Biomedical Image Data Research Articles

An Algorithmic and Software Framework to Incorporate Orientation Distribution Functions in Finite Element Simulations for Biomechanics and Biophysics

Biological tissues and biomaterials routinely feature a fibrous microstructure that contributes to physical and mechanical properties while influencing cellular guidance, organization and extracellular matrix (ECM) production. Specialized three-dimensional (3D) imaging techniques can visualize fibrillar structure and orientation, and previously we developed a nonparametric approach to extract orientation distribution functions (ODFs) directly from 3D image data [1]. In this work, we expanded our previous approach to provide a complete algorithmic and software framework to characterize inhomogeneous ODFs in image data and use ODFs to model the physics of materials with the finite element method. We characterized inhomogeneity using image subdomains and specialized interpolation methods, and we developed methods to incorporate ODFs directly into constitutive models. To facilitate its adoption by the biomechanics and biophysics communities, we developed a unified software framework in FEBio Studio (www.febio.org). This included new interpolation methods to spatially map the ODFs onto finite element meshes and an approach to downsample ODFs for efficient numerical calculations. The software provides the option to fit ODFs to parametric distributions, and scalar metrics provide means to assess goodness of fit. We evaluated the utility and accuracy of the algorithms and implementation using representative 3D image datasets. Our results demonstrated that utilizing the true measured ODFs provide a more accurate and spatially resolved representation of fiber ODFs and the resulting predicted mechanical response when compared with parametric approaches to approximating the true ODFs. This research provides a powerful, interactive software framework to extract and represent the inhomogeneous anisotropic characteristics of fibrous tissues directly from image data, and to incorporate them into biomechanics and biophysics simulations using the finite element method. Statement of SignificanceBiological tissues and biomaterials routinely feature a fibrous microstructure that contributes to physical and mechanical properties while influencing cellular guidance, organization and extracellular matrix (ECM) production. In this study, we developed a complete algorithmic and software framework to characterize inhomogeneous orientation distribution functions (ODFs) directly from biomedical image data and apply the ODFs to model the physics of biological materials. We characterized inhomogeneity using image subdomains and specialized interpolation methods, and we developed methods to incorporate ODFs directly into constitutive models. We developed a unified software framework in FEBio Studio (www.febio.org) to accommodate its adoption by the biomechanics and biophysics communities. The result is a powerful, interactive software framework to extract and represent inhomogeneous, anisotropic characteristics directly from image data, and incorporate them into biomechanics and biophysics simulations.

Computational Methods for Image Analysis in Craniofacial Development and Disease.

Observation is at the center of all biological sciences. Advances in imaging technologies are therefore essential to derive novel biological insights to better understand the complex workings of living systems. Recent high-throughput sequencing and imaging techniques are allowing researchers to simultaneously address complex molecular variations spatially and temporarily in tissues and organs. The availability of increasingly large dataset sizes has allowed for the evolution of robust deep learning models, designed to interrogate biomedical imaging data. These models are emerging as transformative tools in diagnostic medicine. Combined, these advances allow for dynamic, quantitative, and predictive observations of entire organisms and tissues. Here, we address 3 main tasks of bioimage analysis, image restoration, segmentation, and tracking and discuss new computational tools allowing for 3-dimensional spatial genomics maps. Finally, we demonstrate how these advances have been applied in studies of craniofacial development and oral disease pathogenesis.

Semantic redundancy-aware implicit neural compression for multidimensional biomedical image data

The surge in advanced imaging techniques has generated vast biomedical image data with diverse dimensions in space, time and spectrum, posing big challenges to conventional compression techniques in image storage, transmission, and sharing. Here, we propose an intelligent image compression approach with the first-proved semantic redundancy of biomedical data in the implicit neural function domain. This Semantic redundancy based Implicit Neural Compression guided with Saliency map (SINCS) can notably improve the compression efficiency for arbitrary-dimensional image data in terms of compression ratio and fidelity. Moreover, with weight transfer and residual entropy coding strategies, it shows improved compression speed while maintaining high quality. SINCS yields high quality compression with over 2000-fold compression ratio on 2D, 2D-T, 3D, 4D biomedical images of diverse targets ranging from single virus to entire human organs, and ensures reliable downstream tasks, such as object segmentation and quantitative analyses, to be conducted at high efficiency.

Identifying and training deep learning neural networks on biomedical-related datasets.

This manuscript describes the development of a resources module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on implementing deep learning algorithms for biomedical image data in an interactive format that uses appropriate cloud resources for data access and analyses. Biomedical-related datasets are widely used in both research and clinical settings, but the ability for professionally trained clinicians and researchers to interpret datasets becomes difficult as the size and breadth of these datasets increases. Artificial intelligence, and specifically deep learning neural networks, have recently become an important tool in novel biomedical research. However, use is limited due to their computational requirements and confusion regarding different neural network architectures. The goal of this learning module is to introduce types of deep learning neural networks and cover practices that are commonly used in biomedical research. This module is subdivided into four submodules that cover classification, augmentation, segmentation and regression. Each complementary submodule was written on the Google Cloud Platform and contains detailed code and explanations, as well as quizzes and challenges to facilitate user training. Overall, the goal of this learning module is to enable users to identify and integrate the correct type of neural network with their data while highlighting the ease-of-use of cloud computing for implementing neural networks. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Psudo: Exploring Multi-Channel Biomedical Image Data with Spatially and Perceptually Optimized Pseudocoloring.

Over the past century, multichannel fluorescence imaging has been pivotal in myriad scientific breakthroughs by enabling the spatial visualization of proteins within a biological sample. With the shift to digital methods and visualization software, experts can now flexibly pseudocolor and combine image channels, each corresponding to a different protein, to explore their spatial relationships. We thus propose psudo, an interactive system that allows users to create optimal color palettes for multichannel spatial data. In psudo, a novel optimization method generates palettes that maximize the perceptual differences between channels while mitigating confusing color blending in overlapping channels. We integrate this method into a system that allows users to explore multi-channel image data and compare and evaluate color palettes for their data. An interactive lensing approach provides on-demand feedback on channel overlap and a color confusion metric while giving context to the underlying channel values. Color palettes can be applied globally or, using the lens, to local regions of interest. We evaluate our palette optimization approach using three graphical perception tasks in a crowdsourced user study with 150 participants, showing that users are more accurate at discerning and comparing the underlying data using our approach. Additionally, we showcase psudo in a case study exploring the complex immune responses in cancer tissue data with a biologist.

Increasing the reliability of diagnosis of diabetic retinopathy based on machine learning

This paper discusses the method of measuring and analyzing the parameters of the retina with subsequent diagnosis based on them of pathological changes due to diabetic retinopathy, which is crucial in the field of medicine to help doctors in timely detection and treatment of the disease. The main problem of biomedical image data analysis is insufficient pre-processing of images for further clear determination of informative indicators. This paper explores the application of machine learning and image processing techniques to develop an effective method for the diagnosis of diabetic retinopathy. The main focus is on obtaining the optimal model using machine learning and different types of neural networks. This paper considered and analyzed such methods of image preprocessing as: median filtering, grayscale conversion, cropping of non-informative areas of the image, selection of contours. The classification results of three rules (Classical Neural Networks (CNNs), Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) were analyzed, and through experimental studies it was determined that the ANN performed the task best (accuracy=87.1 %, reliability=84.6 %, sensitivity=91.6 %, specificity=84 %). An information model was obtained to support decision-making in assessing the condition of the retina using the processing of the obtained microscopic images and further analysis of informative parameters, and a database of more than 35,000 samples and informative features of the retina was formed. Given the sufficient quality of classification and the availability of software and hardware, this method can be developed and applied in practice in medical institutions after conducting all the necessary clinical studies

SST-editing: in silico spatial transcriptomic editing at single-cell resolution.

Generative Adversarial Nets (GAN) achieve impressive performance for text-guided editing of natural images. However, a comparable utility of GAN remains understudied for spatial transcriptomics (ST) technologies with matched gene expression and biomedical image data. We propose In Silico Spatial Transcriptomic editing that enables gene expression-guided editing of immunofluorescence images. Using cell-level spatial transcriptomics data extracted from normal and tumor tissue slides, we train the approach under the framework of GAN (Inversion). To simulate cellular state transitions, we then feed edited gene expression levels to trained models. Compared to normal cellular images (ground truth), we successfully model the transition from tumor to normal tissue samples, as measured with quantifiable and interpretable cellular features. https://github.com/CTPLab/SST-editing.

A Computational Deep Fuzzy Network-Based Neuroimaging Analysis for Brain Hemorrhage Classification.

The boundaries and regions between individual classes in biomedical image classification are hazy and overlapping. These overlapping features make predicting the correct classification result for biomedical imaging data a difficult diagnostic task. Thus, in precise classification, it is frequently necessary to obtain all necessary information before making a decision. This paper presents a novel deep-layered design architecture based on Neuro-Fuzzy-Rough intuition to predict hemorrhages using fractured bone images and head CT scans. To deal with data uncertainty, the proposed architecture design employs a parallel pipeline with rough-fuzzy layers. In this case, the rough-fuzzy function functions as a membership function, incorporating the ability to process rough-fuzzy uncertainty information. It not only improves the deep model's overall learning process, but it also reduces feature dimensions. The proposed architecture design improves the model's learning and self-adaptation capabilities. In experiments, the proposed model performed well, with training and testing accuracies of 96.77% and 94.52%, respectively, in detecting hemorrhages using fractured head images. The comparative analysis shows that the model outperforms existing models by an average of 2.6 ±0.90% on various performance metrics.

The Application of Artificial Intelligence to The Bayesian Model Algorithm for Combining Genome Data

The combination of bioinformatics and artificial intelligence (AI) has made significant progress in the current phase. AI technology, especially deep learning, has been widely used in biology, resulting in many innovations. Currently, AI plays a key role in genomics, proteomics, and drug discovery. Deep learning models are used to predict protein structures, discover potential drug compounds, interpret genomic sequences, analyze medical images, and make personalized medical recommendations. In addition, AI can also help accelerate the processing and interpretation of biological big data, helping biologists to understand complex problems in the life sciences more deeply. Compared to traditional genetic data analysis methods, AI combined with bioinformatics methods are often faster and more accurate, and are capable of processing large-scale, high-dimensional biological data, opening up unprecedented opportunities for life science research. In this paper, the whole genome data and biomedical imaging data are used from the perspective of Bayesian hypothesis testing. Genome-wide association analysis, led by large-scale multiple tests, is a very popular tool for identifying genetic variation points in new complex diseases. In genome-wide association analysis, tens of thousands of SNPS need to be tested simultaneously to find out some SNPS related to traits. These tests are related due to factors such as linkage imbalances in the genetic process, and the test questions are set against the background of high-dimensional data (p >=n).

OBIA: An Open Biomedical Imaging Archive

With the development of artificial intelligence (AI) technologies, biomedical imaging data play an important role in scientific research and clinical application, but the available resources are limited. Here we present Open Biomedical Imaging Archive (OBIA), a repository for archiving biomedical imaging and related clinical data. OBIA adopts five data objects (Collection, Individual, Study, Series, and Image) for data organization, and accepts the submission of biomedical images of multiple modalities, organs, and diseases. In order to protect personal privacy, OBIA has formulated a unified de-identification and quality control process. In addition, OBIA provides friendly and intuitive web interfaces for data submission, browsing, and retrieval, as well as image retrieval. As of September 2023, OBIA has housed data for a total of 937 individuals, 4136 studies, 24,701 series, and 1,938,309 images covering 9 modalities and 30 anatomical sites. Collectively, OBIA provides a reliable platform for biomedical imaging data management and offers free open access to all publicly available data to support research activities throughout the world. OBIA can be accessed at https://ngdc.cncb.ac.cn/obia.

Enhancing thoracic disease detection using chest X-rays from PubMed Central Open Access

Large chest X-rays (CXR) datasets have been collected to train deep learning models to detect thorax pathology on CXR. However, most CXR datasets are from single-center studies and the collected pathologies are often imbalanced. The aim of this study was to automatically construct a public, weakly-labeled CXR database from articles in PubMed Central Open Access (PMC-OA) and to assess model performance on CXR pathology classification by using this database as additional training data. Our framework includes text extraction, CXR pathology verification, subfigure separation, and image modality classification. We have extensively validated the utility of the automatically generated image database on thoracic disease detection tasks, including Hernia, Lung Lesion, Pneumonia, and pneumothorax. We pick these diseases due to their historically poor performance in existing datasets: the NIH-CXR dataset (112,120 CXR) and the MIMIC-CXR dataset (243,324 CXR). We find that classifiers fine-tuned with additional PMC-CXR extracted by the proposed framework consistently and significantly achieved better performance than those without (e.g., Hernia: 0.9335 vs 0.9154; Lung Lesion: 0.7394 vs. 0.7207; Pneumonia: 0.7074 vs. 0.6709; Pneumothorax 0.8185 vs. 0.7517, all in AUC with p< 0.0001) for CXR pathology detection. In contrast to previous approaches that manually submit the medical images to the repository, our framework can automatically collect figures and their accompanied figure legends. Compared to previous studies, the proposed framework improved subfigure segmentation and incorporates our advanced self-developed NLP technique for CXR pathology verification. We hope it complements existing resources and improves our ability to make biomedical image data findable, accessible, interoperable, and reusable.

Wavelet image scattering based glaucoma detection

BackgroundThe ever-growing need for cheap, simple, fast, and accurate healthcare solutions spurred a lot of research activities which are aimed at the reliable deployment of artificial intelligence in the medical fields. However, this has proved to be a daunting task especially when looking to make automated diagnoses using biomedical image data. Biomedical image data have complex patterns which human experts find very hard to comprehend. Against this backdrop, we applied a representation or feature learning algorithm: Invariant Scattering Convolution Network or Wavelet scattering Network to retinal fundus images and studied the the efficacy of the automatically extracted features therefrom for glaucoma diagnosis/detection. The influence of wavelet scattering network parameter settings as well as 2-D channel image type on the detection correctness is also examined. Our work is a distinct departure from the usual method where wavelet transform is applied to pre-processed retinal fundus images and handcrafted features are extracted from the decomposition results. Here, the RIM-ONE DL image dataset was fed into a wavelet scattering network developed in the Matlab environment to achieve a stage-wise decomposition process called wavelet scattering of the retinal fundus images thereby, automatically learning features from the images. These features were then used to build simple and computationally cheap classification algorithms.ResultsMaximum detection correctness of 98% was achieved on the held-out test set. Detection correctness is highly sensitive to scattering network parameter setting and 2-D channel image type.ConclusionA superficial comparison of the classification results obtained from our work and those obtained using a convolutional neural network underscores the potentiality of the proposed method for glaucoma detection.

HWCD: A hybrid approach for image compression using wavelet, encryption using confusion, and decryption using diffusion scheme

Abstract Image data play important role in various real-time online and offline applications. Biomedical field has adopted the imaging system to detect, diagnose, and prevent several types of diseases and abnormalities. The biomedical imaging data contain huge information which requires huge storage space. Moreover, currently telemedicine and IoT based remote health monitoring systems are widely developed where data is transmitted from one place to another. Transmission of this type of huge data consumes more bandwidth. Along with this, during this transmission, the attackers can attack the communication channel and obtain the important and secret information. Hence, biomedical image compression and encryption are considered the solution to deal with these issues. Several techniques have been presented but achieving desired performance for combined module is a challenging task. Hence, in this work, a novel combined approach for image compression and encryption is developed. First, image compression scheme using wavelet transform is presented and later a cryptography scheme is presented using confusion and diffusion schemes. The outcome of the proposed approach is compared with various existing techniques. The experimental analysis shows that the proposed approach achieves better performance in terms of autocorrelation, histogram, information entropy, PSNR, MSE, and SSIM.

Smartphone-microfluidic fluorescence imaging system for studying islet physiology.

Smartphone technology has been recently applied for biomedical image acquisition and data analysis due to its high-quality imaging capability, and flexibility to customize multi-purpose apps. In this work, we developed and characterized a smartphone-microfluidic fluorescence imaging system for studying the physiology of pancreatic islets. We further evaluated the system capability by performing real-time fluorescence imaging on mouse islets labeled with either chemical fluorescence dyes or genetically encoded fluorescent protein indicators (GEFPIs). Our results showed that the system was capable of analyzing key beta-cell insulin stimulator-release coupling factors in response to various stimuli with high-resolution dynamics. Furthermore, the integration of a microfluidics allowed high-resolution detection of insulin secretion at single islet level. When compared to conventional fluorescence microscopes and macro islet perifusion apparatus, the system has the advantages of low cost, portable, and easy to operate. With all of these features, we envision that this smartphone-microfluidic fluorescence imaging system can be applied to study islet physiology and clinical applications.

Unsupervised segmentation of biomedical hyperspectral image data: tackling high dimensionality with convolutional autoencoders.

Information about the structure and composition of biopsy specimens can assist in disease monitoring and diagnosis. In principle, this can be acquired from Raman and infrared (IR) hyperspectral images (HSIs) that encode information about how a sample's constituent molecules are arranged in space. Each tissue section/component is defined by a unique combination of spatial and spectral features, but given the high dimensionality of HSI datasets, extracting and utilising them to segment images is non-trivial. Here, we show how networks based on deep convolutional autoencoders (CAEs) can perform this task in an end-to-end fashion by first detecting and compressing relevant features from patches of the HSI into low-dimensional latent vectors, and then performing a clustering step that groups patches containing similar spatio-spectral features together. We showcase the advantages of using this end-to-end spatio-spectral segmentation approach compared to i) the same spatio-spectral technique not trained in an end-to-end manner, and ii) a method that only utilises spectral features (spectral k-means) using simulated HSIs of porcine tissue as test examples. Secondly, we describe the potential advantages/limitations of using three different CAE architectures: a generic 2D CAE, a generic 3D CAE, and a 2D convolutional encoder-decoder architecture inspired by the recently proposed UwU-net that is specialised for extracting features from HSI data. We assess their performance on IR HSIs of real colon samples. We find that all architectures are capable of producing segmentations that show good correspondence with HE stained adjacent tissue slices used as approximate ground truths, indicating the robustness of the CAE-driven spatio-spectral clustering approach for segmenting biomedical HSI data. Additionally, we stress the need for more accurate ground truth information to enable a precise comparison of the advantages offered by each architecture.

EmbedSeg: Embedding-based Instance Segmentation for Biomedical Microscopy Data.

Automatic detection and segmentation of biological objects in 2D and 3D image data is central for countless biomedical research questions to be answered. While many existing computational methods are used to reduce manual labeling time, there is still a huge demand for further quality improvements of automated solutions. In the natural image domain, spatial embedding-based instance segmentation methods are known to yield high-quality results, but their utility to biomedical data is largely unexplored. Here we introduce EmbedSeg, an embedding-based instance segmentation method designed to segment instances of desired objects visible in 2D or 3D biomedical image data. We apply our method to four 2D and seven 3D benchmark datasets, showing that we either match or outperform existing state-of-the-art methods. While the 2D datasets and three of the 3D datasets are well known, we have created the required training data for four new 3D datasets, which we make publicly available online. Next to performance, also usability is important for a method to be useful. Hence, EmbedSeg is fully open source (https://github.com/juglab/EmbedSeg), offering (i)tutorial notebooks to train EmbedSeg models and use them to segment object instances in new data, and (ii)a napari plugin that can also be used for training and segmentation without requiring any programming experience. We believe that this renders EmbedSeg accessible to virtually everyone who requires high-quality instance segmentations in 2D or 3D biomedical image data.

Designing optimal convolutional neural network architecture using differential evolution algorithm

SummaryConvolutional neural networks (CNNs) are deep learning models used widely for solving various tasks like computer vision and speech recognition. CNNs are developed manually based on problem-specific domain knowledge and tricky settings, which are laborious, time consuming, and challenging. To solve these, our study develops an improved differential evolution of convolutional neural network (IDECNN) algorithm to design CNN layer architectures for image classification. Variable-length encoding is utilized to represent the flexible layer architecture of a CNN model in IDECNN. An efficient heuristic mechanism is proposed in IDECNN to evolve CNN architecture through mutation and crossover to prevent premature convergence during the evolutionary process. Eight well-known imaging datasets were utilized. The results showed that IDECNN could design suitable architecture compared with 20 existing CNN models. Finally, CNN architectures are applied to pneumonia and coronavirus disease 2019 (COVID-19) X-ray biomedical image data. The results demonstrated the usefulness of the proposed approach to generate a suitable CNN model.

Editorial: Biomedical image or genomic data characterization and radiogenomic/image-omics

Editorial: Biomedical image or genomic data characterization and radiogenomic/image-omics

Bitbox: A Cloud-based data sharing solution for medical images.

With the modernization and digitisation of the healthcare system, the need for exchanging medical data has become increasingly compelling. Biomedical imaging has been no exception, where the gathering of medical imaging acquisitions from multi-site collaborations have enabled to reach data sizes never imaginable until few years ago. Usually, medical imaging data have very large volume and diverse complexity, requiring bespoken transfer systems that protect personal information as well as data integrity. Despite many digital innovations, there are still technical and regulatory bottlenecks that make biomedical imaging data exchange challenging. Here we present Bitbox, a web-based application which provides a reliable yet straightforward service to securely exchange medical imaging data. With Bitbox, both imaging and non-imaging data of any type can be transferred from any external and independent site into a centralized server. A showcase of the system will be illustrated for the COVID-19 Clinical Neuroscience Study (COVID-CNS) project, a UK-wide experimental medicine study to investigate the neurological and neuropsychiatric effects of COVID-19 infections in hundreds of patients.

Biomedical Research and Informatics Living Laboratory for Innovative Advances of New Technologies in Community Mobility Rehabilitation: Protocol for Evaluation and Rehabilitation of Mobility Across Continuums of Care.

BackgroundRapid advances in technologies over the past 10 years have enabled large-scale biomedical and psychosocial rehabilitation research to improve the function and social integration of persons with physical impairments across the lifespan. The Biomedical Research and Informatics Living Laboratory for Innovative Advances of New Technologies (BRILLIANT) in community mobility rehabilitation aims to generate evidence-based research to improve rehabilitation for individuals with acquired brain injury (ABI).ObjectiveThis study aims to (1) identify the factors limiting or enhancing mobility in real-world community environments (public spaces, including the mall, home, and outdoors) and understand their complex interplay in individuals of all ages with ABI and (2) customize community environment mobility training by identifying, on a continuous basis, the specific rehabilitation strategies and interventions that patient subgroups benefit from most. Here, we present the research and technology plan for the BRILLIANT initiative.MethodsA cohort of individuals, adults and children, with ABI (N=1500) will be recruited. Patients will be recruited from the acute care and rehabilitation partner centers within 4 health regions (living labs) and followed throughout the continuum of rehabilitation. Participants will also be recruited from the community. Biomedical, clinician-reported, patient-reported, and brain imaging data will be collected. Theme 1 will implement and evaluate the feasibility of collecting data across BRILLIANT living labs and conduct predictive analyses and artificial intelligence (AI) to identify mobility subgroups. Theme 2 will implement, evaluate, and identify community mobility interventions that optimize outcomes for mobility subgroups of patients with ABI.ResultsThe biomedical infrastructure and equipment have been established across the living labs, and development of the clinician- and patient-reported outcome digital solutions is underway. Recruitment is expected to begin in May 2022.ConclusionsThe program will develop and deploy a comprehensive clinical and community-based mobility-monitoring system to evaluate the factors that result in poor mobility, and develop personalized mobility interventions that are optimized for specific patient subgroups. Technology solutions will be designed to support clinicians and patients to deliver cost-effective care and the right intervention to the right person at the right time to optimize long-term functional potential and meaningful participation in the community.International Registered Report Identifier (IRRID)PRR1-10.2196/12506