Image Analysis Tools Research Articles

Off‐the‐shelf image analysis models outperform human visual assessment in identifying genes controlling seed color variation in sorghum

AbstractSeed color is a complex phenotype linked to both the impact of grains on human health and consumer acceptance of new crop varieties. Today, seed color is often quantified via qualitative human assessment or biochemical assays for specific colored metabolites. Imaging‐based approaches have the potential to be more quantitative than human scoring while being lower cost than biochemical assays. We assessed the feasibility of employing image analysis tools trained on rice (Oryza sativa) or wheat (Triticum aestivum) seeds to quantify seed color in sorghum (Sorghum bicolor) using a dataset of 1,500 images. Quantitative measurements of seed color from images were substantially more consistent across biological replicates than human assessment. Genome‐wide association studies conducted using color phenotypes for 682 sorghum genotypes identified more signals near known seed color genes in sorghum with stronger support than manually scored seed color for the same experiment. Previously unreported genomic intervals linked to variation in seed color in our study co‐localized with a gene encoding an enzyme in the biosynthetic pathway leading to anthocyanins, tannins, and phlobaphenes—colored metabolites in sorghum seeds—and with the sorghum ortholog of a transcription factor shown to regulate several enzymes in the same pathway in rice. The cross‐species transferability of image analysis tools, without the retraining, may aid efforts to develop higher value and health‐promoting crop varieties in sorghum and other specialty and orphan grain crops.

Novel high-content and open-source image analysis tools for profiling mitochondrial morphology in neurological cell models.

Mitochondria undergo dynamic morphological changes depending on cellular cues, stress, genetic factors, or disease. The structural complexity and disease-relevance of mitochondria have stimulated efforts to generate image analysis tools for describing mitochondrial morphology for therapeutic development. Using high-content analysis, we measured multiple morphological parameters and employed unbiased feature clustering to identify the most robust pair of texture metrics that described mitochondrial state. Here, we introduce a novel image analysis pipeline to enable rapid and accurate profiling of mitochondrial morphology in various cell types and pharmacological perturbations. We applied a high-content adapted implementation of our tool, MitoProfilerHC, to quantify mitochondrial morphology changes in i) a mammalian cell dose response study and ii) compartment-specific drug effects in primary neurons. Next, we expanded the usability of our pipeline by using napari, a Python-powered image analysis tool, to build an open-source version of MitoProfiler and validated its performance and applicability. In conclusion, we introduce MitoProfiler as both a high-content-based and an open-source method to accurately quantify mitochondrial morphology in cells, which we anticipate to greatly facilitate mechanistic discoveries in mitochondrial biology and disease.

Enhancing Medical Image Diagnosis Using Convolutional Neural Network and Transfer Learning

Medical image diagnosis is crucial for early disease detection and effective treatment planning. Traditional diagnostic methods, while effective, often struggle with the complexities and variability inherent in medical images. Recent advancements in deep learning, particularly through Convolutional Neural Networks (CNNs) and transfer learning, have shown promise in overcoming these challenges. This study investigates the application of CNNs combined with transfer learning to improve diagnostic accuracy and efficiency. We employed several state-of-the-art CNN architectures, including VGG16 and ResNet, and utilized pre-trained models to leverage existing knowledge and reduce the need for extensive training data. Our approach was validated using a comprehensive dataset of [specific type of medical images, e.g., chest X-rays, MRI scans], and performance was evaluated based on metrics such as accuracy, precision, recall, and F1 score. The results demonstrated a significant improvement in diagnostic performance, with the CNN model augmented by transfer learning achieving an accuracy of [specific accuracy, e.g., 95.2%], compared to [previous methods' accuracy, e.g., 85.3%] for traditional methods. This research highlights the potential of integrating advanced deep learning techniques in medical imaging, offering a robust solution for enhancing diagnostic precision and efficiency. These findings contribute to the growing body of evidence supporting the use of CNNs and transfer learning as transformative tools in medical image analysis.

SAUNAS. II. Discovery of Cross-shaped X-Ray Emission and a Rotating Circumnuclear Disk in the Supermassive S0 Galaxy NGC 5084

Combining Chandra, Atacama Large Millimeter/submillimeter Array, EVLA, and Hubble Space Telescope archival data and newly acquired data from the Apache Point Observatory/Dual Imaging Spectrograph, we detect a double-lobed 17 kpc X-ray emission with plumes oriented approximately perpendicular and parallel to the galactic plane of the massive lenticular galaxy NGC 5084 at 0.3–2.0 keV. We detect a highly inclined ( i=71.2−1.7+1.8◦ ), molecular circumnuclear disk ( D=304−11+10 pc) in the core of the galaxy rotating (V rot(2−1)CO=242.7−6.4+9.6 km s−1) in a direction perpendicular to that of the galactic disk, implying a total mass of log10MBHM⊙=7.66−0.15+0.21 for NGC 5084's supermassive black hole. Archival EVLA radio observations at 6 and 20 cm reveal two symmetric radio lobes aligned with the galactic plane, extending to a distance of R¯=4.6±0.6kpc from the core, oriented with the polar axis of the circumnuclear disk. The spectral energy distribution lacks strong emission lines in the optical range. Three formation scenarios are considered to explain these multiwavelength archival observations: (1) active galactic nuclei (AGN) reorientation caused by accretion of surrounding material, (2) AGN-driven hot gas outflow directed along the galactic minor axis, or (3) a starburst/supernovae driven outflow at the core of the galaxy. This discovery is enabled by new imaging analysis tools including the Selective Amplification of Ultra Noisy Astronomical Signal, demonstrating the abundance of information still to be exploited in the vast and growing astronomical archives.

Batik Image Representation using Multi Texton Co-occurrence Histogram

This paper introduces a novel approach to batik image representation using the texton-based and statistical Multi Texton Co-occurrence Histogram (MTCH). The MTCH framework is leveraged as a robust batik image descriptor, capable of encapsulating a comprehensive range of visual features, including the intricate interplay of color, texture, shape, and statistical attributes. The research extensively evaluates the effectiveness of MTCH through its application on two well-established public batik datasets, namely Batik 300 and Batik Nitik 960. These datasets serve as benchmarks for assessing the performance of MTCH in both classification and image retrieval tasks. In the classification domain, four distinct scenarios were explored, employing various classifiers: the K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), Decision Tree (DT), and Naïve Bayes (NB). Each classifier was rigorously tested to determine its efficacy in correctly identifying batik patterns based on the MTCH descriptors. On the other hand, the image retrieval tasks were conducted using several distance metrics, including the Euclidean distance, City Block, Bray Curtis, and Canberra, to gauge the retrieval accuracy and the robustness of the MTCH framework in matching similar batik images. The empirical results derived from this study underscore the superior performance of the MTCH descriptor across all tested scenarios. The evaluation metrics, including accuracy, precision, and recall, indicate that MTCH not only achieves high classification performance but also excels in retrieving images with high similarity to the query. These findings suggest that MTCH is a highly effective tool for batik image analysis, offering significant potential for applications in cultural heritage preservation, textile pattern recognition, and automated batik classification systems.

Enhancing Laboratory Efficiency: Implementing Custom Image Analysis Tools for Streamlined Pathology Workflows

In the field of pathology, the efficient analysis and interpretation of diagnostic images are critical for timely and accurate decision-making. Traditional manual methods for image analysis are often time-consuming, error-prone, and resource-intensive, leading to delays in diagnosis and increased workloads for pathologists. To address these challenges, this paper explores the development and implementation of custom image analysis tools to streamline pathology workflows. The integration of machine learning (ML) algorithms, computer vision techniques, and automation technologies into laboratory settings has the potential to significantly enhance the speed and accuracy of image processing tasks. This study examines how tailored image analysis solutions can optimize tasks such as tissue segmentation, feature extraction, and classification of abnormal cells. The use of such tools not only improves the diagnostic workflow but also reduces human error, enhances reproducibility, and facilitates real-time analysis. Additionally, the paper discusses the practical considerations for implementing these technologies, including software customization, integration with existing laboratory information systems, and user training. By leveraging the power of custom-built image analysis solutions, pathology laboratories can improve operational efficiency, reduce turnaround times for results, and ultimately enhance patient outcomes. The research provides insights into the future of digital pathology and offers a roadmap for laboratories looking to adopt cutting-edge technologies to stay at the forefront of diagnostic innovation.

Effect of Polyvinylidene Fluoride Binder Distribution on Dry Spray-Coated Graphite Electrodes for Lithium-Ion Batteries for Electric Vehicle Applications

Dry process manufacturing techniques, which involve fabricating electrodes without the use of solvents and directly depositing electrode materials onto current collectors, have emerged as an alternative to traditional lithium-ion battery (LIB) electrode preparation methods1. In the conventional wet process, solvent-based mixtures, known as slurries, are coated onto metallic current collectors. These slurries typically contain electrochemically active material, conductive additive, polymer binder, and solvent for binder dissolution. For cathodes, polyvinylidene fluoride (PVdF) dissolved in N-methyl-2-pyrrolidone (NMP) is commonly used.2 The growing interest in solvent-free techniques is due to their numerous advantages over traditional methods. For instance, eliminating the use of NMP during cathode production reduces energy consumption by almost 47% during the manufacturing process and decreases total costs by around 15%.3,4 This approach is also relevant for anodes, where a mix of sodium carboxymethyl cellulose (CMC) and styrene-butadiene rubber (SBR) dissolved in water is used. The uniform mixing distribution of the binder and CB additive materials throughout the active material is crucial for manufacturing dry-processed LIB electrodes because it can affect the electrode characteristics such as homogeneity of binder distribution, porosity, adhesion, and cohesion between active material and carbon black (CB) conducting agent particles and the current collector, electrical properties, and therefore the battery performance5–10.For our study, we prepared negative electrode powder mixtures consisting of graphite and a low-content of PVdF through a dry-mixing process at room temperature, varying the mixing time. Then, these powder electrode mixtures were subjected to a high-voltage electrostatic dry coating-spray process and applied onto a carbon-coated copper current collector. Afterwards, the electrodes were calendered at high temperature with a determined applied force.Our objective was to characterize the mixing quality in the electrode powder mixtures and explore its correlation with electrode quality. To accomplish this, we employed various methods, including notably Scanning Electron Microscopy (SEM) imaging and powder resistivity tests conducted via impedance measurements with precise pressure control. Additionally, image analysis tools were utilized to quantify the fraction of binder distributed on the surface of the graphite particles and differentiate it from the binder fraction forming agglomerates. An example of this quantification can be observed in Fig. 1a-b, where a graphite particle covered by PVdF nanoparticles is depicted, showcasing how the image analysis tool accurately distinguishes between both materials. This allowed for a better understanding and rationalization of the influence of PVdF binder distribution on the properties of the electrodes.Characterizing the electrodes produced from these powder mixtures was equally important and included a wide range of analyses such as morphology assessment using SEM and Energy Dispersive X-ray Spectroscopy (EDS), as well as surface resistivity measurements. Mechanical properties were evaluated through peel strength tests to measure electrode-current collector adhesion force and nanoindentation tests for hardness, elastic modulus, and plasticity. Electrochemical evaluations were conducted focusing on rate capability to determine the delithiation capacity at various delithiation rates, as well as through long-term cycling measurements.Our research findings suggest that mixing time, particularly the binder distribution, significantly impacts the morphology, electrical, and electrochemical properties of our electrodes. This exploration enabled us to identify optimal parameters for electrode fabrication, resulting in high-quality electrodes with relatively short production times and favorable electrochemical and mechanical properties. These electrodes exhibit performance comparable to those produced through conventional wet-slurry based methods.

A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities.

Biomedical image analysis is fundamental for biomedical discovery. Holistic image analysis comprises interdependent subtasks such as segmentation, detection and recognition, which are tackled separately by traditional approaches. Here, we propose BiomedParse, a biomedical foundation model that can jointly conduct segmentation, detection and recognition across nine imaging modalities. This joint learning improves the accuracy for individual tasks and enables new applications such as segmenting all relevant objects in an image through a textual description. To train BiomedParse, we created a large dataset comprising over 6 million triples of image, segmentation mask and textual description by leveraging natural language labels or descriptions accompanying existing datasets. We showed that BiomedParse outperformed existing methods on image segmentation across nine imaging modalities, with larger improvement on objects with irregular shapes. We further showed that BiomedParse can simultaneously segment and label all objects in an image. In summary, BiomedParse is an all-in-one tool for biomedical image analysis on all major image modalities, paving the path for efficient and accurate image-based biomedical discovery.

Insights into the Identification of iPSC- and Monocyte-Derived Macrophage-Polarizing Compounds by AI-Fueled Cell Painting Analysis Tools.

Macrophage polarization critically contributes to a multitude of human pathologies. Hence, modulating macrophage polarization is a promising approach with enormous therapeutic potential. Macrophages are characterized by a remarkable functional and phenotypic plasticity, with pro-inflammatory (M1) and anti-inflammatory (M2) states at the extremes of a multidimensional polarization spectrum. Cell morphology is a major indicator for macrophage activation, describing M1(-like) (rounded) and M2(-like) (elongated) states by different cell shapes. Here, we introduced cell painting of macrophages to better reflect their multifaceted plasticity and associated phenotypes beyond the rigid dichotomous M1/M2 classification. Using high-content imaging, we established deep learning- and feature-based cell painting image analysis tools to elucidate cellular fingerprints that inform about subtle phenotypes of human blood monocyte-derived and iPSC-derived macrophages that are characterized as screening surrogate. Moreover, we show that cell painting feature profiling is suitable for identifying inter-donor variance to describe the relevance of the morphology feature 'cell roundness' and dissect distinct macrophage polarization signatures after stimulation with known biological or small-molecule modulators of macrophage (re-)polarization. Our novel established AI-fueled cell painting analysis tools provide a resource for high-content-based drug screening and candidate profiling, which set the stage for identifying novel modulators for macrophage (re-)polarization in health and disease.

Radiomics-Based Diagnosis in Dentomaxillofacial Radiology: A Systematic Review.

Radiomics is a quantitative tool for digital image analysis. This systematic review aims to investigate the scientific articles to evaluate the potential implications of Radiomics analysis in Dentomaxillofacial Radiology (DMFR). Studies regarding Radiomics applications in DMFR and human samples, in vivo study, a case reports/series if ≧5 samples were included, while case reports/series if < 5 samples, articles other than in English, abstracts without full text, and studies published before 2015 were excluded. Fifty-one articles were selected from 3789 literatures. The QUADAS-2 tool was used for risk of bias assessment. The accuracy of predicting dentomaxillofacial pathologies was considered as the primary outcome, and the modeling type of Radiomics was considered as the secondary outcome. A meta-analysis could not be performed due to the lack of information and standardization among the reported accuracies. The reported accuracies were found between 0.66 and 99.65%. Logistic regression (n = 6) was found to be the most common Radiomics modeling type, followed by Support Vector Machine and Decision Tree (n = 5). Second-order statistics (n = 38) was the most common type of Radiomics application, followed by first-order (n = 26), higher-order (n = 20), and shape-based (n = 15) statistics. Further work is needed to increase standardization in the Radiomics workflow. Quantitative image analysis is an alternative tool for conventional visual radiographic evaluation. Radiomics systems depend on elements such as imaging modality, feature type, data mining, or statistical method. Radiomics applications do not justify digital transformation on their own, but the potential of its integration into the digital workflow is considerable.

Remote Sensing and GIS in Natural Resource Management: Comparing Tools and Emphasizing the Importance of In-Situ Data

Remote sensing (RS) and Geographic Information Systems (GISs) provide significant opportunities for monitoring and managing natural resources across various temporal, spectral, and spatial resolutions. There is a critical need for natural resource managers to understand the expanding capabilities of image sources, analysis techniques, and in situ validation methods. This article reviews key image analysis tools in natural resource management, highlighting their unique strengths across diverse applications such as agriculture, forestry, water resources, soil management, and natural hazard monitoring. Google Earth Engine (GEE), a cloud-based platform introduced in 2010, stands out for its vast geospatial data catalog and scalability, making it ideal for global-scale analysis and algorithm development. ENVI, known for advanced multi- and hyperspectral image processing, excels in vegetation monitoring, environmental analysis, and feature extraction. ERDAS IMAGINE specializes in radar data analysis and LiDAR processing, offering robust classification and terrain analysis capabilities. Global Mapper is recognized for its versatility, supporting over 300 data formats and excelling in 3D visualization and point cloud processing, especially in UAV applications. eCognition leverages object-based image analysis (OBIA) to enhance classification accuracy by grouping pixels into meaningful objects, making it effective in environmental monitoring and urban planning. Lastly, QGIS integrates these remote sensing tools with powerful spatial analysis functions, supporting decision-making in sustainable resource management. Together, these tools when paired with in situ data provide comprehensive solutions for managing and analyzing natural resources across scales.

Artificial intelligence-based prediction of neurocardiovascular risk score from retinal swept-source optical coherence tomography–angiography

The recent rise of artificial intelligence represents a revolutionary way of improving current medical practices, including cardiovascular (CV) assessment scores. Retinal vascular alterations may reflect systemic processes such as the presence of CV risk factors. The value of swept-source retinal optical coherence tomography–angiography (SS OCT-A) imaging is significantly enhanced by image analysis tools that provide rapid and accurate quantification of vascular features. We report on the interest of using machine-learning (ML) and deep-learning (DL) models for CV assessment from SS OCT-A microvasculature imaging. We assessed the accuracy of ML and DL algorithms in predicting the CHA2DS2-VASc neurocardiovascular score based on SS OCT-A retinal images of patients from the open-source RASTA dataset. The ML and DL models were trained on data from 491 patients. The ML models tested here achieved good performance with area under the curve (AUC) values ranging from 0.71 to 0.96. According to a classification into two neurocardiovascular risk groups, the EfficientNetV2-B3, a well suited DL model for retinal OCT-A images, predicted risk correctly in 68% of cases, with a mean absolute error (MAE) of approximately 0.697. Our models enable a confident prediction of the CHA2DS2-VASc score from SS OCT-A imaging, which could be a useful tool contributing to the assessment of neurocardiovascular profiles in the future.

DDDNet: A lightweight and robust deep learning model for accurate segmentation and analysis of TEM images

The primary aim of this study was to develop an optimal, lightweight model for the segmentation of transmission electron microscopy (TEM) images. Our model is designed with a minimal parameter count, superior performance metrics, and robust adaptability to variations in substrates, nanoparticle sizes, and nanomaterial diversity within TEM images. In achieving this, we benchmarked our model against four deep learning models using subsets from the Bright-Field TEM(BF-TEM) and Au-TEM datasets. Our model demonstrated exceptional segmentation performance, requiring only 0.34 M parameters and 39.33 G floating-point operations. It also provided the most accurate estimates of average nanoparticle sizes, closely matching true labeled values. These results confirm the model’s proficiency and precision in TEM image processing and introduce a powerful tool for nanoscale image analysis. Our work sets a new standard for lightweight and efficient TEM segmentation models, paving the way for future advancements in nanotechnology research.

Agricultural spray drone deposition, Part 1: methods for high-throughput spray pattern analysis

Abstract Effective pesticide application is dependent on precise and sufficient delivery of active ingredients to targeted pests. Water-sensitive papers (WSPs) have been used to estimate the stain coverage, droplet density, droplet size, total spray volume, and other spray-quality metrics by analyzing deposit stains using image analysis software. However, because WSPs are expensive, they are typically distributed along unidimensional transects at intervals of 0.5 m or more, which comprises 0.5% or less of the total treated area. This might limit the ability to accurately represent the deposition of agricultural sprayers with irregular patterns, such as agricultural drone sprayers in the early developmental stage. This study introduces a novel approach utilizing white Kraft paper and a blue colorant proxy for assessing spray deposition. A custom Python-based image analysis tool, SprayDAT (Spray Droplet Analysis Tool), was developed and compared with traditional image analysis software, DepositScan. Both models showed increased accuracy in detecting larger objects, with SprayDAT generally performing better for smaller droplets. DepositScan underestimated the total deposited spray volume by up to 2.7 times less compared with the colorant extraction assessed via spectrophotometry and the predicted output based on flow rate, coverage, and speed. Accuracy of software-estimated spray volume declined with increasing total stain coverage, likely due to overlapping stain objects. Droplet density exhibited a Gaussian trend, with peak density at approximately 22% stain cover, offering evidence for overlapped stains for both DepositScan and SprayDAT as stain cover increased. Both models showed exponential growth in volumetric median diameter (VMD) with increasing stain cover. SprayDAT is freely accessible through an online repository. It features a user-friendly interface for batch processing large sets of scanned images and offers versatility for customization to meet individual needs, such as adjusting spread factor, updating the standard curve for spray volume estimation, or modifying the stain detection threshold.

In-depth morphological and functional analysis of ATTR-CA myocardial cells and niche by electron microscopic 3D reconstruction

Abstract Background Cell-cell interactions and interplay with extracellular matrix are known to be important in the steady state and pathological conditions, including wild-type transthyretin cardiac amyloidosis (wtATTR-CA). However, alterations of the microstructure of cardiomyocytes (CMs) and cardiac niches have not been well analyzed. Normal histological staining is usually limited in resolution, and 2D electron microscopy cannot capture stereoscopic images or the distribution of target particles and organelles in intra- and extracellular 3D space, so novel methods are being attempted. Methods and Results Using Serial Block Face-Scanning Electron Microscopy (SBF-SEM) and a 3D structural reconstruction system, we for the first time investigated the microstructure of human myocardial tissue from control hypertrophied heart (hypertensive heart disease) and wtATTR-CA patients. The microstructure of myocardial niche, cell morphology/numbers, and the distribution of intracellular organelles such as intercalated discs (ICDs) were compared to control endomyocardial biopsy (EMB) samples (2 controls vs 5 wtATTR-CA patients). In all wtATTR-CA samples, amyloid fibers were deposited originating around the capillary structures with various electron densities, compressing myocardial cells not only from the periphery but also dividing them from the center of the cell body, and CMs showed degeneration and dwarfing according to the severity of amyloid fiber deposition (Figure 1). The capillary structures showed degeneration and narrowing with stratified basement membrane, and rarefaction (3378±216.5 vs 1448±116.8/mm2, p&amp;lt;0.0001). Notably, the stromal cells (pericytes, fibroblasts, macrophages) were more likely buried by amyloid fibers than CMs, and their numbers were also reduced, indicating a loss of adjacent and intercellular interaction with capillaries and CMs (Fig. 2, 1892±194.4 vs 1189±107.4 mm2, p=0.0043). This method can also be used to analyze intracellular organelles and microstructures in CMs. In ATTR-CA, morphological abnormalities were observed in ICDs (Figure 3) and mitochondria. Conclusion In the ATTR-CM patients, there was a significant decrease in the number of capillaries in accordance with the deposition of myocardial amyloid fibers, as well as a decrease in stromal cells. These results support the hypothesis that amyloid fibril deposition not only causes chronic myocardial ischemia, but also reduces the number of niche cells, which play a supportive role for CM function and entirely exacerbate the damage. The SBF-SEM is an innovative tool for 3D imaging and quantitative analysis of myocardial ultrastructure and functions.Methods &amp; Figure 1Figure 2 and Figure 3

Improving Prostate Image Segmentation Based on Equilibrium Optimizer and Cross-Entropy

Over the past decade, the development of computer-aided detection tools for medical image analysis has seen significant advancements. However, tasks such as the automatic differentiation of tissues or regions in medical images remain challenging. Magnetic resonance imaging (MRI) has proven valuable for early diagnosis, particularly in conditions like prostate cancer, yet it often struggles to produce high-resolution images with clearly defined boundaries. In this article, we propose a novel segmentation approach based on minimum cross-entropy thresholding using the equilibrium optimizer (MCE-EO) to enhance the visual differentiation of tissues in prostate MRI scans. To validate our method, we conducted two experiments. The first evaluated the overall performance of MCE-EO using standard grayscale benchmark images, while the second focused on a set of transaxial-cut prostate MRI scans. MCE-EO’s performance was compared against six stochastic optimization techniques. Statistical analysis of the results demonstrates that MCE-EO offers superior performance for prostate MRI segmentation, providing a more effective tool for distinguishing between various tissue types.

Concordance of Whole-Slide Imaging and Conventional Light Microscopy for Assessment of Pathologic Response Following Neoadjuvant Therapy for Lung Cancer

Pathologic response is an endpoint in many ongoing clinical trials for neoadjuvant regimens, including immune checkpoint blockade and chemotherapy. Whole-slide scanning of glass slides generates high-resolution digital images and allows for remote review and potential measurement with image analysis tools, but concordance of pathologic response assessment on digital scans compared with that on glass slides has yet to be evaluated. Such a validation goes beyond previous concordance studies, which focused on establishing surgical pathology diagnoses, as it requires quantitative assessment of tumor, necrosis, and regression. Further, as pathologic response assessment is being used as an endpoint, such concordance studies have regulatory implications. The purpose of this study was 2-fold, which was as follows: first, to determine the concordance between pathologic response assessed on glass slides and that assessed on digital scans, and second, to determine if pathologists benefited from using measurement tools when determining pathologic response. To that end, hematoxylin and eosin–stained glass slides from 64 non–small cell lung carcinoma specimens were visually assessed for percent residual viable tumor (%RVT). The sensitivity and specificity for digital vs glass reads of pathologic complete response (0% RVT) and major pathologic response (≤10% RVT) were all >95%. When %RVT was considered as a continuous variable, the intraclass correlation coefficient of digital vs glass reads was 0.94. The visual assessments of pathologic response were supported by pathologist annotations of residual tumor and tumor bed areas. In a separate subset of hematoxylin and eosin–stained glass slides, several measurement approaches to quantifying %RVT were performed. Pathologist estimates strongly reflected measured %RVT. This study demonstrates the high level of concordance between glass slides evaluated using light microscopy and digital whole-slide images for pathologic response assessments. Pathologists did not require measurement tools to generate robust %RVT values from slide annotations. These findings have broad implications for improving clinical workflows and multisite clinical trials.

Enhanced powder characteristics of succinic acid through crystallization techniques for food industry application

During crystallization, succinic acid, an important food additive, exhibit severe agglomeration behaviour, uneven crystal size distribution (CSD), and irregular crystal morphology, resulting in poor powder flow properties. This paper presents an innovative approach to optimize succinic acid crystallization through investigation of novel blade milling method, seeding, and inclusion of green additive, cetyltrimethylammonium bromide (CTAB). CTAB for crystal habit regulation and formation of desired cubic-like morphology. We employ Mask R-CNN, a deep learning-based image analysis tool to quantitatively assess crystal and powder flow properties. Results demonstrate that the inclusion of 0.5 wt% CTAB with two-stage cooling method yields uniform CSD ranging from 30 μm to 160 μm and improves powder properties, reducing the agglomeration degree by 46% and caking ratio to only 6.78%, indicating a decreased tendency for clumping. The integration of crystallization with the proposed deep learning framework not only improves succinic acid food functionality but also showcases highly efficient method for optimizing crystal properties, setting new standard for food crystallization processes. It has been adequately presented with real-time high-precision analysis of CSD, agglomeration, etc, facilitating rapid screening for optimized food quality improvements.

Findings in Corneal Endothelium by Confocal and Specular Microscopy in Patients with Fuchs Uveitis Syndrome Undergoing Phacoemulsification

ABSTRACT Purpose To compare corneal endothelial changes in patients with Fuchs Uveitis Syndrome (FUS) undergoing phacoemulsification surgery using confocal and specular microscopy. Methods We included 14 patients with unilateral FUS and cataracts who underwent phacoemulsification surgery in a Mexican referral center for inflammatory eye diseases. Preoperative confocal and specular microscopies were conducted, establishing baseline images for subsequent analyses. Surgery on the FUS eye was performed by a single surgeon and an intraocular lens was implanted in all cases. Both specular and confocal microscopy were repeated 6 months after FUS eye surgery and compared with baseline images. We used Image J to do a manual segmentation of KP and determine their density for further analysis hence developing a new tool for confocal microscopy image analysis. Results All patients underwent uneventful phacoemulsification surgery in the FUS eye. There was no significant change in endothelial cell density (ECD) from 2257 (±508.2) cells/mm2 preoperatively to 2214 (±535.1) cells/mm2 postoperatively (p = 0.809). Confocal microscopy revealed a decrease in Keratic Precipitate Density (KPD) from a median of 1413 (±2809.7) KPs/mm2 preoperatively to a median of 685.5 (1527.9) KPs/mm2 postoperatively (p = 0.036). Conclusions Phacoemulsification surgery in patients with FUS produces no significant loss of endothelial cells and morphological changes that can be detected by confocal and specular microscopy. We found a reduction in KPD 6 months after surgery on confocal microscopy. Additionally, our manual segmentation technique for KPs utilizing Image J offers a novel and practical approach for confocal microscopy image analysis.

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.