images in a feature space. For extracting these features, the Riesz transform and Monogenic local binary patterns are used. Riesz transform is an extension of the Hilbert transform for 2-D signals. It provides a monogenic representation for an image where we can capture some local structure information. We used these local features for classification and retrieving purposes. We validated our proposed method on two multiclass archives of histological images, namely Kimiapath24 and Kather datasets. We achieved performance of more than 90% for classification and retrieval in two datasets.

Efficient automated detection of mitotic cells from breast histological images using deep convolution neutral network with wavelet decomposed patches

In medical practice, the mitotic cell count from histological images acts as a proliferative marker for cancer diagnosis. Therefore, an accurate method for detecting mitotic cells in histological images is essential for cancer screening. Manual evaluation of clinically relevant image features that might reflect mitotic cells in histological images is time-consuming and error prone, due to the heterogeneous physical characteristics of mitotic cells. Computer-assisted automated detection of mitotic cells could overcome these limitations of manual analysis and act as a useful tool for pathologists to make cancer diagnoses efficiently and accurately. Here, we propose a new approach for mitotic cell detection in breast histological images that uses a deep convolution neural network (CNN) with wavelet decomposed image patches. In this approach, raw image patches of 81 × 81 pixels are decomposed to patches of 21 × 21 pixels using Haar wavelet and subsequently used in developing a deep CNN model for automated detection of mitotic cells. The decomposition step reduces convolution time for mitotic cell detection relative to the use of raw image patches in conventional CNN models. The proposed deep network was tested using the MITOS (ICPR2012) and MITOS-ATYPIA-14 breast cancer histological datasets and shown to outperform existing algorithms for mitotic cell detection. Overall, our method improves the performance and reduces the computational burden of conventional deep CNN approaches for mitotic cell detection.

Cell words: Modelling the visual appearance of cells in histopathology images

Detection and classification of cells in histological images is a challenging task because of the large intra-class variation in the visual appearance of various types of biological cells. In this paper, we propose a discriminative dictionary learning paradigm, termed as Cell Words, for modelling the visual appearance of cells which includes colour, shape, texture and context in a unified manner. The proposed framework is capable of distinguishing mitotic cells from non-mitotic cells (apoptotic, necrotic, epithelial) in breast histology images with high accuracy.

Local isotropic phase symmetry measure for detection of beta cells and lymphocytes

Diabetes can be associated with a reduction in functional β cell mass, which must be restored if the disease is to be cured or progress is to be arrested. To study the cell count, it is also necessary to determine the number of nuclei within the insulin stained area. It can take a single experimentalist several months to complete a single study of this kind, results of which may still be quite subjective. In this paper, we propose a framework based on a novel measure of local symmetry for detection of cells. The local isotropic phase symmetry measure (LIPSyM) is designed to give high values at or near the cell centers. We demonstrate the effectiveness of our algorithm for detection of two types of specific cells in histology images, cells in mouse pancreatic sections and lymphocytes in human breast tissue. Experimental results for these two problems show that our algorithm performs better than human experts for the former problem, and outperforms the best reported results for the latter.

Estimating Cell Count and Distribution in Labeled Histological Samples Using Incremental Cell Search

Cell proliferation is critical to the outgrowth of biological structures including the face and limbs. This cellular process has traditionally been studied via sequential histological sampling of these tissues. The length and tedium of traditional sampling is a major impediment to analyzing the large datasets required to accurately model cellular processes. Computerized cell localization and quantification is critical for high-throughput morphometric analysis of developing embryonic tissues. We have developed the Incremental Cell Search (ICS), a novel software tool that expedites the analysis of relationships between morphological outgrowth and cell proliferation in embryonic tissues. Based on an estimated average cell size and stain color, ICS rapidly indicates the approximate location and amount of cells in histological images of labeled embryonic tissue and provides estimates of cell counts in regions with saturated fluorescence and blurred cell boundaries. This capacity opens the door to high-throughput 3D and 4D quantitative analyses of developmental patterns.