Image Analysis Method Research Articles

Antibody selection and automated quantification of TRPV1 immunofluorescence on human skin

AbstractAssessing localization of the transient receptor potential vanilloid-1 (TRPV1) in skin nerve fibers is crucial for understanding its role in peripheral neuropathy and pain. However, information on the specificity and sensitivity of TRPV1 antibodies used for immunofluorescence (IF) on human skin is currently lacking. To find a reliable TRPV1 antibody and IF protocol, we explored antibody candidates from different manufacturers, used rat DRG sections and human skin samples for screening and human TRPV1-expressing HEK293 cells for further validation. Final specificity assessment was done on human skin samples. Additionally, we developed two automated image analysis methods: a Python-based deep-learning approach and a Fiji-based machine-learning approach. These methods involve training a model or classifier for nerve fibers based on pre-annotations and utilize a nerve fiber mask to filter and count TRPV1 immunoreactive puncta and TRPV1 fluorescence intensity on nerve fibers. Both automated analysis methods effectively distinguished TRPV1 signals on nerve fibers from those in keratinocytes, demonstrating high reliability as evidenced by excellent intraclass correlation coefficient (ICC) values exceeding 0.75. This method holds the potential to uncover alterations in TRPV1 associated with neuropathic pain conditions, using a minimally invasive approach.

Evaluation of Electrical Properties and Uniformity of Single Wall Carbon Nanotube Dip-Coated Conductive Fabrics Using Convolutional Neural Network-Based Image Analysis

This study proposes a convolutional neural network (CNN)-based image analysis method to evaluate the electrical properties and uniformity of conductive fabrics treated with single-walled carbon nanotube (SWCNT) dip-coating. The conductive fabric was produced by dip-coating cotton-blended spandex with SWCNT, and the surface images were scanned and preprocessed to obtain image data, while resistance measurements were conducted to obtain labels and build the dataset. SEM analysis revealed that as the number of dip-coating cycles increased, particle density and path formation improved. The CNN model learned the relationship between surface images and resistance values, achieving a high predictive performance, with an R-squared (R²) value of 0.9422. The model demonstrated prediction accuracies of 99.1792% for the coefficient of variation (CV) of uniformly coated fabrics and 96.8877% for non-uniformly coated fabrics. Additionally, p-value analysis of all fabric samples yielded a result of 0.96044, indicating no statistically significant difference between the predicted and actual values. The proposed CNN-based model can accurately evaluate the electrical uniformity of conductive fabrics, showing potential for contributing to quality control and process optimization in production.

Application of Binary Image Quality Assessment Methods to Predict the Quality of Optical Character Recognition Results

One of the continuous challenges related to the growing popularity of mobile devices and embedded systems with limited memory and computational power is the development of relatively fast methods for real-time image and video analysis. One such example is Optical Character Recognition (OCR), which is usually too complex for such devices. Considering that images captured by cameras integrated into mobile devices may be acquired in uncontrolled lighting conditions, some quality issues related to non-uniform illumination may affect the image binarization results and further text recognition results. The solution proposed in this paper is related to a significant reduction in the computational burden, preventing the necessity of full text recognition. Conducting only the initial image binarization using various thresholding methods, the computation of the mutual similarities of binarization results is proposed, making it possible to build a simple model of binary image quality for a fast prediction of the OCR results’ quality. The experimental results provided in the paper obtained for the dataset of 1760 images, as well as the additional verification for a larger dataset, confirm the high correlation of the proposed quality model with text recognition results.

Improving CNN Fish Detection and Classification with Tracking

The regular and consistent monitoring of marine ecosystems and fish communities is becoming more and more crucial due to increasing human pressures. To this end, underwater camera technology has become a major tool to collect an important amount of marine data. As the size of the data collected outgrew the ability to process it, new means of automatic processing have been explored. Convolutional neural networks (CNNs) have been the most popular method for automatic underwater video analysis for the last few years. However, such algorithms are rather image-based and do not exploit the potential of video data. In this paper, we propose a method of coupling video tracking and CNN image analysis to perform a robust and accurate fish classification on deep sea videos and improve automatic classification accuracy. Our method fused CNNs and tracking methods, allowing us to detect 12% more individuals compared to CNN alone.

Training for Emergencies - How Germany is Preparing for Large-Scale Emergencies Using the EUROMED 2024 Civil Protection Exercise as an Example

Abstract. Every year, numerous major events with thousands of participants take place around the world, for which extensive safety measures are taken to ensure the well-being and safety of visitors. Nevertheless, incidents that lead to injuries and deaths occur time and again. This year, Germany is looking forward to the UEFA EURO 2024, an international football championship for which the host country, Germany, has made extensive preparations on several levels. One component of this is the Medical Task Force (MTF) of the Federal Office of Civil Protection and Disaster Assistance (BBK), which has trained a possible medical service deployment in the event of a mass casualty incident with a specially coordinated series of exercises. The extent to which current remote sensing data can make a useful contribution and be meaningfully integrated was analyzed during the large-scale EUROMED exercise with the help of an airborne camera system on board a helicopter. The system provided up-to-date aerial images of the exercise site, but was also used to record the traffic situation during the transfer of the vehicle convoys to the exercise site. In addition, state-of-the-art AI-based image analysis methods were tested on site and the results were provided and evaluated. An important operational exercise to bring science and practice together step by step in order to be better prepared for an emergency.

Effectiveness and impact factors of passive convergence-permeable reactive barrier (PC-PRB): Insights from tracer simulation study

Passive convergence-permeable reactive barrier (PC-PRB) represents a green and sustainable technology for in-situ remediation of contaminated groundwater. A laboratory-scale PC-PRB tracer simulation system was established to quantify its contaminant plume capture performance using image analysis method. Results indicate that PC-PRB captures the plume 65% wider than C-PRB, which means that fewer PRB sizes and materials volume would be necessary to treat an equivalent contaminated plume. This improvement is due to a significant drawdown within the PC-PRB's passive well, known as the passive hydraulic decompression-convergent flow effect. We further evaluated the effects of water pipe length, hydraulic gradient, and media particle size on PC-PRB's plume capture performance. Results indicate that an increased water pipe length enhances the PC-PRB's plume capture capacity due to greater well drawdown. PC-PRB not only captures the plume but also acts as a hydraulic barrier. The retardation effect of PC-PRB on plume migration increases with water pipe length. Conversely, both hydraulic gradient and media particle size impact the plume capture capacity of PC-PRB by modifying groundwater flow velocity and pollutant dispersion. An increase in either hydraulic gradient or media particle size decreases the plume capture performance of PC-PRB. Therefore, PC-PRB technology may be more effective in contaminated sites characterized by low hydraulic gradients and permeability. Overall, PC-PRB demonstrates significant effectiveness in enhancing plume capture performance, which can notably reduce remediation costs and environmental footprint, broadening its application scope.

Filling the gaps: Introducing plasticizers into π-conjugated OPE-NH2 Langmuir layers for defect-free anisotropic interfaces and membranes towards unidirectional mass, charge, or energy transfer

The construction of ultrathin membranes from linearly aligned π-electron systems is advantageous for targeted energy, charge, or mass transfer. The Langmuir-Blodgett (LB) technique enables the creation of such membranes, especially with amphiphilic π-electron systems. However, these systems often aggregate, forming rigid Langmuir monolayers with defects or holes. In this study we introduce plasticizers to effectively address this issue. To create anisotropic membranes, we used an oligo(phenylene ethynylene) derivative (OPE-NH2) as an linear amphiphile and bisphenol A di-tert-butyl ester (BPAE) as a plasticizer. We analyzed surface pressure (mean molecular area) (Π(mma)) isotherms and characterized Langmuir monolayers with Brewster Angle Microscopy (BAM), to determine the optimal miscibility of OPE-NH2 with BPAE. Detailed analysis of hole areas filled was performed through image binarization. We identified an optimal BPAE concentration of 4 mol-% in the OPE-NH2 Langmuir monolayer. Our BAM image evaluation via binarization determined the difference between the mean molecular areas of close-packed Langmuir domains and those quantified via the Π(mma) isotherm. This study presents an automated method for BAM image analysis and a new approach for fabricating defect-free anisotropic molecular monolayers of π-conjugated amphiphiles.

Deep Learning Based Detection of Silicosis from Computed Tomography Images

Artificial intelligence has increasingly been used in interpreting medical images to support the timely treatment of diseases by providing early and accurate diagnosis. Pneumoconiosis is a tissue reaction that develops as a result of the accumulation of inorganic dust in the lungs. The most common types of pneumoconiosis include diseases such as coal worker's pneumoconiosis, silicosis, asbestosis, and siderosis. Silicosis, which has maintained its importance since the 1900s and has seen over 182,000 articles published in the last 10 years, is a global health problem. The automated detection and recognition of silicosis in lung computed tomography (CT) images can be considered the backbone of assisting the silicosis diagnosis process. Automated medical assistance systems developed using artificial intelligence can simplify the medical examination process and reduce the time required to start accurate treatment. Although the literature contains various studies that benefit silicosis diagnosis using chest X-ray images or pneumoconiosis diagnosis using CT images, there is not enough classification study that can particularly aid the diagnosis of silicosis in CT images.The method of early detection of silicosis from chest radiographs and CT images has been a challenging task due to the high variability among pneumoconiosis readers. Based on the success of deep learning in the classification and segmentation of medical images, this study has shown that deep learning networks and transfer learning algorithms can detect silicosis with high accuracy by classifying CT images. The performance of the six algorithms examined in the study is compared, and the algorithm with the best performance is recommended. Performance criteria such as accuracy, precision, specificity, and F1-score of the algorithms used in the study were calculated. The accuracy rates of the models were obtained as 92.62%, 93.03%, 92.76%, 95.38%, 97.29%, and 95.17% for AlexNet, VGG16, ResNet50, InceptionV3, Xception, and DenseNet121, respectively. These results show that Xception outperformed the other algorithms and was the most successful algorithm in the automatic detection of silicosis with an accuracy rate of 97.29%.Additionally, a new dataset consisting of tomography images from silicosis patients is presented in this study. Experimental results have shown that transfer learning algorithms can significantly benefit the diagnosis of silicosis by successfully classifying CT images. The findings of the study highlight the clinical importance of artificial intelligence methods in medical image analysis and early disease diagnosis.

Cement vs aggregates and textures of aggregates in a mortar: Comparative image analysis methods and analytical protocols

Cement vs aggregates and textures of aggregates in a mortar: Comparative image analysis methods and analytical protocols

Measuring pore sizes of nonwoven geotextiles by X-ray CT images: a comparative evaluation

This paper focuses on evaluating the pore sizes of four nonwoven geotextile specimens with different pore size distribution techniques. The paper introduces a bubble analysis technique to find the two-dimensional (2D) pore size distribution of geotextile specimens. A new method is also introduced to determine the three-dimensional (3D) constriction sizes of nonwoven geotextiles from X-ray CT images. The digital image concept and image processing methods for the 2D image analysis and 3D constriction size measurement of geotextile specimens are explained. Finally, the paper compares the 3D constriction sizes with those measured from 2D image analysis, mercury intrusion porosimetry, manufacturer-reported opening size values based on the wet sieving method, and pore sizes calculated from theoretical equations. The results show that the largest pore opening sizes measured from the 3D constriction size method are in good agreement with the theoretical equations. The values measured from the wet sieving method were found to be the smallest, indicating the limitations of the wet sieving method to determine the largest pore opening size of nonwoven geotextiles. As it is the constriction size which must control filtration behaviour, the 3D constriction size method is recommended for assessing the reference porometry.

Single Vesicle Surface Protein Profiling and Machine Learning-Based Dual Image Analysis for Breast Cancer Detection.

Single-vesicle molecular profiling of cancer-associated extracellular vesicles (EVs) is increasingly being recognized as a powerful tool for cancer detection and monitoring. Mask and target dual imaging is a facile method to quantify the fraction of the molecularly targeted population of EVs in biofluids at the single-vesicle level. However, accurate and efficient dual imaging vesicle analysis has been challenging due to the interference of false signals on the mask images and the need to analyze a large number of images in clinical samples. In this work, we report a fully automatic dual imaging analysis method based on machine learning and use it with dual imaging single-vesicle technology (DISVT) to detect breast cancer at different stages. The convolutional neural network Resnet34 was used along with transfer learning to produce a suitable machine learning model that could accurately identify areas of interest in experimental data. A combination of experimental and synthetic data were used to train the model. Using DISVT and our machine learning-assisted image analysis platform, we determined the fractions of EpCAM-positive EVs and CD24-positive EVs over captured plasma EVs with CD81 marker in the blood plasma of pilot HER2-positive breast cancer patients and compared to those from healthy donors. The amount of both EpCAM-positive and CD24-positive EVs was found negligible for both healthy donors and Stage I patients. The amount of EpCAM-positive EVs (also CD81-positive) increased from 18% to 29% as the cancer progressed from Stage II to III. No significant increase was found with further progression to Stage IV. A similar trend was found for the CD24-positive EVs. Statistical analysis showed that both EpCAM and CD24 markers can detect HER2-positive breast cancer at Stages II, III, or IV. They can also differentiate individual cancer stages except those between Stage III and Stage IV. Due to the simplicity, high sensitivity, and high efficiency, the DISVT with the AI-assisted dual imaging analysis can be widely used for both basic research and clinical applications to quantitatively characterize molecularly targeted EV subtypes in biofluids.

Mapping 18F-FDG PET uptake in the aortic wall and thrombus

Abstract Background Positron Emission Tomography (PET) uptake allows for the assessment of molecular activity, being 18F-FDG uptake indicative of glucose metabolism. PET uptake is commonly quantified manually by drawing regions of interest, which is subjective, poorly-reproducible and may miss relevant information. Purpose To develop and test an image analysis method to quantify and map 18F-FDG uptake on the whole aortic wall and thrombus. Methods PET-MRI was acquired in patients with thoracic or abdominal aortic aneurysms. The aorta was semi-automatically segmented in the MR angiography using 3DSlicer, generating a surface mesh that was expanded by 3 mm inward and outward. PET uptake was interpolated over this volume with a uniform spatial resolution of 1 mm. By means of 9 reference points, the aortic wall was discretized into 52 (thoracic) or 80 (thoraco-abdominal) standardized regions (i.e. wall patches). For each aortic patch and for the whole thrombus volume, the target to background Ratio (TBR) was calculated by dividing the uptake by a reference uptake in the left atrium. Two observers segmented the aorta and thrombus, located the anatomical reference points and quantified TBR. Inter-observer agreement for segmentation and aortic wall discretization was evaluated in terms of the 95% Hausdorff Distance (HD) and Dice Score Coefficient (DSC), while both median and 95% TBR in each region were tested via intra-class correlation coefficient (absolute value, single measure). Results Data from 20 patients with thoracic or abdominal aneurysms were analysed, 10 of them presented thrombus. The reproducibility of the segmentation of the aorta (median HD 3.16 IQR [2.42 - 3.94] mm, DSC 0.90 [0.89 - 0.92]) and of the thrombus (HD 4.87 [3.51 - 8.45] mm, DSC 0.85 [0.80 - 0.87]) were excellent. The inter-observer agreement in the localization of reference points was good (HD 5.10 [3.29 - 8.73] mm), resulting in excellent overlapping of the standardized aortic wall patches (HD 3.80 [2.44 - 5.08] mm). Inter-observer agreement of median and 95% TBR in each aortic patch were excellent (ICC of 0.90 and 0.93, respectively, Figure 1), with minimal bias (Figure 2). Similarly, excellent interobserver reproducibility was observed for median and 95% TBR in the thrombus (ICC of 0.97 and 0.96, respectively, Figure 2). Conclusions A robust and reproducible image analysis method to map PET uptake along the aortic wall was developed, extending the possibilities to study aortic diseases molecular activity and its association with local aortic characteristics.

A review on depth perception techniques in organoid images

Organoids are an in vitro model that can simulate the complex structure and function of tissues in vivo. Functions such as classification, screening and trajectory recognition have been realized through organoid image analysis, but there are still problems such as low accuracy in recognition classification and cell tracking. Deep learning algorithm and organoid image fusion analysis are the most advanced organoid image analysis methods. In this paper, the organoid image depth perception technology is investigated and sorted out, the organoid culture mechanism and its application concept in depth perception are introduced, and the key progress of four depth perception algorithms such as organoid image and classification recognition, pattern detection, image segmentation and dynamic tracking are reviewed respectively, and the performance advantages of different depth models are compared and analyzed. In addition, this paper also summarizes the depth perception technology of various organ images from the aspects of depth perception feature learning, model generalization and multiple evaluation parameters, and prospects the development trend of organoids based on deep learning methods in the future, so as to promote the application of depth perception technology in organoid images. It provides an important reference for the academic research and practical application in this field.

Universal high-throughput image quantification of subcellular structure dynamics and spatial distributions within cells.

Current methods for image analysis in microscopy are tailored to specific biological contexts, which creates challenges in implementation and efficiency for researchers studying a diverse range of subcellular processes. Our application of dispersion indices, traditionally used in economics, offers a universal framework for high throughput quantification of biological structures, enabling easier and more consistent analysis across various biological contexts. By transforming pixel intensity and count into meaningful statistical measures, our method simplifies the quantification of subcellular structures such as autophagic puncta, microtubule dynamics, and mitochondrial clustering. This approach accelerates the quantitative analysis of sub-cellular processes in disease as well as imaged-based drug discovery. Classification: Bioengineering, Cell Biology, Applied Biological Sciences.

Spatial analysis by current multiplexed imaging technologies for the molecular characterisation of cancer tissues.

Tumours are composed of tumour cells and the surrounding tumour microenvironment (TME), and the molecular characterisation of the various elements of the TME and their interactions is essential for elucidating the mechanisms of tumour progression and developing better therapeutic strategies. Multiplex imaging is a technique that can quantify the expression of multiple protein markers on the same tissue section while maintaining spatial positioning, and this method has been rapidly developed in cancer research in recent years. Many multiplex imaging technologies and spatial analysis methods are emerging, and the elucidation of their principles and features is essential. In this review, we provide an overview of the latest multiplex imaging techniques by type of imaging and staining method and an introduction to image analysis methods, primarily focusing on spatial cellular properties, providing deeper insight into tumour organisation and spatial molecular biology in the TME.

Simulation-based analysis of impact of CNT dispersion on hydration of cement paste

Dispersion of carbon nanotubes (CNTs) is one of the key factors determining the mechanical properties of cement composites. This study analyzed impact of CNT dispersion on hydration of cement paste. An image analysis method based on optical microscopy images was proposed to quantitatively consider the CNT dispersion. To quantify CNT dispersion, this paper proposed PCNT, which is the percentage of a 1 μm2 space containing CNTs within entire water volume. To investigate CNT dispersion effect, the same amount of CNT was classified into six steps (S1, S5, S15, S30, S50, and S100) according to PCNT. The relationship between CNT dispersion and the hydration, porosity, and compressive strength of the cement paste was analyzed using a CEMHYD3D considered CNT effect. The optimal CNT dispersion for 0.1 wt% CNT reinforcement in a cement paste with water-to-cement ratio of 0.3 was found to be 15 % PCNT. The improvement in CNT dispersion resulted in acceleration of hydration of cement paste, improvement in compressive strength, and densified microstructure.

A Enhanced Process for Examining Resumes and Organizing Candidates Through Statistical Methods

Selecting suitable applicants is an important part of the screening process in a recruitment consulting firm. In general, traditional recruiting practices are used by the majority of businesses and consulting organizations. The primary strategy is employing resume-based screening techniques to attract exceptionally skilled researchers and developers for high-tech enterprises. Typically, the organization evaluates the individual's qualifications, professional history, employment experience, motivations, specialist abilities, and past creative projects as shown in their résumé in order to select the most suited candidate. The study sought to examine and mechanize efficient screening techniques utilized by consulting firms, emphasizing the need of ongoing research in comprehending evolving customer requirements and enhancing the screening process. Various techniques typically employ automated image analysis and heuristic methods to extract tabular bioinformatics data from resumes, with the aim of gathering pertinent information. This research employs optical character recognition (OCR) as it circumvents the inherent automotive engineering of the document and has been demonstrated to be a viable approach. Resumes in PDF format were gathered via convenience sampling and systematic random sampling, and data was then extracted from the resume documents by OCR. The pertinent data is subsequently analyzed using statistical techniques such as the Chi-square test, percentage studies, and the weighted average approach based on factors such as experience, technical/non-technical skills, employability, career advancement, salary expectations, and role/designation expectations. The proposed approach empowered the selection of the appropriate resumes for the subsequent recruitment progression.. sections. Such references are made by indicating the section number, for example, “In Sec. 2 we showed...” or “Section 2.1 contained a description....” If the word Section, Reference, Equation, or Figure starts a sentence, it is spelled out. When occurring in the middle of a sentence, these words are abbreviated Sec., Ref., Eq., and Fig. At the first occurrence of an acronym, spell it out followed by the acronym in parentheses, e.g., charge-coupled diode (CCD). Key Words: Resume screening process; Optical character recognition; Recruitment; Chi-square test, subsequently analyzed pertinent data; efficient screening technique; appropriate resume; PDF;

A Comprehensive and Repeatable Contrast-Enhanced Ultrasound Quantification Approach for Clinical Evaluations of Tumor Blood Flow.

The aim of this study is to define a comprehensive and repeatable contrast-enhanced ultrasound (CEUS) imaging protocol and analysis method to quantitatively assess lesional blood flow. Easily repeatable CEUS evaluations are essential for longitudinal treatment monitoring. The quantification method described here aims to provide a structure for future clinical studies. This retrospective analysis study included liver CEUS studies in 80 patients, 40 of which contained lesions (primarily hepatocellular carcinoma, n = 28). Each patient was given at least 2 injections of a microbubble contrast agent, and 60-second continuous loops were acquired for each injection to enable evaluation of repeatability. For each bolus injection, 1.2 mL of contrast was delivered, whereas continuous, stationary scanning was performed. Automated respiratory gating and motion compensation algorithms dealt with breathing motion. Similar in size regions of interest were drawn around the lesion and liver parenchyma, and time-intensity curves (TICs) with linearized image data were generated. Four bolus transit parameters, rise time (RT), mean transit time (MTT), peak intensity (PI), and area under the curve (AUC), were extracted either directly from the actual TIC data or from a lognormal distribution curve fitted to the TIC. Interinjection repeatability for each parameter was evaluated with coefficient of variation. A 95% confidence interval was calculated for all fitted lognormal distribution curve coefficient of determination (R2) values, which serves as a data quality metric. One-sample t tests were performed between values obtained from injection pairs and between the fitted lognormal distribution curve and direct extraction from the TIC calculation methods to establish there were no significant differences between injections and measurement precision, respectively. Average interinjection coefficient of variation with both the fitted curve and direct calculation of RT and MTT was less than 21%, whereas PI and AUC were less than 40% for lesion and parenchyma regions of interest. The 95% confidence interval for the R2 value of all fitted lognormal curves was [0.95, 0.96]. The 1-sample t test for interinjection value difference showed no significant differences, indicating there was no relationship between the order of the repeated bolus injections and the resulting parameters. The 1-sample t test between the values from the fitted lognormal distribution curve and the direct extraction from the TIC calculation found no statistically significant differences (α = 0.05) for all perfusion-related parameters except lesion and parenchyma PI and lesion MTT. The scanning protocol and analysis method outlined and validated in this study provide easily repeatable quantitative evaluations of lesional blood flow with bolus transit parameters in CEUS data that were not available before. With vital features such as probe stabilization ideally performed with an articulated arm and an automated respiratory gating algorithm, we were able to achieve interinjection repeatability of blood flow parameters that are comparable or surpass levels currently established for clinical 2D CEUS scans. Similar values and interinjection repeatability were achieved between calculations from a fitted curve or directly from the data. This demonstrated not only the strength of the protocol to generate TICs with minimal noise, but also suggests that curve fitting might be avoided for a more standardized approach. Utilizing the imaging protocol and analysis method defined in this study, we aim for this methodology to potentially assist clinicians to assess true perfusion changes for treatment monitoring with CEUS in longitudinal studies.

Multi-domain improves classification in out-of-distribution and data-limited scenarios for medical image analysis

Current machine learning methods for medical image analysis primarily focus on developing models tailored for their specific tasks, utilizing data within their target domain. These specialized models tend to be data-hungry and often exhibit limitations in generalizing to out-of-distribution samples. In this work, we show that employing models that incorporate multiple domains instead of specialized ones significantly alleviates the limitations observed in specialized models. We refer to this approach as multi-domain model and compare its performance to that of specialized models. For this, we introduce the incorporation of diverse medical image domains, including different imaging modalities like X-ray, MRI, CT, and ultrasound images, as well as various viewpoints such as axial, coronal, and sagittal views. Our findings underscore the superior generalization capabilities of multi-domain models, particularly in scenarios characterized by limited data availability and out-of-distribution, frequently encountered in healthcare applications. The integration of diverse data allows multi-domain models to utilize information across domains, enhancing the overall outcomes substantially. To illustrate, for organ recognition, multi-domain model can enhance accuracy by up to 8% compared to conventional specialized models.

Image Analysis in Histopathology and Cytopathology: From Early Days to Current Perspectives.

Both pathology and cytopathology still rely on recognizing microscopical morphologic features, and image analysis plays a crucial role, enabling the identification, categorization, and characterization of different tissue types, cell populations, and disease states within microscopic images. Historically, manual methods have been the primary approach, relying on expert knowledge and experience of pathologists to interpret microscopic tissue samples. Early image analysis methods were often constrained by computational power and the complexity of biological samples. The advent of computers and digital imaging technologies challenged the exclusivity of human eye vision and brain computational skills, transforming the diagnostic process in these fields. The increasing digitization of pathological images has led to the application of more objective and efficient computer-aided analysis techniques. Significant advancements were brought about by the integration of digital pathology, machine learning, and advanced imaging technologies. The continuous progress in machine learning and the increasing availability of digital pathology data offer exciting opportunities for the future. Furthermore, artificial intelligence has revolutionized this field, enabling predictive models that assist in diagnostic decision making. The future of pathology and cytopathology is predicted to be marked by advancements in computer-aided image analysis. The future of image analysis is promising, and the increasing availability of digital pathology data will invariably lead to enhanced diagnostic accuracy and improved prognostic predictions that shape personalized treatment strategies, ultimately leading to better patient outcomes.