Relevant bibliographies by topics / Histological image

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Academic literature on the topic 'Histological image'

Author: Grafiati
Published: 7 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Histological image"

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Muñoz-Aguirre, Manuel, Vasilis F. Ntasis, Santiago Rojas, and Roderic Guigó. "PyHIST: A Histological Image Segmentation Tool." PLOS Computational Biology 16, no. 10 (2020): e1008349. http://dx.doi.org/10.1371/journal.pcbi.1008349.

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The development of increasingly sophisticated methods to acquire high-resolution images has led to the generation of large collections of biomedical imaging data, including images of tissues and organs. Many of the current machine learning methods that aim to extract biological knowledge from histopathological images require several data preprocessing stages, creating an overhead before the proper analysis. Here we present PyHIST (https://github.com/manuel-munoz-aguirre/PyHIST), an easy-to-use, open source whole slide histological image tissue segmentation and preprocessing command-line tool aimed at tile generation for machine learning applications. From a given input image, the PyHIST pipeline i) optionally rescales the image to a different resolution, ii) produces a mask for the input image which separates the background from the tissue, and iii) generates individual image tiles with tissue content.
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Li, Xinrui, Yau Tsz Chan, and Yangzi Jiang. "Development of an image processing software for quantification of histological calcification staining images." PLOS ONE 18, no. 10 (2023): e0286626. http://dx.doi.org/10.1371/journal.pone.0286626.

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Quantification of the histological staining images gives important insights in biomedical research. In wet lab, it is common to have some stains off the target to become unwanted noisy stains during the generation of histological staining images. The current tools designed for quantification of histological staining images do not consider such situations; instead, the stained region is identified based on assumptions that the background is pure and clean. The goal of this study is to develop a light software named Staining Quantification (SQ) tool which could handle the image quantification job with features for removing a large amount of unwanted stains blended or overlaid with Region of Interest (ROI) in complex scenarios. The core algorithm was based on the method of higher order statistics transformation, and local density filtering. Compared with two state-of-art thresholding methods (i.e. Otsu’s method and Triclass thresholding method), the SQ tool outperformed in situations such as (1) images with weak positive signals and experimental caused dirty stains; (2) images with experimental counterstaining by multiple colors; (3) complicated histological structure of target tissues. The algorithm was developed in R4.0.2 with over a thousand in-house histological images containing Alizarin Red (AR) and Von Kossa (VK) staining, and was validated using external images. For the measurements of area and intensity in total and stained region, the average mean of difference in percentage between SQ and ImageJ were all less than 0.05. Using this as a criterion of successful image recognition, the success rate for all measurements in AR, VK and external validation batch were above 0.8. The test of Pearson’s coefficient, difference between SQ and ImageJ, and difference of proportions between SQ and ImageJ were all significant at level of 0.05. Our results indicated that the SQ tool is well established for automatic histological staining image quantification.
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Li, Dan, Hui Hui, Yingqian Zhang, et al. "Deep Learning for Virtual Histological Staining of Bright-Field Microscopic Images of Unlabeled Carotid Artery Tissue." Molecular Imaging and Biology 22, no. 5 (2020): 1301–9. http://dx.doi.org/10.1007/s11307-020-01508-6.

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Abstract Purpose Histological analysis of artery tissue samples is a widely used method for diagnosis and quantification of cardiovascular diseases. However, the variable and labor-intensive tissue staining procedures hinder efficient and informative histological image analysis. Procedures In this study, we developed a deep learning-based method to transfer bright-field microscopic images of unlabeled tissue sections into equivalent bright-field images of histologically stained versions of the same samples. We trained a convolutional neural network to build maps between the unstained images and histologically stained images using a conditional generative adversarial network model. Results The results of a blind evaluation by board-certified pathologists illustrate that the virtual staining and standard histological staining images of rat carotid artery tissue sections and those involving different types of stains showed no major differences. Quantification of virtual and histological H&E staining in carotid artery tissue sections showed that the relative errors of intima thickness, intima area, and media area were lower than 1.6 %, 5.6 %, and 12.7 %, respectively. The training time of deep learning network was 12.857 h with 1800 training patches and 200 epoches. Conclusions This virtual staining method significantly mitigates the typically laborious and time-consuming histological staining procedures and could be augmented with other label-free microscopic imaging modalities.
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Cisneros, Juan, Alain Lalande, Binnaz Yalcin, Fabrice Meriaudeau, and Stephan Collins. "Automatic Segmentation of Histological Images of Mouse Brains." Algorithms 16, no. 12 (2023): 553. http://dx.doi.org/10.3390/a16120553.

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Using a high-throughput neuroanatomical screen of histological brain sections developed in collaboration with the International Mouse Phenotyping Consortium, we previously reported a list of 198 genes whose inactivation leads to neuroanatomical phenotypes. To achieve this milestone, tens of thousands of hours of manual image segmentation were necessary. The present work involved developing a full pipeline to automate the application of deep learning methods for the automated segmentation of 24 anatomical regions used in the aforementioned screen. The dataset includes 2000 annotated parasagittal slides (24,000 × 14,000 pixels). Our approach consists of three main parts: the conversion of images (.ROI to .PNG), the training of the deep learning approach on the compressed images (512 × 256 and 2048 × 1024 pixels of the deep learning approach) to extract the regions of interest using either the U-Net or Attention U-Net architectures, and finally the transformation of the identified regions (.PNG to .ROI), enabling visualization and editing within the Fiji/ImageJ 1.54 software environment. With an image resolution of 2048 × 1024, the Attention U-Net provided the best results with an overall Dice Similarity Coefficient (DSC) of 0.90 ± 0.01 for all 24 regions. Using one command line, the end-user is now able to pre-analyze images automatically, then runs the existing analytical pipeline made of ImageJ macros to validate the automatically generated regions of interest resulting. Even for regions with low DSC, expert neuroanatomists rarely correct the results. We estimate a time savings of 6 to 10 times.
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Söhling, Nicolas, Olivia Von Jan, Maren Janko, et al. "Measuring Bone Healing: Parameters and Scores in Comparison." Bioengineering 10, no. 9 (2023): 1011. http://dx.doi.org/10.3390/bioengineering10091011.

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(1) Background: Bone healing is a complex process that can not be replicated in its entirety in vitro. Research on bone healing still requires the animal model. The critical size femur defect (CSFD) in rats is a well-established model for fractures in humans that exceed the self-healing potential. New therapeutic approaches can be tested here in vivo. Histological, biomechanical, and radiological parameters are usually collected and interpreted. However, it is not yet clear to what extent they correlate with each other and how necessary it is to record all parameters. (2) Methods: The basis for this study was data from three animal model studies evaluating bone healing. The µCT and histological (Movat pentachrome, osteocalcin) datasets/images were reevaluated and correlation analyses were then performed. Two image processing procedures were compared in the analysis of the image data. (3) Results: There was a significant correlation between the histologically determined bone fraction (Movat pentachrome staining) and bending stiffness. Bone fraction determined by osteocalcin showed no prognostic value. (4) Conclusions: The evaluation of the image datasets using ImageJ is sufficient and simpler than the combination of both programs. Determination of the bone fraction using Movat pentachrome staining allows conclusions to be drawn about the biomechanics of the bone. A standardized procedure with the ImageJ software is recommended for determining the bone proportion.
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Berchenko, Gennadiy N., Nina V. Fedosova, Mikhail G. Kochan, and Dmitriy V. Mashoshin. "Neural network model development for detecting atypical mitoses in histological slides." N.N. Priorov Journal of Traumatology and Orthopedics 31, no. 3 (2024): 337–50. http://dx.doi.org/10.17816/vto626361.

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Background: Modern computer systems allow digitizing and examining images of histological preparations, which led the authors to the idea of using machine learning tools in digital pathohistology. The ability of neural networks to find sub-visual image features in digitized histological preparations provides the basis for better qualitative and quantitative image analysis. Existing machine learning methods provide good accuracy and speed in recognizing various images, which gives hope for their wide application, including in oncologic diagnostics. AIM: Use methods of mathematical modeling to identify pathological mitoses in histological preparations as the main sign of the difference between malignant and benign tumor growth. MATERIALS AND METHODS: Histological images of the N.N. Priorov National Medical Research Center of Traumatology and Orthopedics were used as a data set for the neural network model. The model was tested using 188 histologic slides from 67 patients treated at the institute. Histological preparations were scanned on a Leica Aperio CS2 microscope with a ×400 resolution and converted into JPEG format with further processing. Next, the test images were analyzed in streaming mode using the created neural network model in order to obtain the coordinates of the desired diagnostic object — pathological mitosis and the probability with which the model found the object of this category. The obtained images were analyzed by a pathologist to determine whether the detected object corresponded to pathological mitosis. RESULTS: The authors have chosen an architecture, developed a methodology for training a neural network, and created a model that can be used to detect pathologic mitoses in histologic preparations. The authors do not attempt to replace the physician, but show the possibility of an integrated approach to data analysis by a computer system and a pathologist. Conclusions: The developed mathematical model of neural network used as a part of technological solution for recognizing pathological mitoses in scanned histological preparations can be used as a tool to reduce the time of research and increase the accuracy of diagnosis by a pathologist.
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Estrela, Vania V. "Content-Based Image Retrieval (CBIR) in Big Histological Image Databases." Medical Technologies Journal 4, no. 3 (2020): 581–82. http://dx.doi.org/10.26415/2572-004x-vol4iss3p581-582.

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Background: Automatic analysis of Histopathological Images (HIs) demands image processing and Computational Intelligence (CI) techniques. Both Computer-Aided Diagnosis (CAD) and Content-Based Image-Retrieval (CBIR) systems assist diagnosis, disease discovery, and biological decision-making. Classical tests comprise screening examinations and biopsy. Histopathology slides offer more ample diagnosis data. However, manual examination of microscopic images is labor-intensive and time-consuming and may depend on a subjective assessment by the pathologist, which can be a challenge.
 Methods: This work discusses a CBIR framework to extract and handle histological data, histological metadata, integrated patient records, specimen metadata, attributes, and similar stored files. This work presents a scalable image-retrieval framework for intelligent HI analysis with real-time retrieval. The potential applications of this framework include image-guided diagnosis, decision support, healthcare education, and efficient biological data management.
 Results: The considerable amount of biological-related data prompted the development and deployment of large-scale databases and data-driven techniques to bridge the semantic gap between images and diagnostic information. The new cloud computing technologies and the concept of cyber-physical systems have improved the CBIR architectures considerably. The proposed scalable architecture relies on CI and validates performance on several HIs acquired from microscopic tissues. Extensive assessments show improvements in terms of disease classification and retrieval tests.
 Conclusion: This research effort significant contributions are twofold. 1) Defining a comprehensive and large-scale CBIR framework to analyze HIs with high-dimensional features and CI methods successfully. 2) high-performance updating and optimization strategies improve the querying while better handling new training samples than traditional methods.
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Robota, D. V., and B. S. Burlaka. "SELECTING AN EFFECTIVE METHOD OF COLOR NORMALIZATION FOR HISTOLOGICAL IMAGES OF INTESTINAL TISSUES IN DEEP LEARNING MODEL DEVELOPMENT." Актуальні проблеми сучасної медицини: Вісник Української медичної стоматологічної академії 25, no. 1 (2025): 203–10. https://doi.org/10.31718/2077-1096.25.1.203.

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The advancement of modern computer technologies opens new opportunities for the automated analysis of whole-slide histological images. This is made possible by digital pathology approaches and artificial intelligence methods, particularly machine learning and deep learning. One of the key challenges in this process is the significant variability in the color of histological images. This variability arises from different staining techniques, the characteristics of laboratory equipment used for scanning specimens, and the individual properties of patient tissues. These factors can significantly affect the accuracy of automated decision-support systems, histological image classification, and tissue structure segmentation. The absence of a unified color normalization standard complicates the application of deep neural networks in histological image analysis tasks. This study examines the selection of a color normalization method for intestinal tissue images in the development of deep learning models aimed at multiclass segmentation of structural and functional tissue components. The conducted research characterizes approaches to standardizing the features of whole-slide histological images, which can be used to improve the accuracy of histopathological analysis of intestinal tissues. Various normalization methods were evaluated, and the most suitable approach for this type of task was identified. The experimental results demonstrate the high efficiency of using the Reinhard Modified method, which ensures high segmentation quality metrics: Mean IoU: 0.7086, Mean Dice: 0.8279, Precision: 0.8321, Recall: 0.8241, Accuracy: 0.8241, F1-Score: 0.8279, Specificity: 0.9112. The obtained results confirm the potential of color normalization in digital pathology and automated histological image analysis. The application of standardized normalization methods can significantly improve the accuracy of artificial intelligence systems, which is crucial for medical diagnostics and research activities. The advancement of modern computational technologies has opened new opportunities for the automated analysis of whole-slide histological images. This progress is driven by digital pathology approaches and artificial intelligence (AI) methods, particularly machine learning and deep learning. One of the key challenges in this field is the significant variability in the color of histological images. This variability arises due to differences in staining techniques, variations in laboratory equipment used for specimen scanning, and the unique properties of patient tissues. Such factors can substantially impact the accuracy of automated decision-support systems, histological image classification, and tissue structure segmentation. The absence of a unified color normalization standard further complicates the application of deep neural networks in histological image analysis. This study examines the selection of an optimal color normalization method for intestinal tissue images in the development of deep learning models designed for multiclass segmentation of structural and functional tissue components. The article characterizes various approaches to standardizing whole-slide histological image features, which can enhance the accuracy of histopathological analysis. Multiple normalization methods were evaluated, and the most suitable approach for this task was identified. Experimental results demonstrate the high efficiency of the Reinhard Modified method, which achieved superior segmentation quality metrics: Mean IoU: 0.7086, Mean Dice: 0.8279, Precision: 0.8321, Recall: 0.8241, Accuracy: 0.8241, F1-Score: 0.8279, Specificity: 0.9112. The findings confirm the potential of color normalization in digital pathology and automated histological image analysis. Implementing standardized normalization methods can significantly improve the accuracy of AI-driven diagnostic systems, which is crucial for medical diagnostics and research applications.
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Berezsky, O. M., P. B. Liashchynskyi, O. Y. Pitsun, and G. M. Melnyk. "DEEP NETWORK-BASED METHOD AND SOFTWARE FOR SMALL SAMPLE BIOMEDICAL IMAGE GENERATION AND CLASSIFICATION." Radio Electronics, Computer Science, Control, no. 4 (January 2, 2024): 76. http://dx.doi.org/10.15588/1607-3274-2023-4-8.

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Context. The authors of the article investigated the problem of generating and classifying breast cancer histological images. The widespread incidence of breast cancer explains the problem’s relevance. The automated diagnosing procedure saves time and eliminates the subjective aspect. The study’s findings can be applied to cancer CAD systems.
 Objective. The purpose of the study is to develop a deep neural network-based method and software tool for generating and classifying histological images in order to increase classification accuracy.
 Method. The method of histological image generation and classification was developed in the research study. This method employs CNN and GAN. To improve the classification accuracy, the initial image sample was expanded using GAN.
 Results. The computer research of the developed method of image generation and classification was conducted on the basis of the dataset located on the Zenodo platform. Light microscopy served as the basis for obtaining the image. The dataset contained three classes of G1, G2, and G3 breast cancer histological images. Based on the developed method, the accuracy of image classification was 96%. This is a higher classification accuracy compared to existing models such as AlexNet, LeNet5, and VGG16. The software module can be integrated into CAD.
 Conclusions. The developed method of generating and classifying images is the basis of the software module. The software module can be integrated into CAD.
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Peyrin-Biroulet, L., S. Adsul, J. Dehmeshki, and O. Kubassova. "DOP58 An artificial intelligence–driven scoring system to measure histological disease activity in Ulcerative Colitis." Journal of Crohn's and Colitis 16, Supplement_1 (2022): i105. http://dx.doi.org/10.1093/ecco-jcc/jjab232.097.

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Abstract Background Histological remission is increasingly regarded as an important and deep therapeutic target for ulcerative colitis (UC). Assessment and scoring of histological images is a tedious procedure, that can be imprecise and prone to inter- and intra-observer variability. Therefore, a need exists for an automated method that is accurate, reproducible and reliable. This study aimed to investigate whether an artificial intelligence (AI) system developed using image processing and machine learning algorithms could measure histological disease activity based on the Nancy index. Methods A total of 200 histological images of patients with UC from a database at University Hospital, Vandoeuvre-lès-Nancy, France were used for this study. The novel AI system was used to fully characterise histological images and automatically measure Nancy index. The in-house AI algorithm was developed using state-of-the-art image processing and machine learning algorithms based on deep learning and feature extraction. The cell regions of each image, followed by Nancy index, were manually annotated and measured independently by 3 histopathologists. Manual and AI-automated measurements of Nancy index score were done and assessed using the intraclass correlation coefficient (ICC). Results The 200-image dataset was divided into 2 groups (80% was used for training and 20% for testing). ICC statistical analyses were performed to evaluate AI tool and used as a reference to calculate the accuracy (Table 1). The average ICC amongst the histopathologists was 89.33 and average ICC between histopathologists and AI tool was 87.20. Despite the small number of image data, the AI tool was found to be highly correlated with histopathologists. Conclusion The high correlation of performance of the AI method suggested promising potential for IBD clinical applications. A standardised and validated histological AI-driven scoring system can potentially be used in daily IBD practice to eliminate the subjectivity of the pathologists and assess the disease severity for treatment decision.
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Dissertations / Theses on the topic "Histological image"

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Cooper, Lee Alex Donald. "High Performance Image Analysis for Large Histological Datasets." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1250004647.

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Masood, Khalid. "Histological image analysis and gland modelling for biopsy classification." Thesis, University of Warwick, 2010. http://wrap.warwick.ac.uk/3918/.

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The area of computer-aided diagnosis (CAD) has undergone tremendous growth in recent years. In CAD, the computer output is used as a second opinion for cancer diagnosis. Development of cancer is a multiphase process and mutation of genes is involved over the years. Cancer grows out of normal cells in the body and it usually occurs when growth of the cells in the body is out of control. This phenomenon changes the shape and structure of the tissue glands. In this thesis, we have developed three algorithms for classification of colon and prostate biopsy samples. First, we computed morphological and shape based parameters from hyperspectral images of colon samples and used linear and non-linear classifiers for the identification of cancerous regions. To investigate the importance of hyperspectral imagery in histopathology, we selected a single spectral band from its hyperspectral cube and performed an analysis based on texture of the images. Texture refers to an arrangement of the basic constituents of the material and it is represented by the interrelationships between the spatial arrangements of the image pixels. A novel feature selection method based on the quality of clustering is developed to discard redundant information. In the third algorithm, we used Bayesian inference for segmentation of glands in colon and prostate biopsy samples. In this approach, glands in a tissue are represented by polygonal models with variuos number of vertices depending on the size of glands. An appropriate set of proposals for Metropolis- Hastings-Green algorithm is formulated and a series of Markov chain Monte Carlo (MCMC) simulations are run to extract polygonal models for the glands. We demonstrate the performance of 3D spectral and spatial and 2D spatial analyses with over 90% classification accuracies and less than 10% average segmentation error for the polygonal models.
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Nazaran, Amin. "Ultra Short MR Relaxometry and Histological Image Processing for Validation of Diffusion MRI." BYU ScholarsArchive, 2016. https://scholarsarchive.byu.edu/etd/6348.

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Magnetic Resonance Imaging (MRI) is an imaging modality that acquires an image with little to no damage to the tissue. MRI does not introduce foreign particles or high energy radiation into the body, making it one of the least invasive medical imaging modalities. MRI can achieve excellent soft tissue contrast and is therefore useful for diagnosis of a wide variety of diseases. While there are a wide variety of available techniques for generating contrast in MRI, there are still many open areas for research. For example, many tissues in the human body exhibit such rapid signal decay that they are difficult to image with MRI: they are "MRI invisible". Furthermore, some of the newer MRI imaging techniques have not been fully validated to ensure that they are truly revealing accurate information about the underlying anatomical microstructure that they purport to image. This dissertation focuses on the development of new techniques in two distinct areas. First, a novel method for accurately assessing the MRI signal decay properties of tissues that are normally MRI invisible, such as tendons, ligaments, and certain pathological chemical deposits in the brain, is presented. This is termed "ultrashort MRI relaxometry". Second, two new image processing algorithms that operate on high resolution images of stained histological slices of the ex vivo brain are presented. The first of these image processing algorithms allows the semi-automated extraction of nerve fiber directionality from the histological slice images, a process that is normally done manually, is incredibly time consuming, and is prone to human error. This new technique represents one significant step in the complicated problem of attempting to validate a popular MRI technique, Diffusion Tensor Imaging (DTI), by ensuring that DTI results correlate with the true underlying physiology revealed by histological slicing and staining. The second of these image processing algorithms attempts to extract and segment regions of different "cytoarchitectonic characteristics" from stained histological slices of ex vivo brain. Again, traditional cytoarchitectonic segmentation relies on manual segmentation by an expert neuroanatomist, which is slow and sometimes inconsistent. The new technique is a first step towards automated this process, potentially providing greater accuracy and repeatability of the segmentations in a much shorter time. Together, these contributions represent a significant contribution to the body of MR imaging techniques, and associated image processing techniques for validation of newer MR neuroimaging techniques against the gold standard of stained histological slices of ex vivo brain.
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Marghani, Khaled A. S. "Automated quantitative image analysis for the histological assessments of colorectal and pancreas cancer tissues." Thesis, University of Newcastle Upon Tyne, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.420015.

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Azar, Jimmy. "Automated Tissue Image Analysis Using Pattern Recognition." Doctoral thesis, Uppsala universitet, Bildanalys och människa-datorinteraktion, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-231039.

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Automated tissue image analysis aims to develop algorithms for a variety of histological applications. This has important implications in the diagnostic grading of cancer such as in breast and prostate tissue, as well as in the quantification of prognostic and predictive biomarkers that may help assess the risk of recurrence and the responsiveness of tumors to endocrine therapy. In this thesis, we use pattern recognition and image analysis techniques to solve several problems relating to histopathology and immunohistochemistry applications. In particular, we present a new method for the detection and localization of tissue microarray cores in an automated manner and compare it against conventional approaches. We also present an unsupervised method for color decomposition based on modeling the image formation process while taking into account acquisition noise. The method is unsupervised and is able to overcome the limitation of specifying absorption spectra for the stains that require separation. This is done by estimating reference colors through fitting a Gaussian mixture model trained using expectation-maximization. Another important factor in histopathology is the choice of stain, though it often goes unnoticed. Stain color combinations determine the extent of overlap between chromaticity clusters in color space, and this intrinsic overlap sets a main limitation on the performance of classification methods, regardless of their nature or complexity. In this thesis, we present a framework for optimizing the selection of histological stains in a manner that is aligned with the final objective of automation, rather than visual analysis. Immunohistochemistry can facilitate the quantification of biomarkers such as estrogen, progesterone, and the human epidermal growth factor 2 receptors, in addition to Ki-67 proteins that are associated with cell growth and proliferation. As an application, we propose a method for the identification of paired antibodies based on correlating probability maps of immunostaining patterns across adjacent tissue sections. Finally, we present a new feature descriptor for characterizing glandular structure and tissue architecture, which form an important component of Gleason and tubule-based Elston grading. The method is based on defining shape-preserving, neighborhood annuli around lumen regions and gathering quantitative and spatial data concerning the various tissue-types.
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Hamilton, Alastair M. A. "MRI and histological analysis of brain metastasis and the effect of systemic inflammation." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:7bd5c6b9-592a-4afa-8053-907db849a0d9.

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Background: Brain metastasis is a leading cause of cancer mortality and affects 20-40% of all cancer patients. The BBB is responsible for isolation and protection of the brain parenchyma from many diagnostic and therapeutic agents. New molecular agents that target tumour-associated VCAM-1 expression on the brain endothelium show improvements in the early diagnosis of brain metastasis. The vascular endothelium of the CNS plays an important role in the maintenance of the brain microenvironment and possibly aids the extravasation of tumour cells via expression of CAMs. Aims: Using the breast carcinoma-derived 4T1 cell line, syngeneic to BALB/c mice, this work aimed (i) to determine the level of colocalisation between VCAM-1 expression at sites of brain metastasis and the presence of VCAM-MPIO-induced hypointensities in MR datasets; (ii) to describe the normal developmental characteristics of the intracardial BALB/c-4T1 brain metastatic model in the absence of overt inflammation; (iii) to test the effects of an adenovirus-induced systemic inflammatory challenge on metastatic uptake and development in the brain. Results: The level of correspondence of VCAM-MPIO-derived hypointensities with VCAM-1 expression at the tumour site was found to be dependent on the size of metastasis. An improved method for detection of VCAM-MPIO hypointensities using an automated method has been presented. Tumours were found to develop preferentially on venous rather than arteriolar blood vessels, and showed greater and lesser abundance in different anatomical brain regions. Adenovirus injection was found to cause an upregulation of a range of peripheral pro-inflammatory cytokines, and expression of VCAM-1 on cerebral vasculature, preferentially on arteriolar blood vessels. Both pre- and post-treatment with adenovirus caused a two-fold reduction in tumour numbers and altered developmental characteristics of established tumours, although no significant differences were observed in VCAM-MPIO hypointensities in MR datasets. Conclusions: The development of molecular MRI approaches to target VCAM-1 expression at the site of brain metastases has improved the sensitivity of tumour detection. 4T1-GFP metastasis to the brain is specific both to anatomical sites and to regions of the vascular bed, suggesting differences in vascular morphology and/or signalling dynamics in these locations. The changes in tumour number and morphology as a result of systemic inflammation suggest an anti-tumour effect of adenoviral treatment and, given the role of the systemic immune system and its importance in the development of immunotherapies, possible future directions for research.
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D'Agostino, Alessandro. "Automatic generation of synthetic datasets for digital pathology image analysis." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21722/.

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The project is inspired by an actual problem of timing and accessibility in the analysis of histological samples in the health-care system. In this project, I address the problem of synthetic histological image generation for the purpose of training Neural Networks for the segmentation of real histological images. The collection of real histological human-labeled samples is a very time consuming and expensive process and often is not representative of healthy samples, for the intrinsic nature of the medical analysis. The method I propose is based on the replication of the traditional specimen preparation technique in a virtual environment. The first step is the creation of a 3D virtual model of a region of the target human tissue. The model should represent all the key features of the tissue, and the richer it is the better will be the yielded result. The second step is to perform a sampling of the model through a virtual tomography process, which produces a first completely labeled image of the section. This image is then processed with different tools to achieve a histological-like aspect. The most significant aesthetical post-processing is given by the action of a style transfer neural network that transfers the typical histological visual texture on the synthetic image. This procedure is presented in detail for two specific models: one of pancreatic tissue and one of dermal tissue. The two resulting images compose a pair of images suitable for a supervised learning technique. The generation process is completely automatized and does not require the intervention of any human operator, hence it can be used to produce arbitrary large datasets. The synthetic images are inevitably less complex than the real samples and they offer an easier segmentation task to solve for the NN. However, the synthetic images are very abundant, and the training of a NN can take advantage of this feature, following the so-called curriculum learning strategy.
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Bug, Daniel [Verfasser], Dorit [Akademischer Betreuer] Merhof, and Horst K. [Akademischer Betreuer] Hahn. "Digital histopathology : Image processing for histological analyses and immune response quantification / Daniel Bug ; Dorit Merhof, Horst K. Hahn." Aachen : Universitätsbibliothek der RWTH Aachen, 2020. http://d-nb.info/1240689543/34.

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Costa, Eudriano Florêncio dos Santos. "Reproductive strategies of the marine fishes from the southwest Atlantic Ocean: an application of histological and image processing techniques." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/21/21134/tde-18062015-095137/.

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This thesis has hypothesised that species inhabiting different environments such as coastal and estuarine areas exhibit the same reproductive strategies in terms of oocyte development, oocyte recruitment and fecundity. Thus, the ovaries of Anchoa filifera, Cetengraulis edentulus, Citharichthys spilopterus, Stellifer brasiliensis, S. rastrifer, Menticirrhus americanus, Paralonchurus brasiliensis and Diplectrum radiale were examined using histology and image processing techniques. The fishes were captured bimonthly, from June (2012) to May (2013), in the coastal of Ubatuba and in the estuary of Cananéia, inner shelf of São Paulo State, Brazil. The ovaries were removed, weighed, fixed in formalin and prepared the histological sections. All sections were photographed and the images analysed using the free software ImageJ. The results revealed that all species exhibited asynchronous oocyte development, although some differences in the oocyte development pattern among species have been recorded. The stock of pre-vitellogenic oocytes can develop and be recruited into yolked stock at any time in all species. The simultaneous hermaphrodite D. radiale has an unrestricted testicular type with cystic spermatogenesis. In this species, the accessory reproductive structure has the function to store the hydrated oocytes up to the next spawning event and absorb non-spawned oocytes. The size at which the oocytes are recruited to vitellogenesis (ORS) showed difference among species. The total number of oocytes produced per development stage did not differ significantly between the congeneric species S. brasiliensis and S. rastrifer. However, the number of advanced vitellogenic oocytes varied among gonochoristic species and increased with increasing the ovary weight, total length and weight. The mean batch fecundity ranged from 1,644 in A. filifera to 58,884 oocytes in M. americanus, whereas the mean relative BF ranged 51 in D. radiale to 1,205 oocytes g-1 in C. spilopterus. The number of potential batches in the ovaries also differed among species, ranging from 1 in A. filifera, C. edentulus and M. americanus to 4 in C. spilopterus. Thus, the hypothesis of this thesis was rejected.<br>A presente tese testou a hipótese de que espécies que habitam diferentes ambientes, costeiros e estuarinos, apresentam as mesmas estratégias reprodutivas em relação ao desenvolvimento ovariano, recrutamento ovocitário e fecundidade. Desse modo, ovários de Anchoa filifera, Cetengraulis edentulus, Citharichthys spilopterus, Stellifer brasiliensis, S. rastrifer, Menticirrhus americanus, Paralonchurus brasiliensis e Diplectrum radiale foram amostrados e analisados através de técnicas histológicas e de processamento de imagens. As capturas foram realizadas no período de junho (2012) a maio (2013) na região costeira de Ubatuba e no estuário de Cananéia, São Paulo, Brasil. Os ovários foram removidos, pesados, fixados em solução de formalina e obtidos os preparados permanentes. Todas as secções histológicas foram fotografadas e as imagens analisadas no programa ImageJ. Os resultados revelaram que todas as espécies apresentam desenvolvimento ovócitário do tipo assincrônico. O recrutamento ovocitário ocorre constantemente durante o período de desova das espécies. Os testículos do hermafrodita simultâneo D. radiale é do tipo irrestrito com espermatogênese cística. Nessa espécie, a estrutura acessória reprodutiva tem a função de armazenar os ovócitos hidratados até o próximo evento de desova e absorver ovócitos que não foram desovados (atresia). Os ovócitos das espécies iniciam a vitelogênese em diferentes tamanhos. O número total de ovócitos produzidos por fase de desenvolvimento não diferiram significativamente entre as espécies congêneres S. brasiliensis e S. rastrifer. No entanto, o número de ovócitos em vitelogênese avançada diferiu entre as espécies gonocóricas. A fecundidade média por lote variou de 1.644 em A. filifera a 58.884 ovócitos em M. americanus, enquanto que a fecundidade relativa variou de 51 a 1.205 ovócitos g-1 em D. radiale e C. spilopterus, respectivamente. O número de lotes potenciais presentes nos ovários também diferiu entre as espécies, variando de 1 em A. filifera, C. edentulus e M. americanus a 4 em C. spilopterus. Assim, a hipótese postulada inicialmente foi rejeitada.
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Medeiros, Savi Flavia. "Bone responses to tissue engineered constructs (TECs) in critical large bone defects: Towards improved histological and immunohistochemical assessment." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/203906/1/Flavia_Medeiros%20Savi_Thesis.pdf.

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This research was focused on the development of advanced quantitative methods to evaluate the use of a 3D printed membrane scaffold, for the guidance and spatiotemporal delivery of recombinant human bone growth factor for the regeneration of a large bone defect, which still represents a major challenge in orthopaedic and reconstructive surgery. Two histomorphometric approaches were investigated and successfully applied. The combined histological, immunohistochemical and histomorphometric analyses outlined in this thesis further elucidated the mechanisms and patterns that govern the bone regeneration of a critical sized bone defect in a large experimental ovine model.
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Books on the topic "Histological image"

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Y, Mary J., Rigaut J. P, Unité de recherches biomathématiques et biostatistiques., Institut national de la santé et de la recherche médicale., Association pour la recherche sur le cancer., and European Society of Pathology, eds. Quantitative image analysis in cancer cytology and histology. Elsevier Science, 1986.

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Y, Mary J., Rigaut J. P, Institut national de la santé et de la recherche médicale (France). Unité de recherches biomathématiques et biostatistiques., Association pour le développment de la recherche sur le cancer (France), and European Society of Pathology, eds. Quantitative image analysis in cancer cytology and histology: Based on a symposium. Elsevier, 1986.

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R, Wootton, Springall D. R, and Polak Julia M, eds. Image analysis in histology: Conventional and confocal microscopy. Published in association with the Royal Postgraduage Medical School, University of London by Cambridge University Press, 1995.

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Chevanne, Marta, and Riccardo Caldini. Immagini di Istopatologia. Firenze University Press, 2007. http://dx.doi.org/10.36253/978-88-5518-023-8.

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This collection of images of Histopathology is the fruit of the authors' thirty years' experience in the performance of practical exercises in General Pathology. It is aimed at students attending lessons of General Pathology on the Degree Courses in Medical Surgery and Biological Sciences. It does not aspire either to be complete from the point of view of the various organic pathologies, or to replace direct and personal observation of the histological preparations through the microscope, but is rather intended as an aid to students preparing for the exam. It does not include the rudiments of cytology and microscopic anatomy, which it is assumed have already been mastered by those approaching General Histopathology, nor are histopathological phenomena systematically addressed, for which the reader is referred to textbooks on General Pathology. The 44 preparations presented here have been grouped in line with the main arguments of General Pathology: Cellular Degeneration, Inflammation, Neoplasia both benign and malign, and Vascular Pathology. They have been selected for their didactic significance and the simplicity and clarity of the lesions present, without taking into account the information to be derived from the clinical case history. The images of the preparations, in which the best possible quality of reproduction has been sought, are presented in progressive enlargements and are accompanied by brief descriptions comprising the explanations essential for identification of the characteristic aspects of the elementary lesion, as well as any eventual defects in the preparations themselves. Effectively, the objective of the work is to enable the student to exercise his understanding of the images. For this reason the casuistics included is as essential as possible, and the method of presentation utilised is designed to avoid mere visual memorisation, stimulating first analysis and then synthesis, and the development of individual logical skills so as to indicate whether aspects of cellular pathology, inflammation or neoplasia are present.
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Tral’, Tat’yana, Gulrukhsor Tolibova, Igor Kogan, and Anna Olina. Embryo losses. Atlas. Publishing Center RIOR, 2023. http://dx.doi.org/10.29039/978-5-907218-78-9.

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Histologic examination of abortive material is the basic approach to identify the etiology of miscarriage. Morphological diagnostics in case of embryo loss makes it possible to draw up the plan to fully prepare the woman for future pregnancy, whether spontaneous or after fertility treatment, increasing the chance of a favorable outcome. This educational book contains the data from various studies of the endometrium and abortive material undertaken at the Ott Research Institute of Obstetrics, Gynecology and Reproductology. Histology illustrations are supplemented with images of immunohistochemical studies and confocal laser scanning microscopy photos, as well as detailed text descriptions. Images can be viewed in the atlas, with QR codes linking to high-resolution electronic photos. This edition highlights the features of endometrial structural changes related to different modes of conception, the details of assessing abortive material, trophoblast chromosomal abnormalities, anembryony, hydatidiform mole, choriocarcinoma, as well as examination of embryo losses of various origins. The atlas is intended for pathologists, obstetrician-gynecologists and heads of women’s health clinics, perinatal centers, gynecological departments of general hospitals, fertility specialists, clinical laboratory diagnostics specialists, fellows and heads of departments of obstetrics and gynecology, pathological anatomy, students of all forms of continuous medical education, graduate students and clinical residents.
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Butler, Reni S. Architectural Distortion (Radial Scar). Edited by Christoph I. Lee, Constance D. Lehman, and Lawrence W. Bassett. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190270261.003.0030.

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Radial scars are benign lesions of the breast characterized pathologically by a fibroelastic core containing entrapped ducts and lobules that radiate outwards in a stellate pattern. This chapter, highlighting radial scar as a cause of architectural distortion, reviews its imaging features and differential diagnosis on mammography, digital breast tomosynthesis, ultrasound, and MRI; its diagnostic workup using multiple modalities; and its histological confirmation with image-guided core needle biopsy. The particular challenge of radial scar presenting as architectural distortion seen only with tomosynthesis is discussed, along with an algorithm for imaging evaluation and biopsy guidance in this setting. As radial scar, which is histologically related to complex sclerosing lesion and radial sclerosing lesion, is considered a high-risk lesion, management recommendations are also reviewed.
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Di Carlo, Philip A. Ultrasound-Guided Core Biopsy. Edited by Christoph I. Lee, Constance D. Lehman, and Lawrence W. Bassett. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190270261.003.0056.

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Prior to 1993, when ultrasound-guided core breast biopsy was first described by Parker and colleagues, surgery following image-guided needle localization was necessary to obtain a histological diagnosis of breast lesions. But there are many financial, practical, and clinical advantages of image-guided core biopsy over surgical excisional biopsy. There are also many advantages to ultrasound-guided biopsy over stereotactic- or MRI-guided biopsy, detailed in this chapter. Ultrasound is now usually the modality of choice by which to perform core biopsies if the lesion is visualized by multiple imaging modalities. This chapter, appearing in the section on interventions and surgical changes, reviews the key points of performing ultrasound-guided core biopsy. Topics discussed include protocols for both spring-loaded and vacuum-assisted devices; pre-procedure and post-procedure management, and imaging follow-up.
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DeFelipe, Javier. Cajal's Neuronal Forest. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190842833.001.0001.

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Cajal’s Neuronal Forest: Science and Art continues the tradition set forth in the 2009 publication Cajal’s Butterflies of the Soul: Science and Art. This new compilation contains a vastly large collection of beautiful figures produced throughout the nineteenth century and the beginning of the twentieth century. These images continue to represent and illustrate characteristic examples of the early days of research in neuroscience. Most scientific figures presented by the neuroanatomists of the time were their own drawings; microphotography was not yet a well-developed technique. Therefore, a successful neuroanatomist required a combination of artistic talent and an ability to interpret microscopic images effectively. The problem was that these illustrations were not necessarily free of technical errors and they may have been subject to the scientists’ own interpretations. Indeed, in some cases, these drawings were considered to be basically artistic interpretations rather than accurate copies of the histological preparations. Furthermore, there are many examples showing that even using the same microscopes and the same techniques, scientists “see” differently through the microscope. As a result, this period of scientific “art” and skepticism represents a fascinating page in the history of neuroscience as it provided the basis of our current understanding of the anatomy of the nervous system.
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Book chapters on the topic "Histological image"

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Bug, Daniel, Gregor Nickel, Anne Grote, et al. "Image Quilting for Histological Image Synthesis." In Informatik aktuell. Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-29267-6_72.

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Wodzinski, Marek, and Henning Müller. "Learning-Based Affine Registration of Histological Images." In Biomedical Image Registration. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50120-4_2.

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Cerci, Juliano J., Mateos Bogoni, and Dominique Delbeke. "History of Image-Guided Biopsy." In Oncological PET/CT with Histological Confirmation. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27880-3_1.

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Di Ruberto, Cecilia, Andrea Loddo, and Lorenzo Putzu. "Histological Image Analysis by Invariant Descriptors." In Image Analysis and Processing - ICIAP 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68560-1_31.

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Ushikawa, Takuya, Masanobu Takahashi, and Masayuki Nakano. "Multimodal Image Analysis of Unstained Histological Sections." In IFMBE Proceedings. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11128-5_54.

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Pyatov, Vladislav A., and Dmitry V. Sorokin. "TAHIR: Transformer-Based Affine Histological Image Registration." In Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37742-6_42.

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Livio, Corain, Arboretti Rosa, and Bonnini Stefano. "Cytological and Histological Analysis by Image Processing." In Ranking of Multivariate Populations. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/b19673-10.

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Bug, Daniel, Felix Bartsch, Nadine Sarah Schaadt, et al. "Scalable HEVC for Histological Whole-Slide Image Compression." In Informatik aktuell. Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-29267-6_71.

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Pyatov, Vladislav A., and Dmitry V. Sorokin. "Unsupervised Feature Matching for Affine Histological Image Registration." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-78201-5_3.

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Sloboda, Tibor, Lukáš Hudec, and Wanda Benešová. "Editable Stain Transformation of Histological Images Using Unpaired GANs." In Image Analysis and Processing - ICIAP 2023 Workshops. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51026-7_3.

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Conference papers on the topic "Histological image"

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Haggouni, Jamal, Salma Azzouzi, and Moulay El Hassan Charaf. "Deep Learning Optimizers for Histological Image Classification: A Comparative Study." In 2024 10th International Conference on Optimization and Applications (ICOA). IEEE, 2024. http://dx.doi.org/10.1109/icoa62581.2024.10754238.

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Bayona Quesada, Juan Camilo, Angie Julieth Fuentes Barragán, Gabriel Eduardo Perez, and David Romo-Bucheli. "Domain Adaptation Strategies Based on Color Normalisation for Histological Image Classification." In 2024 20th International Symposium on Medical Information Processing and Analysis (SIPAIM). IEEE, 2024. https://doi.org/10.1109/sipaim62974.2024.10783544.

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Mendoza Chavarría, Juan N., Laura Quintana-Quintana, Samuel Ortega, Gustavo M. Callico, and Daniel U. Campos-Delgado. "Semi-Supervised Hyperspectral Unmixing: Integration of Fixed and Variable End-members." In Latin America Optics and Photonics Conference. Optica Publishing Group, 2024. https://doi.org/10.1364/laop.2024.m3d.3.

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This study introduces a semi-supervised hyperspectral unmixing method, where some specific end-members are known upfront, while others are estimated during the unmixing process. Validation on a brain tissue histological image stained with eosin and hematoxylin confirms its effectiveness.
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VESHKIN, A. S., and A. V. KHVOSTIKOV. "NEURAL NETWORK METHOD FOR CONTENT-BASED HISTOLOGICAL IMAGE RETRIEVAL." In GRAPHICON 2024. Omsk State Technicl University, 2024. http://dx.doi.org/10.25206/978-5-8149-3873-2-2024-618-628.

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In this paper, we study the impact of different approaches to feature extraction from histological images on the performance of content-based retrieval methods. Particular attention is paid to the comparative analysis of three approaches to feature extraction: based on statistical image features, using an autoencoder, and using a hyperbolic extractor. The paper considers theoretical aspects of each approach, adapts them to the tasks of histological image analysis, and experimentally evaluates their performance on the PATH-DT-MSU WSS1, WSS2 histological datasets. The Accuracy@10 and MAP@10 metrics are used to evaluate the search quality. The best results were shown by the method using the hyperbolic feature extractor: 73.45% by Accuracy@10. The approach described in the paper is universal and can be applied to other datasets for the task of content-based retrieval of images.
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Dehua Zhao, Yixin Chen, and N. Correa. "Statistical categorization of human histological images." In rnational Conference on Image Processing. IEEE, 2005. http://dx.doi.org/10.1109/icip.2005.1530470.

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Sun, Zhongao, Alexander Vladimirovich Khvostikov, Andrey Serdjevich Krylov, and Nikolai Krainiukov. "Super-resolution for Whole Slide Histological Images." In 33rd International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2023. http://dx.doi.org/10.20948/graphicon-2023-609-619.

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Histological images serve as crucial tools in the diagnosis and treatment of diverse afflictions. However, the acquisition of images exhibiting exceptional resolution whole slide images, WSIs, capturing intricate textures and vital nuances, can present a formidable challenge, primarily due to the requirement of expensive and intricate apparatus, proficient personnel, and considerable time commitments. To tackle this predicament, it is important that we conceive an effective and precise framework to increase the resolution of whole slide histological images. There are several algorithms used for super-resolution, including interpolation-based, deep learning based and bayes based algorithms. After scrutinizing and dissecting the available super-resolution models and algorithms, we arrived at the conclusion that the most suitable approach for histological WSIs would be to fine-tune the already trained Real-ESRGAN model to reconstruct histological images and apply it in a patch-based way. For histological WSIs, it is typical to have a lagre number of empty areas that do not contain tissue. To circumvent the impact of these empty areas on the model’s efficiency, we filter the dataset using Shannon’s information entropy. Furthermore, we have modified the structure of the loss function to optimize the reconstruction of low-level details of histological images. In this study, we fine-tune the pre-trained Real-ESRGAN model using the histological image dataset PATH-DT-MSU. It enabled us to outperform all preexisting models in terms of reconstructing low-level details in WSI histological images. Moreover, without retraining the model, we have tested it on additional histological image datasets, thereby proving its high generalization ability.
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Kuska, Jens-peer, Ulf-dietrich Braumann, Nico Scherf, et al. "Image Registration of Differently Stained Histological Sections." In 2006 International Conference on Image Processing. IEEE, 2006. http://dx.doi.org/10.1109/icip.2006.313161.

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Lockshin, Nikita Djeffrievich, Alexander Vladimirovich Khvostikov, and Andrey Serdjevich Krylov. "Augmenting Histological Images with Adversarial Attacks." In 32nd International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2022. http://dx.doi.org/10.20948/graphicon-2022-637-647.

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Neural networks have shown to be vulnerable against adversarial attacks - images with carefully crafted adversarial noise that is imperceptible to the human eye. In medical imaging tasks this can be a major threat for making predictions based on deep neural network solutions. In this paper we propose a pipeline for augmenting a small histological image dataset using State-of-the-Art data generation methods and demonstrate an increase in accuracy of a neural classifier trained on the augmented dataset when faced with adversarial images. When trained on the non-augmented dataset, the neural network achieves an accuracy of 55.24 on the test set with added adversarial noise, and an accuracy of 97.40 on the same test set when trained on the augmented dataset.
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Pochernina, Olga Leonidovna, Alexander Vladimirovich Khvostikov, and Andrey Serdjevich Krylov. "Semi-automatic Algorithm for Lumen Segmentation in Histological Images." In 32nd International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2022. http://dx.doi.org/10.20948/graphicon-2022-648-656.

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In this paper we focus on a problem of lumen segmentation in histological images. A large number of annotated images are necessary for the development of diagnostic algorithms that can help to detect changes, such as lumen serration, indicating really serious health problems like cancer. We propose a semi-automatic interactive segmentation algorithm to accelerate the process of manual image annotation. The core of our annotation approach is a classical graph-cut algorithm that uses manually selected parameters. The user annotates an image with two types of scribbles corresponding to the gland lumen structure and the non-lumen area. After that, the model annotates all unlabeled pixels, providing the user with a fully annotated image based on the scribbled input data. The user can interact with the annotation algorithm and add new scribbles to adjust the result. The algorithm allows to reduce the annotation time of a typical histological image by 10 times for the PATH-DT-MSU dataset that can potentially seriously increase the number of fully annotated histological images.
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Imai, Mizuho, Akane Takei, Keita Miyamoto, Masanobu Takahashi, and Masayuki Nakano. "Composite imaging method for histological image analysis." In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2013. http://dx.doi.org/10.1109/embc.2013.6610268.

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Reports on the topic "Histological image"

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George, Bennie. Histological Image Analysis: A Deep Dive. ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/researchhub.kk5ytyrh.

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