Atomic Force Microscopy in the Characterization of the Structure of Cell Wall Components.

Atomic force microscopy (AFM) has broken boundaries in the characterization of the supramolecular architecture of cell wall assemblies and single cell wall polysaccharides at the nanoscale level. Moreover, AFM provides an opportunity to evaluate the mechanical properties of cell wall material which is not possible with any other method. However, in the case of plant tissue, the critical step is a smart sample preparation that should not affect the polysaccharide structure or assembly and on the other hand should consider device limitations, especially scanner ranges. In this chapter, the protocols from the sample preparation, including isolation of cell wall material and extraction of cell wall polysaccharide fractions, through AFM imaging of polysaccharide assemblies and single molecules until an image analysis to obtain quantitative data characterizing the biopolymers are presented.

Cellular data extraction from multiplexed brain imaging data using self-supervised Dual-loss Adaptive Masked Autoencoder.

Reliable large-scale cell detection and segmentation is the fundamental first step to understanding biological processes in the brain. The ability to phenotype cells at scale can accelerate preclinical drug evaluation and system-level brain histology studies. The impressive advances in deep learning offer a practical solution to cell image detection and segmentation. Unfortunately, categorizing cells and delineating their boundaries for training deep networks is an expensive process that requires skilled biologists. This paper presents a novel self-supervised Dual-Loss Adaptive Masked Autoencoder (DAMA) for learning rich features from multiplexed immunofluorescence brain images. DAMA's objective function minimizes the conditional entropy in pixel-level reconstruction and feature-level regression. Unlike existing self-supervised learning methods based on a random image masking strategy, DAMA employs a novel adaptive mask sampling strategy to maximize mutual information and effectively learn brain cell data. To the best of our knowledge, this is the first effort to develop a self-supervised learning method for multiplexed immunofluorescence brain images. Our extensive experiments demonstrate that DAMA features enable superior cell detection, segmentation, and classification performance without requiring many annotations. In addition, to examine the generalizability of DAMA, we also experimented on TissueNet, a multiplexed imaging dataset comprised of two-channel fluorescence images from six distinct tissue types, captured using six different imaging platforms. Our code is publicly available at https://github.com/hula-ai/DAMA.

Tools and methods for high-throughput single-cell imaging with the mother machine.

Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning-based segmentation, 'what you put is what you get' (WYPIWYG) - that is, pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother machine-based high-throughput imaging and analysis methods in their research.

New Methods in Digital Wood Anatomy: The Use of Pixel-Contrast Densitometry with Example of Angiosperm Shrubs in Southern Siberia.

This methodological study describes the adaptation of a new method in digital wood anatomy, pixel-contrast densitometry, for angiosperm species. The new method was tested on eight species of shrubs and small trees in Southern Siberia, whose wood structure varies from ring-porous to diffuse-porous, with different spatial organizations of vessels. A two-step transformation of wood cross-section photographs by smoothing and Otsu's classification algorithm was proposed to separate images into cell wall areas and empty spaces within (lumen) and between cells. Good synchronicity between measurements within the ring allowed us to create profiles of wood porosity (proportion of empty spaces) describing the growth ring structure and capturing inter-annual differences between rings. For longer-lived species, 14-32-year series from at least ten specimens were measured. Their analysis revealed that maximum (for all wood types), mean, and minimum porosity (for diffuse-porous wood) in the ring have common external signals, mostly independent of ring width, i.e., they can be used as ecological indicators. Further research directions include a comparison of this method with other approaches in densitometry, clarification of sample processing, and the extraction of ecologically meaningful data from wood structures.

Quantifying Abdominal Coloration of Worker Honey Bees.

The main drawback in using coloration to identify honey bee subspecies is the lack of knowledge regarding genetic background, subjectivity of coloration grading, and the effect of the environment. The aim of our study was to evaluate the effect of environmental temperature on the abdominal coloration of honey bee workers and to develop a tool for quantifying abdominal coloration. We obtained four frames of honey bee brood from two colonies and incubated them at two different temperatures (30 and 34 °C). One colony had workers exhibiting yellow marks on the abdomen, while the other did not. We collected hatched workers and photographed abdomens. Images were analyzed using custom-written R script to obtain vectors that summarize the coloration over the abdomen length in a single value-coloration index. We used UMAP to reduce the dimensions of the vectors and to develop a classification procedure with the support vector machine method. We tested the effect of brood origin and temperature on coloration index with ANOVA. UMAP did not distinguish individual abdomens according to experimental group. The trained classifier sufficiently separated abdomens incubated at different temperatures. We improved the performance by preprocessing data with UMAP. The differences among the mean coloration index values were not significant between the gray groups incubated at different temperatures nor between the yellow groups. However, the differences between the gray and yellow groups were significant, permitting options for application of our tool and the newly developed coloration index. Our results indicate that the environmental temperature in the selected range during development does not seem to impact honey bee coloration significantly. The developed color-recording protocol and statistical analysis provide useful tools for quantifying abdominal coloration in honey bees.

Development and Implementation of an Innovative Framework for Automated Radiomics Analysis in Neuroimaging.

Radiomics represents an innovative approach to medical image analysis, enabling comprehensive quantitative evaluation of radiological images through advanced image processing and Machine or Deep Learning algorithms. This technique uncovers intricate data patterns beyond human visual detection. Traditionally, executing a radiomic pipeline involves multiple standardized phases across several software platforms. This could represent a limit that was overcome thanks to the development of the matRadiomics application. MatRadiomics, a freely available, IBSI-compliant tool, features its intuitive Graphical User Interface (GUI), facilitating the entire radiomics workflow from DICOM image importation to segmentation, feature selection and extraction, and Machine Learning model construction. In this project, an extension of matRadiomics was developed to support the importation of brain MRI images and segmentations in NIfTI format, thus extending its applicability to neuroimaging. This enhancement allows for the seamless execution of radiomic pipelines within matRadiomics, offering substantial advantages to the realm of neuroimaging.

Time-course analysis system for leaf feeding marks revealed effect of Arabidopsis thaliana trichomes on herbivore insect feeding behavior.

Bioassay with insect herbivore is a common approach to studying plant defense levels. While measuring insect growth rate as a negative indicator of plant defense levels is simple and straightforward, analyzing more detailed feeding behavior parameters of insects, such as feeding rates, leaf area consumed per feeding event, intervals between feeding events, and spatiotemporal patterns of feeding sites on leaves, is more informative. However, such observations are generally time consuming and labor-intensive. Here, we provide a semi-automated system for quantifying feeding behavior parameters of insects feeding on plant leaves. Automated photo scanners record time-course development of feeding marks on leaves. An image analysis pipeline processes the scanned images and extracts leaf area. By analyzing changes in leaf area over time, it detects insect feeding events and calculates the leaf area consumed during each feeding event, providing quantitative parameters of insects feeding behavior. In addition, it visualizes spatio-temporal changes in feeding sites, providing a measure of the complex behavior of insects on leaves. Using this analysis pipeline, we demonstrate that Arabidopsis thaliana trichomes reduce insect feeding rate, but not feeding duration or intervals between feeding events. Our image acquisition system requires only photo a scanner and a laptop computer and does not require any specialized equipment. The analysis software pipeline is provided as an ImageJ macro and R package and is available at no cost. Taken together, our work provides a scalable method for quantitative assessment of insect feeding behavior on leaves, facilitating understanding of plant defense mechanisms.

Sub-features orthogonal decoupling: Detecting bone wall absence via a small number of abnormal examples for temporal CT images.

The absence of bone wall located in the jugular bulb and sigmoid sinus of the temporal bone is one of the important reasons for pulsatile tinnitus. Automatic and accurate detection of these abnormal singes in CT slices has important theoretical significance and clinical value. Due to the shortage of abnormal samples, imbalanced samples, small inter-class differences, and low interpretability, existing deep-learning methods are greatly challenged. In this paper, we proposed a sub-features orthogonal decoupling model, which can effectively disentangle the representation features into class-specific sub-features and class-independent sub-features in a latent space. The former contains the discriminative information, while, the latter preserves information for image reconstruction. In addition, the proposed method can generate image samples using category conversion by combining the different class-specific sub-features and the class-independent sub-features, achieving corresponding mapping between deep features and images of specific classes. The proposed model improves the interpretability of the deep model and provides image synthesis methods for downstream tasks. The effectiveness of the method was verified in the detection of bone wall absence in the temporal bone jugular bulb and sigmoid sinus.

Nextflow pipeline for Visium and H&E data from patient-derived xenograft samples.

We designed a Nextflow DSL2-based pipeline, Spatial Transcriptomics Quantification (STQ), for simultaneous processing of 10x Genomics Visium spatial transcriptomics data and a matched hematoxylin and eosin (H&E)-stained whole-slide image (WSI), optimized for patient-derived xenograft (PDX) cancer specimens. Our pipeline enables the classification of sequenced transcripts for deconvolving the mouse and human species and mapping the transcripts to reference transcriptomes. We align the H&E WSI with the spatial layout of the Visium slide and generate imaging and quantitative morphology features for each Visium spot. The pipeline design enables multiple analysis workflows, including single or dual reference genome input and stand-alone image analysis. We show the utility of our pipeline on a dataset from Visium profiling of four melanoma PDX samples. The clustering of Visium spots and clustering of H&E imaging features reveal similar patterns arising from the two data modalities.

Combined 100 keV Cryo-Electron Microscopy and Image Analysis Methods to Characterize the Wider Adeno-Associated Viral Products.

Adeno-associated viruses (AAVs) are effective vectors for gene therapy. However, AAV drug products are inevitably contaminated with empty particles (EP), which lack a genome, owing to limitations of the purification steps. EP contamination can reduce the transduction efficiency and induce immunogenicity. Therefore, it is important to remove EPs and to determine the ratio of full genome-containing AAV particles to empty particles (F/E ratio). However, most of the existing methods fail to reliably evaluate F/E ratios that are greater than 90 %. In this study, we developed two approaches based on the image analysis of cryo-electron micrographs to determine the F/E ratios of various AAV products. Using our developed convolutional neural network (CNN) and morphological analysis, we successfully calculated the F/E ratios of various AAV products and determined the slight differences in the F/E ratios of highly purified AAV products (purity > 95 %). In addition, the F/E ratios calculated by analyzing more than 1000 AAV particles had good correlations with theoretical F/E ratios. Furthermore, the CNN reliably determined the F/E ratio with a smaller number of AAV particles than morphological analysis. Therefore, combining 100 keV cryo-EM with the developed image analysis methods enables the assessment of a wide range of AAV products.

Federated learning for medical image analysis: A survey.

Machine learning in medical imaging often faces a fundamental dilemma, namely, the small sample size problem. Many recent studies suggest using multi-domain data pooled from different acquisition sites/centers to improve statistical power. However, medical images from different sites cannot be easily shared to build large datasets for model training due to privacy protection reasons. As a promising solution, federated learning, which enables collaborative training of machine learning models based on data from different sites without cross-site data sharing, has attracted considerable attention recently. In this paper, we conduct a comprehensive survey of the recent development of federated learning methods in medical image analysis. We have systematically gathered research papers on federated learning and its applications in medical image analysis published between 2017 and 2023. Our search and compilation were conducted using databases from IEEE Xplore, ACM Digital Library, Science Direct, Springer Link, Web of Science, Google Scholar, and PubMed. In this survey, we first introduce the background of federated learning for dealing with privacy protection and collaborative learning issues. We then present a comprehensive review of recent advances in federated learning methods for medical image analysis. Specifically, existing methods are categorized based on three critical aspects of a federated learning system, including client end, server end, and communication techniques. In each category, we summarize the existing federated learning methods according to specific research problems in medical image analysis and also provide insights into the motivations of different approaches. In addition, we provide a review of existing benchmark medical imaging datasets and software platforms for current federated learning research. We also conduct an experimental study to empirically evaluate typical federated learning methods for medical image analysis. This survey can help to better understand the current research status, challenges, and potential research opportunities in this promising research field.

Genetic analysis of cassava brown streak disease root necrosis using image analysis and genome-wide association studies.

Cassava brown streak disease (CBSD) poses a substantial threat to food security. To address this challenge, we used PlantCV to extract CBSD root necrosis image traits from 320 clones, with an aim of identifying genomic regions through genome-wide association studies (GWAS) and candidate genes. Results revealed strong correlations among certain root necrosis image traits, such as necrotic area fraction and necrotic width fraction, as well as between the convex hull area of root necrosis and the percentage of necrosis. Low correlations were observed between CBSD scores obtained from the 1-5 scoring method and all root necrosis traits. Broad-sense heritability estimates of root necrosis image traits ranged from low to moderate, with the highest estimate of 0.42 observed for the percentage of necrosis, while narrow-sense heritability consistently remained low, ranging from 0.03 to 0.22. Leveraging data from 30,750 SNPs obtained through DArT genotyping, eight SNPs on chromosomes 1, 7, and 11 were identified and associated with both the ellipse eccentricity of root necrosis and the percentage of necrosis through GWAS. Candidate gene analysis in the 172.2kb region on the chromosome 1 revealed 24 potential genes with diverse functions, including ubiquitin-protein ligase, DNA-binding transcription factors, and RNA metabolism protein, among others. Despite our initial expectation that image analysis objectivity would yield better heritability estimates and stronger genomic associations than the 1-5 scoring method, the results were unexpectedly lower. Further research is needed to comprehensively understand the genetic basis of these traits and their relevance to cassava breeding and disease management.

Diffusion Tensor Image Analysis ALong the Perivascular Space (DTI-ALPS): Revisiting the Meaning and Significance of the Method.

More than 5 years have passed since the Diffusion Tensor Image Analysis ALong the Perivascular Space (DTI-ALPS) method was proposed with the intention of evaluating the glymphatic system. This method is handy due to its noninvasiveness, provision of a simple index in a straightforward formula, and the possibility of retrospective analysis. Therefore, the ALPS method was adopted to evaluate the glymphatic system for many disorders in many studies. The purpose of this review is to look back and discuss the ALPS method at this moment.The ALPS-index was found to be an indicator of a number of conditions related to the glymphatic system. Thus, although this was expected in the original report, the results of the ALPS method are often interpreted as uniquely corresponding to the function of the glymphatic system. However, a number of subsequent studies have pointed out the problems on the data interpretation. As they rightly point out, a higher ALPS-index indicates predominant Brownian motion of water molecules in the radial direction at the lateral ventricular body level, no more and no less. Fortunately, the term "ALPS-index" has become common and is now known as a common term by many researchers. Therefore, the ALPS-index should simply be expressed as high or low, and whether it reflects a glymphatic system is better to be discussed carefully. In other words, when a decreased ALPS-index is observed, it should be expressed as "decreased ALPS-index" and not directly as "glymphatic dysfunction". Recently, various methods have been proposed to evaluate the glymphatic system. It has become clear that these methods also do not seem to reflect the entirety of the extremely complex glymphatic system. This means that it would be desirable to use various methods in combination to evaluate the glymphatic system in a comprehensive manner.

A semi-automated spectral approach to analyzing cyclical growth patterns using fish scales.

We introduce a new semi-automated approach to analyzing growth patterns recorded on fish scales. After manually specifying the center of the scale, the algorithm radially unwraps the scale patterns along a series of transects from the center to the edge of the scale. A sliding window Fourier transform is used to produce a spectrogram for each sampled transect of the scale image. The maximum frequency over all sampled transects of the average spectrogram yields a well-discriminated peak frequency trace that can then serve as a growth template for that fish. The spectrogram patterns of individual fish scales can be adjusted to a common period accounting for differences in date of return or size of fish at return without biasing the growth profile of the scale. We apply the method to 147 Atlantic salmon scale images sampled from 3 years and contrast the information derived with this automated approach to what is obtained using classical human operator measurements. The spectrogram analysis quantifies growth patterns using the entire scale image rather than just a single transect and provides the possibility of more robustly analyzing individual scale growth patterns. This semi-automated approach that removes essentially all the human operator interventions provides an opportunity to process large datasets of fish scale images and combined with advanced analyses such as deep learning methods could lead to a greater understanding of salmon marine migration patterns and responses to variations in ecosystem conditions.

Decreased diffusivity along the perivascular space and cerebral hemodynamic disturbance in adult moyamoya disease.

Moyamoya disease (MMD) causes cerebral arterial stenosis and hemodynamic disturbance, the latter of which may disrupt glymphatic system activity, the waste clearance system. We evaluated 46 adult patients with MMD and 33 age- and sex-matched controls using diffusivity along the perivascular space (ALPS) measured with diffusion tensor imaging (ALPS index), which may partly reflect glymphatic system activity, and multishell diffusion MRI to generate freewater maps. Twenty-three patients were also evaluated via 15O-gas positron emission tomography (PET), and all patients underwent cognitive tests. Compared to controls, patients (38.4 (13.2) years old, 35 females) had lower ALPS indices in the left and right hemispheres (1.94 (0.27) vs. 1.65 (0.25) and 1.94 (0.22) vs. 1.65 (0.19), P < 0.001). While the right ALPS index showed no correlation, the left ALPS index was correlated with parenchymal freewater (ρ = -0.47, P < 0.001); perfusion measured with PET (cerebral blood flow, ρ = 0.70, P < 0.001; mean transit time, ρ = -0.60, P = 0.003; and oxygen extraction fraction, ρ = -0.52, P = 0.003); and cognitive tests (trail making test part B for executive function; ρ = -0.37, P = 0.01). Adult patients with MMD may exhibit decreased glymphatic system activity, which is correlated with the degree of hemodynamic disturbance, increased interstitial freewater, and cognitive dysfunction, but further investigation is needed.

A deep learning framework for identifying and segmenting three vessels in fetal heart ultrasound images.

BACKGROUND: Congenital heart disease (CHD) is one of the most common birth defects in the world. It is the leading cause of infant mortality, necessitating an early diagnosis for timely intervention. Prenatal screening using ultrasound is the primary method for CHD detection. However, its effectiveness is heavily reliant on the expertise of physicians, leading to subjective interpretations and potential underdiagnosis. Therefore, a method for automatic analysis of fetal cardiac ultrasound images is highly desired to assist an objective and effective CHD diagnosis. METHOD: In this study, we propose a deep learning-based framework for the identification and segmentation of the three vessels-the pulmonary artery, aorta, and superior vena cava-in the ultrasound three vessel view (3VV) of the fetal heart. In the first stage of the framework, the object detection model Yolov5 is employed to identify the three vessels and localize the Region of Interest (ROI) within the original full-sized ultrasound images. Subsequently, a modified Deeplabv3 equipped with our novel AMFF (Attentional Multi-scale Feature Fusion) module is applied in the second stage to segment the three vessels within the cropped ROI images. RESULTS: We evaluated our method with a dataset consisting of 511 fetal heart 3VV images. Compared to existing models, our framework exhibits superior performance in the segmentation of all the three vessels, demonstrating the Dice coefficients of 85.55%, 89.12%, and 77.54% for PA, Ao and SVC respectively. CONCLUSIONS: Our experimental results show that our proposed framework can automatically and accurately detect and segment the three vessels in fetal heart 3VV images. This method has the potential to assist sonographers in enhancing the precision of vessel assessment during fetal heart examinations.

Image-based second opinion for blood typing.

This paper considers a new method for providing a recommendation (second opinion) for a laboratory assistant in manual blood typing based on serological plates. The manual method consists of two steps: preparation and analysis. During preparation step the laboratory assistant needs to fill each well of a plate with a blood sample and a reagent mixture according to methodological guidelines. In the second step it is necessary to visually determine the result of the reactions, named agglutination. Despite the popularity of this method, it is slow and highly influenced by human factor, which cause blood typing errors. To increase the quality and performance of the analysis step, we propose a novel neural-based classification method. Our solution provides a fast way to fill the results into a laboratory system. We collected a new large dataset consisting of 3139 well images with GTs from donors' medical history and six experts' assessment for each. We showed that the proposed solution based on state-of-the-art architectures is comparable with the best expert and has 2.75 times fewer errors than the average one, with an overall accuracy equal to 98.4%. Taking into account the low-semantic nature of the task, we also considered shallow neural networks, which showed accuracy comparable with state-of-the-art models.

Amyloid brain-dedicated PET images can diagnose Alzheimer's pathology with Centiloid Scale.

PURPOSE: To evaluate whether the Centiloid Scale may be used to diagnose Alzheimer's Disease (AD) pathology effectively with the only use of amyloid PET imaging modality from a brain-dedicated PET scanner. METHODS: This study included 26 patients with amyloid PET images with 3 different radiotracers. All patients were acquired both on a PET/CT and a brain-dedicated PET scanner (CareMiBrain, CMB), from which 4 different reconstructions were implemented. A new pipeline was proposed and used for the PET image analysis based on the original Centiloid Scale processing pipeline, but with only PET images. The Youden's Index was employed to calculate the optimal cutoffs for diagnosis and evaluated by the AUC, accuracy, precision, and recall metrics. RESULTS: The Centiloid Scale (CL) processing pipeline was validated with and without the use of MR images. The CL cutoffs for AD pathology diagnosis on the PET/CT and the 4 CMB reconstructions were 34.4 ±â€¯2.2, 43.5 ±â€¯3.5, 51.9 ±â€¯12.5, 57.5 ±â€¯6.8 and 41.8 ±â€¯1.2 respectively. Overall, for these cutoffs all metrics obtained the maximum score. CONCLUSION: The Centiloid scale applied to PET images allows for AD pathology diagnosis. The CMB scanner can be used with the Centiloid scale to automatically assist in the diagnosis of AD pathology, relieving the large burden of neurodegenerative diseases on a traditional PET/CT.

A method for automated masking and plantar pressure analysis using segmented computed tomography scans.

BACKGROUND: Plantar pressure, a common gait and foot biomechanics measurement, is typically analyzed using proprietary commercial software packages. Regional plantar pressure analysis is often reported in terms of underlying bony geometry, and recent advances in image processing and accessibility have made computed tomography, radiographs, magnetic resonance imaging, or other imaging methods more popular for incorporating bone analyses in biomechanics. RESEARCH QUESTION: Can a computed tomography-based regional mask provide comparable regional analysis to commercial plantar pressure software and can the increased flexibility of an in-house method obtain additional insight from common measurements? METHODS: A plantar pressure analysis method was developed based on bony geometry from computed tomography scans to calculate peak pressure, pressure time integral incorporating sub-peak values, force time integral, pressure gradient, and pressure gradient angle. Static and dynamic plantar pressure were acquired for 4 subjects (male, 65 ± 2.4 years). Plantar pressure variables were calculated using commercial and computed tomography-based systems. RESULTS: Dynamic peak pressure, pressure time integral, and force-time integral computed using the bone-based software was 5 % (9kPa), 7 % (0.3kPa-s) and 13 % (0.3 N-s) different than the commercial software on average. Region masks of the metatarsals and toes differed between commercial and computed tomography-based software due to subject-specific bone geometry and toe shape. Pressure time integral values incorporating sub-peak pressure were higher and demonstrated higher relative hindfoot values compared to those without. Removing step-on frames to static pressure analysis decreased forefoot pressures. Regional maps of peak pressure and maximum pressure gradient demonstrate different peak locations. SIGNIFICANCE: Computed tomography-based regional masks are comparable to commercial masks. Inclusion of static step-on frames and sub-peak pressures may change regional plantar pressure patterns. Differences in location of maximum pressure gradient and peak pressure may be useful for assessing subject specific injury risk.