Image Analysis Tools Research Articles

Detecting the corneal neovascularisation area using artificial intelligence

AimsTo create and assess the performance of an artificial intelligence-based image analysis tool for the measurement and quantification of the corneal neovascularisation (CoNV) area.MethodsSlit lamp images of patients with CoNV...

New interactive machine learning tool for marine image analysis.

Advancing imaging technologies are drastically increasing the rate of marine video and image data collection. Often these datasets are not analysed to their full potential as extracting information for multiple species is incredibly time-consuming. This study demonstrates the capability of the open-source interactive machine learning tool, RootPainter, to analyse large marine image datasets quickly and accurately. The ability of RootPainter to extract the presence and surface area of the cold-water coral reef associate sponge species, Mycale lingua, was tested in two datasets: 18 346 time-lapse images and 1420 remotely operated vehicle video frames. New corrective annotation metrics integrated with RootPainter allow objective assessment of when to stop model training and reduce the need for manual model validation. Three highly accurate M. lingua models were created using RootPainter, with an average dice score of 0.94 ± 0.06. Transfer learning aided the production of two of the models, increasing analysis efficiency from 6 to 16 times faster than manual annotation for time-lapse images. Surface area measurements were extracted from both datasets allowing future investigation of sponge behaviours and distributions. Moving forward, interactive machine learning tools and model sharing could dramatically increase image analysis speeds, collaborative research and our understanding of spatiotemporal patterns in biodiversity.

Bringing Artificial Intelligence (AI) into Environmental Toxicology Studies: A Perspective of AI-Enabled Zebrafish High-Throughput Screening.

The booming development of artificial intelligence (AI) has brought excitement to many research fields that could benefit from its big data analysis capability for causative relationship establishment and knowledge generation. In toxicology studies using zebrafish, the microscopic images and videos that illustrate the developmental stages, phenotypic morphologies, and animal behaviors possess great potential to facilitate rapid hazard assessment and dissection of the toxicity mechanism of environmental pollutants. However, the traditional manual observation approach is both labor-intensive and time-consuming. In this Perspective, we aim to summarize the current AI-enabled image and video analysis tools to realize the full potential of AI. For image analysis, AI-based tools allow fast and objective determination of morphological features and extraction of quantitative information from images of various sorts. The advantages of providing accurate and reproducible results while avoiding human intervention play a critical role in speeding up the screening process. For video analysis, AI-based tools enable the tracking of dynamic changes in both microscopic cellular events and macroscopic animal behaviors. The subtle changes revealed by video analysis could serve as sensitive indicators of adverse outcomes. With AI-based toxicity analysis in its infancy, exciting developments and applications are expected to appear in the years to come.

Quaternion fast and accurate polar harmonic Fourier moments for color image analysis and object recognition

Image moments, as a kind of global feature descriptor of images, have become a valuable tool for pattern recognition and image analysis. However, traditional methods are mainly used to deal with grayscale images. In this paper, we apply quaternions to fast and accurate polar harmonic Fourier moments, proposing a kind of quaternion fast and accurate polar harmonic Fourier moment (QFAPHFM) capable of handling color images. Furthermore, this paper provides a detailed analysis of the invariance of QFAPHFMs under rotation, scaling, and translation transformations. The experimental results show that QFAPHFMs exhibit excellent performance in both image reconstruction and object recognition tasks. QFAPHFMs achieve accurate image reconstruction under noiseless and noisy conditions, and demonstrate excellent recognition performance in the color-based object recognition tasks.

ГЛУБОКАЯ НЕЙРОННАЯ СЕТЬ ДЛЯ ОБНАРУЖЕНИЯ ТОЧЕК НА ИЗОБРАЖЕНИЯХ С ИСПОЛЬЗОВАНИЕМ KERAS

Convolutional neural networks are a powerful tool for image processing and analysis. They allow you to automatically extract features from the input data and apply them 
 to classification, object detection and other computer vision tasks. This article presents the convolutional neural networks architecture developed using the Keras library. The architecture of a convolutional neural network, which consists of several successive layers, is analyzed. The structure of the model has been built, which will be trained using the Adam optimizer and monitor the recall and precision metrics during the training process. The results of experiments are presented, which showed that the trained model successfully detects points in images, achieving high accuracy and completeness. The proposed model can be used in various areas where the detection of facial points in photographs is required.

Mutation mapping of a variegated EMS tomato reveals an FtsH-like protein precursor potentially causing patches of four phenotype classes in the leaves with distinctive internal morphology

BackgroundLeaf variegation is an intriguing phenomenon observed in many plant species. However, questions remain on its mechanisms causing patterns of different colours. In this study, we describe a tomato plant detected in an M2 population of EMS mutagenised seeds, showing variegated leaves with sectors of dark green (DG), medium green (MG), light green (LG) hues, and white (WH). Cells and tissues of these classes, along with wild-type tomato plants, were studied by light, fluorescence, and transmission electron microscopy. We also measured chlorophyll a/b and carotene and quantified the variegation patterns with a machine-learning image analysis tool. We compared the genomes of pooled plants with wild-type-like and mutant phenotypes in a segregating F2 population to reveal candidate genes responsible for the variegation.ResultsA genetic test demonstrated a recessive nuclear mutation caused the variegated phenotype. Cross-sections displayed distinct anatomy of four-leaf phenotypes, suggesting a stepwise mesophyll degradation. DG sectors showed large spongy layers, MG presented intercellular spaces in palisade layers, and LG displayed deformed palisade cells. Electron photomicrographs of those mesophyll cells demonstrated a gradual breakdown of the chloroplasts. Chlorophyll a/b and carotene were proportionally reduced in the sectors with reduced green pigments, whereas white sectors have hardly any of these pigments. The colour segmentation system based on machine-learning image analysis was able to convert leaf variegation patterns into binary images for quantitative measurements. The bulk segregant analysis of pooled wild-type-like and variegated progeny enabled the identification of SNP and InDels via bioinformatic analysis. The mutation mapping bioinformatic pipeline revealed a region with three candidate genes in chromosome 4, of which the FtsH-like protein precursor (LOC100037730) carries an SNP that we consider the causal variegated phenotype mutation. Phylogenetic analysis shows the candidate is evolutionary closest to the Arabidopsis VAR1. The synonymous mutation created by the SNP generated a miRNA binding site, potentially disrupting the photoprotection mechanism and thylakoid development, resulting in leaf variegation.ConclusionWe described the histology, anatomy, physiology, and image analysis of four classes of cell layers and chloroplast degradation in a tomato plant with a variegated phenotype. The genomics and bioinformatics pipeline revealed a VAR1-related FtsH mutant, the first of its kind in tomato variegation phenotypes. The miRNA binding site of the mutated SNP opens the way to future studies on its epigenetic mechanism underlying the variegation.

An Effective analysis of brain tumor detection using deep learning

INTRODUCTION: Cancer remains a significant health concern, with early detection crucial for effective treatment. Brain tumors, in particular, require prompt diagnosis to improve patient outcomes. Computational models, specifically deep learning (DL), have emerged as powerful tools in medical image analysis, including the detection and classification of brain tumors. DL leverages multiple processing layers to represent data, enabling enhanced performance in various healthcare applications.
 OBJECTIVES: This paper aims to discuss key topics in DL relevant to the analysis of brain tumors, including segmentation, prediction, classification, and assessment. The primary objective is to employ magnetic resonance imaging (MRI) pictures for the identification and categorization of brain malignancies. By reviewing prior research and findings comprehensively, this study provides valuable insights for academics and professionals in deep learning seeking to contribute to brain tumor identification and classification.
 METHODS: The methodology involves a systematic review of existing literature on DL applications in brain tumor analysis, focusing on MRI imaging. Various DL techniques, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models, are explored for their efficacy in tasks such as tumor segmentation, prediction of tumor characteristics, classification of tumor types, and assessment of treatment response.
 RESULTS: The review reveals significant advancements in DL-based approaches for brain tumor analysis, with promising results in segmentation accuracy, tumor subtype classification, and prediction of patient outcomes. Researchers have developed sophisticated DL architectures tailored to address the complexities of brain tumor imaging data, leading to improved diagnostic capabilities and treatment planning.
 CONCLUSION: Deep learning holds immense potential for revolutionizing the diagnosis and management of brain tumors through MRI-based analysis. This study underscores the importance of leveraging DL techniques for accurate and efficient brain tumor identification and classification. By synthesizing prior research and highlighting key findings, this paper provides valuable guidance for researchers and practitioners aiming to contribute to the field of medical image analysis and improve outcomes for patients with brain malignancies.

ComPRePS: An Automated Cloud-based Image Analysis tool to democratize AI in Digital Pathology.

Artificial intelligence (AI) has extensive applications in a wide range of disciplines including healthcare and clinical practice. Advances in high-resolution whole-slide brightfield microscopy allow for the digitization of histologically stained tissue sections, producing gigapixel-scale whole-slide images (WSI). The significant improvement in computing and revolution of deep neural network (DNN)-based AI technologies over the last decade allow us to integrate massively parallelized computational power, cutting-edge AI algorithms, and big data storage, management, and processing. Applied to WSIs, AI has created opportunities for improved disease diagnostics and prognostics with the ultimate goal of enhancing precision medicine and resulting patient care. The National Institutes of Health (NIH) has recognized the importance of developing standardized principles for data management and discovery for the advancement of science and proposed the Findable, Accessible, Interoperable, Reusable, (FAIR) Data Principles1 with the goal of building a modernized biomedical data resource ecosystem to establish collaborative research communities. In line with this mission and to democratize AI-based image analysis in digital pathology, we propose ComPRePS: an end-to-end automated Computational Renal Pathology Suite which combines massive scalability, on-demand cloud computing, and an easy-to-use web-based user interface for data upload, storage, management, slide-level visualization, and domain expert interaction. Moreover, our platform is equipped with both in-house and collaborator developed sophisticated AI algorithms in the back-end server for image analysis to identify clinically relevant micro-anatomic functional tissue units (FTU) and to extract image features.

Segmentation and deep learning to digitalize the kinematics of flow-type landslides

Flow-type landslides, including subaerial and submarine debris flows, have poor spatiotemporal predictability. Therefore, researchers rely heavily on experimental evidence in revealing complex flow mechanisms and evaluating theoretical models. To measure the velocity field of experimental flows, conventional image analysis tools for measuring soil deformation and hydraulics have been borrowed. However, these tools were not developed for capturing the kinematics of fast-moving soil–water mixtures over complex terrain under non-uniform lighting conditions. In this study, a new framework based on deep learning was used to automatically digitalize the kinematics of experimental flow-type landslides. Captured images were broken into sequences and binarized using a fully convolutional neural network (FCNN). The proposed framework was demonstrated to outperform classic image processing algorithms (e.g., particle image velocimetry, trainable Weka segmentation, and thresholding algorithms) over a wide range of experimental conditions. The FCNN model was even able to process images from consumer-grade cameras under complex shadow, light, and boundary conditions. This feature is most useful for field-scale experimentation. With fewer than 15 annotated training images, the FCNN digitalized experimental flows with an accuracy of 97% in semantic segmentation.

Optimized Federated Learning Algorithm for Breast Cancer Detection Using the Marine Predators Algorithm

Breast cancer stands as the leading cause of cancer-related fatalities among women worldwide, necessitating effective detection methods. However, the current landscape of breast cancer detection poses several challenges, including high costs, complexity, and suboptimal accuracy rates. In response, this paper presents a pioneering approach to breast cancer detection leveraging federated learning, a paradigm that allows model training across distributed data sources without centralized data sharing. Central to this novel detection model is the integration of a Convolutional Neural Network (CNN) architecture and a meta-heuristic optimization algorithm known as the marine predator's algorithm (MPA). CNNs have emerged as powerful tools for biomedical image analysis, enabling automatic feature extraction crucial for detecting abnormalities in medical images. However, optimizing CNNs for detection tasks requires meticulous tuning of hyperparameters across different layers, a process impractical to perform manually due to its complexity and time-intensive nature. To address this challenge, the paper proposes the utilization of the MPA as an optimization algorithm within the federated learning framework. The MPA, inspired by the foraging behaviour of marine predators, enhances the exploration-exploitation trade-off to prevent the model from getting stuck in local optima during training. By incorporating the MPA into the CNN architecture, the proposed model achieves remarkable testing accuracy, surpassing the performance of traditional DCNN models. During testing, the proposed model attained an impressive accuracy rate of 98.32%, showcasing its superior performance compared to the DCNN model, which achieved a 95.95% accuracy. Moreover, the proposed approach outperforms existing algorithms, achieving an accuracy rate of 98%. These results underscore the effectiveness of the federated learning-based approach in enhancing breast cancer detection accuracy. the proposed breast cancer detection model represents a significant advancement in the field, offering a highly accurate and efficient solution to overcome the challenges associated with traditional detection methods. By leveraging federated learning and innovative optimization techniques, this model holds promise for improving early detection rates, ultimately contributing to better patient outcomes in the fight against breast cancer

Quantitative imaging and semiotic phenotyping of mitochondrial network morphology in live human cells.

The importance of mitochondria in tissue homeostasis, stress responses and human diseases, combined to their ability to transition between various structural and functional states, makes them excellent organelles for monitoring cell health. There is therefore a need for technologies to accurately analyze and quantify changes in mitochondrial organization in a variety of cells and cellular contexts. Here we present an innovative computerized method that enables accurate, multiscale, fast and cost-effective analysis of mitochondrial shape and network architecture from confocal fluorescence images by providing more than thirty features. In order to facilitate interpretation of the quantitative results, we introduced two innovations: the use of Kiviat-graphs (herein named MitoSpider plots) to present highly multidimensional data and visualization of the various mito-cellular configurations in the form of morphospace diagrams (called MitoSigils). We tested our fully automated image analysis tool on rich datasets gathered from live normal human skin cells cultured under basal conditions or exposed to specific stress including UVB irradiation and pesticide exposure. We demonstrated the ability of our proprietary software (named MitoTouch) to sensitively discriminate between control and stressed dermal fibroblasts, and between normal fibroblasts and other cell types (including cancer tissue-derived fibroblasts and primary keratinocytes), showing that our automated analysis captures subtle differences in morphology. Based on this novel algorithm, we report the identification of a protective natural ingredient that mitigates the deleterious impact of hydrogen peroxide (H2O2) on mitochondrial organization. Hence we conceived a novel wet-plus-dry pipeline combining cell cultures, quantitative imaging and semiotic analysis for exhaustive analysis of mitochondrial morphology in living adherent cells. Our tool has potential for broader applications in other research areas such as cell biology and medicine, high-throughput drug screening as well as predictive and environmental toxicology.

Abstract 2336: A prototype for a machine-learning assisted image analysis tool for preclinical in vivo studies

Abstract The purpose of this study was to build a software tool to test the feasibility of machine-learning (ML) assisted analysis of in vivo 3D images in oncology research studies. Conventionally, in vivo image segmentations are performed by hand, and are a bottleneck for data analysis, taking time and introducing both inter and intra-user variability. Recently, advances in ML have shown promise in accelerating similar clinical image analysis tasks. Our objective was to work toward making this technology accessible to preclinical cancer researchers. 3D image data across multiple mouse models, organs, modalities, and imaging systems were used. ML segmentation models were trained with user-defined masks on images of mouse bladders (N=134), kidneys (N=131), livers (N=65), and lungs (N=26) from either ultrasound or micro-computed tomography (uCT) scanners. Models were developed using PyTorch/MONAI, hosted in an AWS cloud environment, and served to users via a custom extension for 3D Slicer (www.slicer.org). Performance was assessed as the difference between the human vs. ML organ volumes on a test set unseen by the models during training. Lastly, the functionality of on-the-fly segmentation improvement tools through iterative user feedback was assessed. Results demonstrated that generating segmentations with the prototype ML software took 2-6 seconds per image running on a modern GPU (NVIDIA RTX 3090), compared to 3-7 minutes if performed manually by an expert user (>30x improvement). The Dice scores between the ML generated and human segmentations ranged from 0.81 (liver ultrasound) to 0.84 (bladder ultrasound) but could be improved using ML-based on-the-fly mask improvement tools. Using data from this pilot study, we estimate that the threshold to achieve 90% accuracy on first-pass segmentations is between 200-300 training images, depending on the variance observed in the image data. In summary, the prototype software was able to decrease the manual analysis time across a variety of preclinical in vivo image modalities, which is broadly relevant to preclinical oncology research. The next steps for this project will be to extend the platform to additional orthotopic tumor models (e.g. pancreatic, liver, prostate, etc.) and deploy to the field to enable access to this toolset by preclinical imaging scientists across the world. Citation Format: Adam M. Aji, Thomas M. Kierski, Juan D. Rojas, Hannah Morilak, Olivia J. Kelada, Kathryn H. Gessner, Andrew S. Gdowski, Lucia Kim, William Y. Kim, Ryan C. Gessner, Tomek J. Czernuszewicz. A prototype for a machine-learning assisted image analysis tool for preclinical in vivo studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2336.

Exploring water-absorbing capacity: a digital image analysis of seeds from 120 wheat varieties

Wheat is a staple food crop that provides a significant portion of the world's daily caloric intake, serving as a vital source of carbohydrates and dietary fiber for billions of people. Seed shape studies of wheat typically involve the use of digital image analysis software to quantify various seed shape parameters such as length, width, area, aspect ratio, roundness, and symmetry. This study presents a comprehensive investigation into the water-absorbing capacity of seeds from 120 distinct wheat lines, leveraging digital image analysis techniques facilitated by SmartGrain software. Water absorption is a pivotal process in the early stages of seed germination, directly influencing plant growth and crop yield. SmartGrain, a powerful image analysis tool, was employed to extract precise quantitative data from digital images of wheat seeds, enabling the assessment of various seed traits in relation to their water-absorbing capacity. The analysis revealed significant transformations in seed characteristics as they absorbed water, including changes in size, weight, shape, and more. Through statistical analysis and correlation assessments, we identified robust relationships between these seed traits, both before and after water treatment. Principal Component Analysis (PCA) and Agglomerative Hierarchical Clustering (AHC) were employed to categorize genotypes with similar trait patterns, providing insights valuable for crop breeding and genetic research. Multiple linear regression analysis further elucidated the influence of specific seed traits, such as weight, width, and distance, on water-absorbing capacity. Our study contributes to a deeper understanding of seed development, imbibition, and the crucial role of water absorption in wheat. These insights have practical implications in agriculture, offering opportunities to optimize breeding programs for improved water absorption in wheat genotypes. The integration of SmartGrain software with advanced statistical methods enhances the reliability and significance of our findings, paving the way for more efficient and resilient wheat crop production. Significant changes in wheat seed shape parameters were observed after imbibition, with notable increases in area, perimeter, length, width, and weight. The length-to-width ratio (LWR) and circularity displayed opposite trends, with higher values before imbibition and lower values after imbibition.

DLGAN: Undersampled MRI reconstruction using Deep Learning based Generative Adversarial Network

Magnetic Resonance Imaging (MRI) is a crucial tool for quantitative image analysis and clinical diagnosis, providing detailed anatomical images to assist in the detection of various abnormalities. However, the widespread use of MRI is hindered by the challenges associated with long sampling periods and low-resolution image processing, prompting the development of various traditional methods to address these limitations. In recent times, advanced Deep Learning (DL) techniques have been applied to tackle the inverse problem of reconstructing MRI images from undersampled data. These DL models have demonstrated substantial improvements in terms of image reconstruction performance, cost-effectiveness, and reduced acquisition time, offering significant potential for further enhancements in this domain. This study introduces a novel DLGAN (Deep Learning Generative Adversarial Network) model comprising two sub-GAN modules tailored for specific datasets. Each module incorporates DLGAN (Generator Block, GB) and DLGAN (Discriminator Block, DB) blocks, strategically designed to regenerate MR images from K Space data. These blocks effectively leverage information from both the ground truth and KSpace characteristics, resulting in enhanced image reconstruction performance while also reducing complexity and improving overall efficiency. The DLGAN model effectively addresses common issues such as artefact removal and vanishing-gradient problems through the extraction of hierarchical features. To evaluate the model’s effectiveness, comprehensive experiments were conducted, utilizing metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The experimental findings demonstrate that the proposed DLGAN model outperforms the most recent designs in MRI image reconstruction, showcasing its potential for significant advancements in clinical diagnosis and quantitative image analysis.

Report on the Marine Imaging Workshop 2022

Imaging is increasingly used to capture information on the marine environment thanks to the improvements in imaging equipment, devices for carrying cameras and data storage in recent years. In that context, biologists, geologists, computer specialists and end-users must gather to discuss the methods and procedures for optimising the quality and quantity of data collected from images. The 4th Marine Imaging Workshop was organised from 3-6 October 2022 in Brest (France) in a hybrid mode. More than a hundred participants were welcomed in person and about 80 people attended the online sessions. The workshop was organised in a single plenary session of presentations followed by discussion sessions. These were based on dynamic polls and open questions that allowed recording of the imaging community’s current and future ideas. In addition, a whole day was dedicated to practical sessions on image analysis, data standardisation and communication tools. The format of this edition allowed the participation of a wider community, including lower-income countries, early career scientists, all working on laboratory, benthic and pelagic imaging. This article summarises the topics addressed during the workshop, particularly the outcomes of the discussion sessions for future reference and to make the workshop results available to the open public.

Spatial Pattern Analysis using Closest Events (SPACE)-A Nearest Neighbor Point Pattern Analysis Framework for Assessing Spatial Relationships from Digital Images.

The quantitative description of biological structures is a valuable yet difficult task in the life sciences. This is commonly accomplished by imaging samples using fluorescence microscopy and analyzing resulting images using Pearson's correlation or Manders' co-occurrence intensity-based colocalization paradigms. Though conceptually and computationally simple, these approaches are critically flawed due to their reliance on signal overlap, sensitivity to cursory signal qualities, and inability to differentiate true and incidental colocalization. Point pattern analysis provides a framework for quantitative characterization of spatial relationships between spatial patterns using the distances between observations rather than their overlap, thus overcoming these issues. Here we introduce an image analysis tool called Spatial Pattern Analysis using Closest Events (SPACE) that leverages nearest neighbor-based point pattern analysis to characterize the spatial relationship of fluorescence microscopy signals from image data. The utility of SPACE is demonstrated by assessing the spatial association between mRNA and cell nuclei from confocal images of cardiac myocytes. Additionally, we use synthetic and empirical images to characterize the sensitivity of SPACE to image segmentation parameters and cursory image qualities such as signal abundance and image resolution. Ultimately, SPACE delivers performance superior to traditional colocalization methods and offers a valuable addition to the microscopist's toolbox.

AraDQ: an automated digital phenotyping software for quantifying disease symptoms of flood-inoculated Arabidopsis seedlings

BackgroundPlant scientists have largely relied on pathogen growth assays and/or transcript analysis of stress-responsive genes for quantification of disease severity and susceptibility. These methods are destructive to plants, labor-intensive, and time-consuming, thereby limiting their application in real-time, large-scale studies. Image-based plant phenotyping is an alternative approach that enables automated measurement of various symptoms. However, most of the currently available plant image analysis tools require specific hardware platform and vendor specific software packages, and thus, are not suited for researchers who are not primarily focused on plant phenotyping. In this study, we aimed to develop a digital phenotyping tool to enhance the speed, accuracy, and reliability of disease quantification in Arabidopsis.ResultsHere, we present the Arabidopsis Disease Quantification (AraDQ) image analysis tool for examination of flood-inoculated Arabidopsis seedlings grown on plates containing plant growth media. It is a cross-platform application program with a user-friendly graphical interface that contains highly accurate deep neural networks for object detection and segmentation. The only prerequisite is that the input image should contain a fixed-sized 24-color balance card placed next to the objects of interest on a white background to ensure reliable and reproducible results, regardless of the image acquisition method. The image processing pipeline automatically calculates 10 different colors and morphological parameters for individual seedlings in the given image, and disease-associated phenotypic changes can be easily assessed by comparing plant images captured before and after infection. We conducted two case studies involving bacterial and plant mutants with reduced virulence and disease resistance capabilities, respectively, and thereby demonstrated that AraDQ can capture subtle changes in plant color and morphology with a high level of sensitivity.ConclusionsAraDQ offers a simple, fast, and accurate approach for image-based quantification of plant disease symptoms using various parameters. Its fully automated pipeline neither requires prior image processing nor costly hardware setups, allowing easy implementation of the software by researchers interested in digital phenotyping of diseased plants.

Automated workflows using Quantitative Colour Pattern Analysis (QCPA): a guide to batch processing and downstream data analysis

Animal and plant colouration presents a striking dimension of phenotypic variation, the study of which has driven general advances in ecology, evolution, and animal behaviour. Quantitative Colour Pattern Analysis (QCPA) is a dynamic framework for analysing colour patterns through the eyes of non-human observers. However, its extensive array of user-defined image processing and analysis tools means image analysis is often time-consuming. This hinders the full use of analytical power provided by QCPA and its application to large datasets. Here, we offer a robust and comprehensive batch script, allowing users to automate many QCPA workflows. We also provide a complimentary set of useful R scripts for downstream data extraction and analysis. The presented batch processing extension will empower users to further utilise the analytical power of QCPA and facilitate the development of customised semi-automated workflows. Such quantitatively scaled workflows are crucial for exploring colour pattern spaces and developing ever-richer frameworks for analysing organismal colouration accounting for visual perception in animals other than humans. These advances will, in turn, facilitate testing hypotheses on the function and evolution of vision and signals at quantitative and qualitative scales, which are otherwise computationally unfeasible.

Deep learning-based predictive classification of functional subpopulations of hematopoietic stem cells and multipotent progenitors.

Hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) play a pivotal role in maintaining lifelong hematopoiesis. The distinction between stem cells and other progenitors, as well as the assessment of their functions, has long been a central focus in stem cell research. In recent years, deep learning has emerged as a powerful tool for cell image analysis and classification/prediction. In this study, we explored the feasibility of employing deep learning techniques to differentiate murine HSCs and MPPs based solely on their morphology, as observed through light microscopy (DIC) images. After rigorous training and validation using extensive image datasets, we successfully developed a three-class classifier, referred to as the LSM model, capable of reliably distinguishing long-term HSCs, short-term HSCs, and MPPs. The LSM model extracts intrinsic morphological features unique to different cell types, irrespective of the methods used for cell identification and isolation, such as surface markers or intracellular GFP markers. Furthermore, employing the same deep learning framework, we created a two-class classifier that effectively discriminates between aged HSCs and young HSCs. This discovery is particularly significant as both cell types share identical surface markers yet serve distinct functions. This classifier holds the potential to offer a novel, rapid, and efficient means of assessing the functional states of HSCs, thus obviating the need for time-consuming transplantation experiments. Our study represents the pioneering use of deep learning to differentiate HSCs and MPPs under steady-state conditions. This novel and robust deep learning-based platform will provide a basis for the future development of a new generation stem cell identification and separation system. It may also provide new insight into the molecular mechanisms underlying stem cell self-renewal.

Towards standardising retinal OCT angiography image analysis with open-source toolbox OCTAVA

Quantitative assessment of retinal microvasculature in optical coherence tomography angiography (OCTA) images is important for studying, diagnosing, monitoring, and guiding the treatment of ocular and systemic diseases. However, the OCTA user community lacks universal and transparent image analysis tools that can be applied to images from a range of OCTA instruments and provide reliable and consistent microvascular metrics from diverse datasets. We present a retinal extension to the OCTA Vascular Analyser (OCTAVA) that addresses the challenges of providing robust, easy-to-use, and transparent analysis of retinal OCTA images. OCTAVA is a user-friendly, open-source toolbox that can analyse retinal OCTA images from various instruments. The toolbox delivers seven microvascular metrics for the whole image or subregions and six metrics characterising the foveal avascular zone. We validate OCTAVA using images collected by four commercial OCTA instruments demonstrating robust performance across datasets from different instruments acquired at different sites from different study cohorts. We show that OCTAVA delivers values for retinal microvascular metrics comparable to the literature and reduces their variation between studies compared to their commercial equivalents. By making OCTAVA publicly available, we aim to expand standardised research and thereby improve the reproducibility of quantitative analysis of retinal microvascular imaging. Such improvements will help to better identify more reliable and sensitive biomarkers of ocular and systemic diseases.