Image Analysis Research Articles

Vision Systems for a UR5 Cobot on a Quality Control Robotic Station

This paper addresses the development of a vision system for the UR5 cobot and the corresponding operating algorithm of the robotic quality control station. The hardware–software architecture of the developed robotic station consisting of a UR5 cobot equipped with a web camera and a stationary industrial camera with a lighting system is presented. Image processing and analysis algorithms are described, the method of control and communication between the station components is discussed, and operating scenarios are presented as a single robotic station and as a part of the robotic line. Based on the results which were obtained, the level of measurement noise, accuracy, and repeatability of the developed vision system were estimated. A novel complete vision system architecture with hardware–software modules for the UR5 cobot quality control station is shown and discussed. The software part based on Python 3.12 language, OpenCV 4.7.0.68 libraries, and the PolyScope 1.8 environment incorporates modules for system calibration, image acquisition, preprocessing and image analysis (for objects’ location and geometric measurements) and for robotic cell communication and control. The hardware part of the UR5 cobot vision system is based on a PC with two independent and distinct cameras: one permanently affixed and the other mounted to the cobot’s flange. This innovative setup, combined with software architecture, broadens the scope of existing robotic quality control applications.

On the reliability of image analysis for bubble size distribution measurements in electrolyte solutions in stirred reactors

The translation of chemical processes from laboratory to industrial scale is crucial for the effective and sustainable implementation of new technologies. This transition presents significant challenges, particularly in multiphase systems where variations in physical chemistry can complicate scale-up efforts. A key aspect of this challenge is understanding bubble dynamics in gas-liquid systems, which are pivotal in processes such as hydrogen production and CO2 absorption. Bubble size significantly influences mass transfer rates and process efficiency, necessitating accurate measurement methods. A factorial design approach was employed to assess the sensitivity of results to key parameters. The findings provide quantitative guidelines for optimizing image analysis techniques and improving the accuracy of bubble size measurements in diverse operational conditions. This work advances the understanding of bubble dynamics in gas-liquid systems and offers practical insights for refining measurement techniques, ultimately supporting more effective scale-up of chemical processes.

MSIreg: an R package for unsupervised coregistration of mass spectrometry and H&E images.

Joint analysis of mass spectrometry images (MS images) and microscopy images of hematoxylin and eosin (H&E) stained tissues assists pathologists in characterizing the morphological structure of the tissues, and in performing diagnosis. Unfortunately, the analysis is undermined by substantial differences between these modalities in terms of aspect ratios, spatial resolution, number of channels in each image, as well as by large global or small local elastic spatial deformations of one image with respect to the other. Therefore, accurate coregistration of the images is a critical pre-requisite for their joint interpretation. We introduce MSIreg, an open-source R package for coregistration of MSI and H&E images. MSIreg is designed for high-dimensional MSI experiments where each spatial location is represented by thousands of mass features. Unlike most existing coregistration methods, MSIreg implements a landmark free workflow, and quantitative metrics for performance evaluation. We evaluate the performance of MSIreg on six case studies, including coregistration of contiguous tissues with large deformations, as well as simultaneous coregistration of 29 tissue microarray cores. The R package, installation instructions, and fully reproducible vignettes describing methods and Case Studies are are available open-source under the GPL-3.0 license at https://github.com/sslakkimsetty/msireg/.

Research on Key Technologies of Spatial Domain Image Steganography and Analysis of Typical Applications

In the digital age, secure storage and communication of information are crucial aspects of everyday life, drawing significant global interest. Steganography serves as a pivotal method for concealing private information within various media forms—such as images, videos, and text—enabling secure information transmission. This paper delineates the distinctions between information hiding and encryption and delves into image steganography. The focus here is on spatial-based image steganography techniques, which are broadly categorized into traditional and deep learning-based methods. Traditional methods rely on modifications to the pixel values of the host image, often resulting in minor but detectable changes if not carefully managed. On the other hand, deep learning-based methods leverage neural networks to learn how to encode data in an image in a way that is much harder to detect. A comprehensive performance analysis of these techniques is provided, complete with comparative tables to aid in clarity and reference. Additionally, this paper aims to guide researchers by charting current trends in the field and suggesting future research directions. By exploring these advanced techniques, the paper contributes to the broader discourse on enhancing the security and efficacy of steganographic practices.

Fully automated measurement of noise, signal-to-noise ratio, and contrast-to-noise ratio on chest CT images: feasibility and efficiency.

Rapid and accurate measurement of computed tomography (CT) image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) is a clinical challenge. To explore the feasibility of intelligent measurement of chest CT image noise, SNR, and CNR. A total of 300 chest CT scans were included in the study, which was divided into research dataset, internal test dataset, and external test dataset. Based on the research dataset, automatically segment and measure the average CT values and standard deviation (SD) of CT values for background air and lung field under different thresholds to obtain noise, SNR, and CNR results. Using the results of manual measurements as the reference standard, we determine the optimal threshold with the highest consistency. Using internal and external test datasets, validate the consistency of automated measurements of noise, SNR, and CNR at the optimal CT threshold with reference standards. With background air set at -900 HU and lung field at -800 HU as thresholds, the automated measurements of noise, SNR, and CNR demonstrate the highest consistency with the reference standards. At the optimal threshold, the noise, SNR, and CNR measured automatically on both the internal (intraclass correlation coefficient [ICC] = 0.85-0.96) and external (ICC = 0.75-0.85) test datasets exhibit high consistency with their respective reference standards. The method we explored can intelligently measure the noise, SNR, and CNR of chest CT images, exhibits high consistency with radiologists, and offers a novel tool for image quality evaluation and analysis.

Comparative Analysis of AI Models for Atypical Pigmented Facial Lesion Diagnosis.

Diagnosing atypical pigmented facial lesions (aPFLs) is a challenging topic for dermatologists. Accurate diagnosis of these lesions is crucial for effective patient management, especially in dermatology, where visual assessment plays a central role. Incorrect diagnoses can result in mismanagement, delays in appropriate interventions, and potential harm. AI, however, holds the potential to enhance diagnostic accuracy and provide reliable support to clinicians. This work aimed to evaluate and compare the effectiveness of machine learning (logistic regression of lesion features and patient metadata) and deep learning (CNN analysis of images) models in dermoscopy diagnosis and the management of aPFLs. This study involved the analysis of 1197 dermoscopic images of facial lesions excised due to suspicious and histologically confirmed malignancy, classified into seven classes (lentigo maligna-LM; lentigo maligna melanoma-LMM; atypical nevi-AN; pigmented actinic keratosis-PAK; solar lentigo-SL; seborrheic keratosis-SK; and seborrheic lichenoid keratosis-SLK). Image samples were collected through the Integrated Dermoscopy Score (iDScore) project. The statistical analysis of the dataset shows that the patients mean age was 65.5 ± 14.2, and the gender was equally distributed (580 males-48.5%; 617 females-51.5%). A total of 41.7% of the sample constituted malignant lesions (LM and LMM). Meanwhile, the benign lesions were mainly PAK (19.3%), followed by SL (22.2%), AN (10.4%), SK (4.0%), and SLK (2.3%). The lesions were mainly localised in the cheek and nose areas. A stratified analysis of the assessment provided by the enrolled dermatologists was also performed, resulting in 2445 evaluations of the 1197 images (2.1 evaluations per image on average). The physicians demonstrated higher accuracy in differentiating between malignant and benign lesions (71.2%) than in distinguishing between the seven specific diagnoses across all the images (42.9%). The logistic regression model obtained a precision of 39.1%, a sensitivity of 100%, a specificity of 33.9%, and an accuracy of 53.6% on the test set, while the CNN model showed lower sensitivity (58.2%) and higher precision (47.0%), specificity (90.8%), and accuracy (59.5%) for melanoma diagnosis. This research demonstrates how AI can enhance the diagnostic accuracy in complex dermatological cases like aPFLs by integrating AI models with clinical data and evaluating different diagnostic approaches, paving the way for more precise and scalable AI applications in dermatology, showing their critical role in improving patient management and the outcomes in dermatology.

Implementing Antimony Supply and Sustainability Measures via Extraction as a By-Product in Skarn Deposits: The Case of the Chalkidiki Pb-Zn-Au Mines

Antimony is one of the world’s scarcest metals and is listed as a Critical Raw Material (CRM) for the European Union. To meet the increasing demand for metals in a sustainable way, one of the strategies that could be implemented would be the recovery of metals as by-products. This would decrease the amount of hazardous materials filling mining dumps. The present study investigates the potential for producing antimony as a by-product at the Olympias separation plant in Northern Greece. This plant works a skarn mineralization that shows interesting amounts of Sb. Boulangerite (Pb5Sb4S11) reports on Pb concentrate levels reached 8% in the analyzed product. This pre-enrichment is favorable in terms of boulangerite recovery since it can be separated from galena through froth flotation. Boulangerite distribution in the primary ore is quite heterogeneous in terms of the inclusion relationships and grain size. However, a qualitative assessment shows that the current Pb concentrate grain size is too coarse to successfully liberate a good amount of boulangerite. The use of image analysis and textural assessments is pivotal in determining shape factors and crystal size, which is essential for the targeting of flotation parameters during separation. The extraction of antimony as a by-product is possible through a two-step process; namely, (i) the preliminary concentration of boulangerite, followed by (ii) the hydrometallurgical extraction of the antimony from the boulangerite concentrate. The Olympias enrichment plant could therefore set a positive example by promoting the benefits of targeted Sb extraction as a by-product within similar sulfide deposits within the European territory.

A Novel Lipid Nanoparticle NBF-006 Encapsulating Glutathione S-Transferase P siRNA for the Treatment of KRAS-driven Non-small Cell Lung Cancer.

Non-small cell lung cancer (NSCLC) accounts for approximately 85% of lung cancers, and KRAS mutations occur in 25-30% of NSCLC. Our approach to developing a therapeutic with the potential to target KRAS mutant NSCLC was to identify a new target involved in modulating signaling proteins in the RAS pathway. Glutathione S-Transferase P (GSTP) known as a Phase II detoxification enzyme has more recently been identified as a modulator of MAP kinase-related cell-signaling pathways. Therefore, developing a GSTP siRNA may be an effective therapeutic approach to treat KRAS mutant NSCLC. The lead drug product candidate (NBF-006) is a proprietary siRNA-based lipid nanoparticle (LNP) comprising GSTP siRNA (NDT-05-1040). Here, studies using a panel of KRAS mutant NSCLC cell lines demonstrated that NDT-05-1040 is a very potent and selective GSTP siRNA inhibitor. Our Western blot analysis showed that NDT-05-1040 effectively decreased the phosphorylation of MAPK and PI3K pathway components while upregulating apoptotic signaling cascade. Our in vivo studies revealed statistically significant higher distribution of NBF-006 to the lungs and tumor as compared to liver. In the subcutaneous and orthotopic tumor models, NBF-006 led to a statistically significant and dose dependent anti-tumor growth inhibition. Further, quantitative image analysis of PCNA and PARP staining showed that NBF-006 decreased proliferation and induced apoptosis, respectively, in tumors. Additionally, in a surgically implanted orthotopic lung tumor model, the survival rate of the NBF-006 treatment group was significantly prolonged (P <0.005) as compared to the vehicle control group. Together, these preclinical studies supported advancement of NBF-006 into clinical studies.

Core-Periphery Multi-Modality Feature Alignment for Zero-Shot Medical Image Analysis.

Multi-modality learning, exemplified by the language-image pair pre-trained CLIP model, has demonstrated remarkable performance in enhancing zero-shot capabilities and has gained significant attention recently. However, simply applying language-image pre-trained CLIP to medical image analysis encounters substantial domain shifts, resulting in severe performance degradation due to inherent disparities between natural (non-medical) and medical image characteristics. To address this challenge and uphold or even enhance CLIP's zero-shot capability in medical image analysis, we develop a novel approach, Core-Periphery feature alignment for CLIP (CP-CLIP), to model medical images and corresponding clinical text jointly. To achieve this, we design an auxiliary neural network whose structure is organized by the core-periphery (CP) principle. This auxiliary CP network not only aligns medical image and text features into a unified latent space more efficiently but also ensures alignment driven by principles of brain network organization. In this way, our approach effectively mitigates and further enhances CLIP's zero-shot performance in medical image analysis. More importantly, the proposed CP-CLIP exhibits excellent explanatory capability, enabling the automatic identification of critical disease-related regions in clinical analysis. Extensive experiments and evaluation across five public datasets covering different diseases underscore the superiority of our CP-CLIP in zero-shot medical image prediction and critical features detection, showing its promising utility in multimodal feature alignment in current medical applications.

Implantable Intraocular Fluorescence Sensor for Visualized Monitoring of Alzheimer's Disease

AbstractWith an increase in the older individuals, the global incidence of Alzheimer's disease (AD) is increasing. Early diagnosis of AD necessitates long‐term monitoring; however, conventional diagnostic methods such as positron emission tomography (PET) and magnetic resonance imaging (MRI) are unsuitable for frequent use because of infrastructure limitations. The eye, connected to the brain via the optic nerve, offers a potential window for monitoring neurodegenerative diseases. This study presents a novel system for continuous intraocular monitoring using a fluorescent aptamer‐conjugated intraocular lens (FAIOL). FAIOL emits fluorescent signals in the presence of beta‐secretase‐1 (BACE1), an enzyme associated with amyloid beta 1–42 production. These signals can be analyzed using mobile phone applications. In vitro, experiments demonstrates that FAIOL responded to BACE1 by detecting low concentrations of the target molecule over extended periods. Ex vivo tests using porcine eyeballs shows an increase in the fluorescence signal intensity by more than 8% within 5 h in the presence of BACE1. The integrated image analysis program reduces sensor noise and provides numerical signal values, facilitating a straightforward interpretation. FAIOL holds significant potential as an innovative platform for the early diagnosis and long‐term monitoring of AD and promises improved patient outcomes through timely intervention and ongoing surveillance.

A multi-institutional study on Ultrasound image analysis for staging HBV-derived liver fibrosis: a potential non-invasive alternative to liver stiffness measurement

Background: Liver stiffness measurement is principal for staging liver fibrosis but not included in routine examinations. We investigated whether comparable diagnostic performance can be achieved by mining ultrasound images and developing a novel serum index (NSI). Methods: Texture features were extracted from ultrasound images. Spearman correlation and logistics regression selected independent variables for significant (F≥2) and advanced (F≥3) fibrosis. We compared the diagnostic performance of transient elastography (TE), ultrasound image biomarker (UIB), conventional serum indices (APRI, FIB-4, GPR), and NSI in 365 chronic hepatitis B patients. Results: Among patients, 52.1% had significant fibrosis and 24.2% had advanced fibrosis. PLT, GGT, prealbumin, and globulin were incorporated into NSI. In the validation group, TE achieved the best performance (AUC: 0.765 [0.690-0.849] for significant fibrosis; 0.812 [0.745-0.878] for advanced fibrosis), followed by UIB (AUC: 0.712 [0.629-0.795]; 0.678 [0.595-0.763]) and NSI (AUC: 0.630 [0.534-0.725]; 0.659 [0.572-0.745]), outperforming conventional indices. Conclusions: Texture analysis enhances ultrasound's diagnostic utility, but TE remains superior. When TE is unavailable, ultrasound image analysis and NSI, incorporating prealbumin, can serve as alternative tools for fibrosis staging.

AI-enabled workflow for automated classification and analysis of feto-placental Doppler images.

Extraction of Doppler-based measurements from feto-placental Doppler images is crucial in identifying vulnerable new-borns prenatally. However, this process is time-consuming, operator dependent, and prone to errors. To address this, our study introduces an artificial intelligence (AI) enabled workflow for automating feto-placental Doppler measurements from four sites (i.e., Umbilical Artery (UA), Middle Cerebral Artery (MCA), Aortic Isthmus (AoI) and Left Ventricular Inflow and Outflow (LVIO)), involving classification and waveform delineation tasks. Derived from data from a low- and middle-income country, our approach's versatility was tested and validated using a dataset from a high-income country, showcasing its potential for standardized and accurate analysis across varied healthcare settings. The classification of Doppler views was approached through three distinct blocks: (i) a Doppler velocity amplitude-based model with an accuracy of 94%, (ii) two Convolutional Neural Networks (CNN) with accuracies of 89.2% and 67.3%, and (iii) Doppler view- and dataset-dependent confidence models to detect misclassifications with an accuracy higher than 85%. The extraction of Doppler indices utilized Doppler-view dependent CNNs coupled with post-processing techniques. Results yielded a mean absolute percentage error of 6.1 ± 4.9% (n = 682), 1.8 ± 1.5% (n = 1,480), 4.7 ± 4.0% (n = 717), 3.5 ± 3.1% (n = 1,318) for the magnitude location of the systolic peak in LVIO, UA, AoI and MCA views, respectively. The developed models proved to be highly accurate in classifying Doppler views and extracting essential measurements from Doppler images. The integration of this AI-enabled workflow holds significant promise in reducing the manual workload and enhancing the efficiency of feto-placental Doppler image analysis, even for non-trained readers.

TRAITER: Transformer-guided diagnosis and prognosis of heart failure using cell nuclear morphology and DNA damage marker.

Heart failure (HF), a major cause of morbidity and mortality, necessitates precise diagnostic and prognostic methods. This study presents a novel deep learning approach, Transformer-based Analysis of Images of Tissue for Effective Remedy (TRAITER), for HF diagnosis and prognosis. Employing image segmentation techniques and a Vision Transformer, TRAITER predicts HF likelihood from cardiac tissue cell nuclear morphology images and the potential for left ventricular reverse remodeling (LVRR) from dual-stained images with cell nuclei and DNA damage markers. In HF prediction using 31,158 images from 9 patients, TRAITER achieved 83.1% accuracy. For LVRR prediction with 231,840 images from 46 patients, TRAITER attained 84.2% accuracy for individual images and 92.9% for individual patients. TRAITER outperformed other neural network models in terms of receiver operating characteristics, and precision-recall curves. Our method promises to advance personalized HF medicine decision-making. The source code and data are available at the following link: Https://github.com/HamanoLaboratory/predict-of-HF-and-LVRR. Supplementary data are available at Bioinformatics online.

Associations of prostate tumor immune landscape with vigorous physical activity and prostate cancer progression.

Vigorous physical activity has been associated with lower risk of fatal prostate cancer. However, mechanisms contributing to this relationship are not understood. We studied 117 men with prostate cancer in the University of North Carolina Cancer Survivorship Cohort (UNC CSC) who underwent radical prostatectomy, and 101 radiation-treated prostate cancer patients in FASTMAN. Structured questionnaires administered in UNC CSC assessed physical activity. In both studies, digital image analysis of H&E-stained tissues was applied to quantify Tumor Infiltrating Lymphocytes (TILs) in segmented regions. Nanostring gene expression profiling in UNC CSC and microarray in FASTMAN were performed on tumor tissue and a 50-gene signature utilized to predict immune cell types. Vigorous recreational activity, reported by 34 (29.1%) UNC men, was inversely associated with TILs abundance. Tumors of men reporting any vigorous activity versus none showed lower gene expression-predicted abundance of Th, exhausted CD4 T cells and macrophages. T cell subsets, including Treg, Th, Tfh, exhausted CD4 T cells, and macrophages were associated with increased risk of biochemical recurrence, only among men with ERG-positive tumors. Vigorous activity was associated with lower prostate tumor inflammation and immune microenvironment differences. Macrophages and T cell subsets, including those with immunosuppressive roles and those with lower abundance in men reporting vigorous exercise, were associated with worse outcomes in ERG-positive prostate cancer. Our novel findings contribute to our understanding of the role of the tumor immune microenvironment in prostate cancer progression, and may provide insight into how vigorous exercise could affect prostate tumor biology.

Predicting physicochemical properties of papayas (Carica papaya L.) using a convolutional neural networks model approach.

The current state of quality assessment methods for agricultural produce, particularly fruits, heavily relies on manual inspection techniques, which could be subjective, time-consuming, and prone to human errors. Consequently, there have been emerging trends and needs for non-destructive methods to evaluate fruit quality accurately and practically. This research aimed to develop a novel approach for predicting the physicochemical properties of papayas using a convolutional neural network (CNN) model that combines image analysis and weight assessment. This study involved capturing images of papayas at different ripening stages, measuring papaya weights, and determining various physicochemical properties such as texture, pH, total soluble solids, and seed weight. A total of 532 images were obtained from 132 papayas, and an additional 1064 images were generated through image augmentation. The dataset was divided into three sets with an 8:1:1 ratio for training, validation, and testing. The CNN model was trained using papaya images and weights as input values to predict and estimate the physicochemical property values. Model performance was evaluated using mean squared error (MSE) and the coefficient of determination (R2) as metrics. The CNN model, integrated with image processing, could predict the diverse physicochemical properties of papayas with high accuracy. The MSE values estimated for the training and validation sets were 0.0284 and 0.1729, respectively. The R2 values for the test dataset ranged from 0.71 to 0.94. These findings demonstrate that CNN-based models could provide detailed and quantitative insights, facilitating improved understanding and management of papaya quality while enhancing predictive modeling accuracy in agriculture. PRACTICAL APPLICATION: This research introduces a new method for accurately predicting the quality of papayas using a computer model. Instead of relying on manual inspection, which can be slow and prone to errors, this model uses images of papayas and their weights to predict properties, including texture, pH, total soluble solids, and seed weight. This can help manage papaya quality better while also improving agricultural production and transportation processes.

Optical trapping of lipid bodies reveals increased cytoplasm viscosity in Candida tropicalis post antimicrobial photodynamic therapy

Abstract Antimicrobial photodynamic therapy (aPDT) is a common treatment for large cell colonies, but its effectiveness is typically assessed through colony-forming unit counting, which lacks microscopic details about cell death. This study monitors the trap stiffness of optically trapped lipid bodies of Candida tropicalis of approximately 1 μm of radius following aPDT treatment. Methylene blue served as the photosensitizer at 10 μM concentration, with a lethal light dose of 60 J cm-². The results revealed a significant increase in viscosity after aPDT treatment. Additionally, image analysis confirmed substantial morphological changes indicative of cell death. These findings demonstrate the potential of optical tweezers as a non-invasive tool for assessing cellular health by providing both functional (viscosity) and morphological data on the response to aPDT.&amp;#xD;

Real-Time 3D Imaging and Inhibition Analysis of Human Serum Amyloid A Aggregations Using Quantum Dots.

Serum amyloid A (SAA) is one of the most important precursor amyloid proteins discovered during the study of amyloidosis, but its underlying aggregation mechanism has not yet been well elucidated. Since SAA aggregation is a key step in the pathogenesis of AA amyloidosis, amyloid inhibitors can be used as a tool to study its pathogenesis. Previously, we reported a novel microliter-scale high-throughput screening (MSHTS) system for screening amyloid β (Aβ) aggregation inhibitors based on quantum dot (QD) fluorescence imaging technology. In this study, we report the aggregation of human SAA (hSAA) in phosphate-buffered saline, in which we successfully visualized hSAA aggregation by QD using fluorescence microscopy and confocal microscopy. Two-dimensional and three-dimensional image analyses showed that most aggregations were observed at 40 μM hSAA, which was the optimal aggregation concentration in vitro. The accuracy of this finding was verified by a Thioflavin T assay. The transmission electron microscopy results showed that QD uniformly bound to hSAA aggregation. hSAA aggregation inhibitory activity was also evaluated by rosmarinic acid (RA). The results showed that RA, which is a compound with high inhibitory activity against Aβ aggregation, also exhibited high inhibitory activity against 40 μM hSAA. These results indicate that the MSHTS system is an effective tool for visualizing hSAA aggregation and for screening highly active inhibitors.

Detection and classification of breast cancer in mammographic images with fine-tuned convolutional neural networks

ABSTRACT Breast cancer is cancer that forms in the cells of the breasts and is a severe health issue that affects many people around the world, especially since it is the most deadly cancer in women. By finding it early and using new treatments, patients can overcome this challenge and get back to a healthier life. This study proposed a procedure to fine-tune the Convolutional Neural Networks (CNN) model with data preprocessing and augmentation in classifying mammogram images called the Hybrid Mammogram Classification and Detection Pipeline (HMCaD). After using CNN for classification because it brings higher confidence in classifying tasks, the YOLOv8 has been applied for localization subtask to detect abnormal positions with predicted bounding boxes. The database is provided by the Mammographic Image Analysis Society (MIAS) and is protected by the United Kingdom. It comprises 330 samples, including 79 benign, 54 malignant, and 207 normal images. As a result, the classification in our model based on the custom EfficientNetB3 model and seam carving technique received a great validation accuracy, test accuracy, and F1 score throughout three scenarios at 96.73%, 97.59%, and 97.58%, respectively. Furthermore, the area under the Receiver Operating Characteristic (ROC) curve also has a surprise result of 0.96 (i.e. AUC = 0.96 ). Moreover, YOLOv8 for detecting abnormal positions in our study achieved 83.22% in Intersection over Union (IoU). This led to the research reaching good results in classifying and detecting breast cancer by considering several performance metrics.

P1.06A.04 Deep Learning-based Whole Slide Image Analysis Predicts PD-L1 Status from H&E-Stained Histopathology Images in Lung Cancer

P1.06A.04 Deep Learning-based Whole Slide Image Analysis Predicts PD-L1 Status from H&E-Stained Histopathology Images in Lung Cancer

Gut Analysis Toolbox - automating quantitative analysis of enteric neurons.

The enteric nervous system (ENS) consists of an extensive network of neurons and glial cells embedded within the wall of the gastrointestinal (GI) tract. Alterations in neuronal distribution and function are strongly associated with GI dysfunction. Current methods for assessing neuronal distribution suffer from undersampling, partly due to challenges associated with imaging and analyzing large tissue areas, and operator bias due to manual analysis. We present the Gut Analysis Toolbox (GAT), an image analysis tool designed for characterization of enteric neurons and their neurochemical coding using two-dimensional images of GI wholemount preparations. GAT is developed in Fiji, has a user-friendly interface, and offers rapid and accurate segmentation via custom deep learning (DL)-based cell segmentation models developed using StarDist, as well as a ganglia segmentation model in deepImageJ. We apply proximal neighbor-based spatial analysis to reveal differences in cellular distribution across gut regions using a public dataset. In summary, GAT provides an easy-to-use toolbox to streamline routine image analysis tasks in ENS research. GAT enhances throughput, allowing rapid unbiased analysis of larger tissue areas, multiple neuronal markers and numerous samples.