Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images

A continuous-action deep reinforcement learning-based agent for coronary artery centerline extraction in coronary CT angiography images

The lumen centerline of the coronary artery allows vessel reconstruction used to detect stenoses and plaques. Discrete-action-based centerline extraction methods suffer from artifacts and plaques. This study aimed to develop a continuous-action-based method which performs more effectively in cases involving artifacts or plaques. A continuous-action deep reinforcement learning-based model was trained to predict the artery's direction and radius value. The model is based on an Actor-Critic architecture. The Actor learns a deterministic policy to output the actions made by an agent. These actions indicate the centerline's direction and radius value consecutively. The Critic learns a value function to evaluate the quality of the agent's actions. A novel DDR reward was introduced to measure the agent's action (both centerline extraction and radius estimate) at each step. The method achieved an average OV of 95.7%, OF of 93.6%, OT of 97.3%, and AI of 0.22 mm in 80 test data. In 53 cases with artifacts or plaques, it achieved an average OV of 95.0%, OF of 91.5%, OT of 96.7%, and AI of 0.23 mm. The 95% limits of agreement between the reference and estimated radius values were - 0.46 mm and 0.43 mm in the 80 test data. Experiments demonstrate that the Actor-Critic architecture can achieve efficient centerline extraction and radius estimate. Compared with discrete-action-based methods, our method performs more effectively in cases involving artifacts or plaques. The extracted centerlines and radius values allow accurate coronary artery reconstruction that facilitates the detection of stenoses and plaques.

Semaphorin 3E-Plexin-D1 Pathway Downstream of the Luteinizing Hormone Surge Regulates Ovulation, Granulosa Cell Luteinization, and Ovarian Angiogenesis in Mice

Ovulation is induced by the luteinizing hormone (LH) surge and accompanied by granulosa cell luteinization and ovarian angiogenesis. Semaphorin 3E (Sema3E)-Plexin-D1 pathway regulates angiogenesis in other tissues, but its role in the ovary is unknown. Evidence indicates that Sema3E-Plexin-D1 pathway plays an important role in the mouse ovary. The expression of Sema3E and its receptor, Plexin-D1, is dynamically regulated in the mouse ovary downstream of the LH surge. This regulation requires the modulation of chromatin accessibility by CCAAT/enhancer-binding proteins α and β. Intraovarian injection of recombinant Sema3E results in reduced ovulation, impaired corpus luteum formation, and aberrant ovarian angiogenesis. These in vivo physiological abnormalities are consistent with altered expression of genes regulating these processes, and with data from in vitro cultured granulosa cells and ovarian stromal tissues treated with Sema3E or neutralizing antibody of Plexin-D1. The findings pinpoint Sema3E-Plexin-D1 pathway as a potential therapeutic target for fertility and infertility management.

Impact of canny edge detection preprocessing on performance of machine learning models for Parkinson's disease classification

This study investigates the classification of individuals as healthy or at risk of Parkinson's disease using machine learning (ML) models, focusing on the impact of dataset size and preprocessing techniques on model performance. Four datasets are created from an original dataset: [Formula: see text] (normal dataset), [Formula: see text] ([Formula: see text] subjected to Canny edge detection and Hessian filtering), [Formula: see text] (augmented [Formula: see text]), and [Formula: see text] (augmented [Formula: see text]). We evaluate a range of ML models-Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), XGBoost (XBG), Naive Bayes (NB), Support Vector Machine (SVM), and AdaBoost (AdB)-on these datasets, analyzing prediction accuracy, model size, and prediction latency. The results show that while larger datasets lead to increased model memory footprints and prediction latencies, the Canny edge detection preprocessing supplemented by Hessian filtering (used in [Formula: see text] and [Formula: see text]) degrades the performance of most models. In our experiment, we observe that Random Forest (RF) maintains a stable memory footprint of 61 KB across all datasets, while models like KNN and SVM show significant increases in memory usage, from 5.7-7 KB on [Formula: see text] to 102-220 KB on [Formula: see text], and similar increases in prediction time. Logistic Regression, Decision Tree, and Naive Bayes show stable memory footprints and fast prediction times across all datasets. XGBoost's prediction time increases from 180-200 ms on [Formula: see text] to 700-3000 ms on [Formula: see text]. Statistical analysis using the Mann-Whitney U test with 100 prediction accuracy observations per model (98 degrees of freedom) reveals significant differences in performance between models trained on [Formula: see text] and [Formula: see text] (p-values < 1e-34 for most models), while the effect sizes measured by estimating Cliff's delta values (approaching [Formula: see text]) indicate large shifts in performance, especially for SVM and XGBoost. These findings highlight the importance of selecting lightweight models like LR and DT for deployment in resource-constrained healthcare applications, as models like KNN, SVM, and XGBoost show significant increases in resource demands with larger datasets, particularly when Canny preprocessing is applied.

Distinguishing Neuromyelitis Optica Spectrum Disorders Subtypes: A Study on AQP4 and C3d Epitope Expression in Cytokine-Primed Human Astrocytes

Neuromyelitis optica spectrum disorders (NMOSD) are severe autoimmune conditions affecting the central nervous system. In a subset of cases, no autoantibodies are detectable with the currently used routine assays. This study aimed to determine whether the levels of expression of aquaporin-4 (AQP4), excitatory amino acid transporter 2 (EAAT2), or complement C3/C3d and C5b-9 in human astrocytes following incubation with patient sera under inflammatory conditions differ between the various NMOSD subtypes and whether such differences can help to identify autoantibody-mediated cases of NMOSD. Levels of AQP4, EAAT2, complement C3/C3d and C5b-9 epitope expression on human astrocytes pretreated with various cytokines were quantitatively analyzed via indirect immunofluorescence after exposure to sera from patients with AQP4-IgG seropositive, MOG-IgG seropositive, and AQP4/MOG-IgG double seronegative NMOSD. Significant differences in AQP4 and C3d epitope expression were observed, with IL-17A, IL-10, and IL-6 pre-treatment notably influencing astrocytic responses. Using uniform manifold approximation and projection (UMAP), patients were classified into clusters corresponding to AQP4-IgG seropositive, MOG-IgG seropositive, or double seronegative NMOSD. These results demonstrate distinct astrocytic staining patterns across NMOSD subtypes, providing a potential diagnostic tool for distinguishing between autoantibody-mediated astrocytopathy and other cases. These findings suggest specific pathogenic mechanisms linked to each NMOSD subtype, which may have implications for tailoring therapeutic strategies based on cytokine involvement and astrocyte reactivity.

The actin and microtubule network regulator WHAMM is identified as a key kidney disease risk gene

Nearly 850 million people suffer from kidney disease worldwide. Genome-wide association studies identify genetic variations at more than 800 loci associated with kidney dysfunction; however, the target genes, cell types, and mechanisms remain poorly understood. Here, we show that nucleotide variants on chromosome 15 are not only associated with kidney dysfunction but also regulate the expression of Wasp homolog associated with actin, membranes, and microtubules (WHAMM). WHAMM expression is higher in mice and patients with chronic and acute kidney disease. Mice with genetic deletion of Whamm appear healthy at baseline but develop less injury following cisplatin, folic acid, and unilateral ureteral obstruction. In vitro cell studies indicate that WHAMM controls cell death by regulating actin-mediated cytochrome c release from mitochondria and the formation of ASC speck. Pharmacological inhibition of actin dynamics mitigates kidney disease in experimental models. In summary, our study identifies a key role of WHAMM in the development of kidney disease.

Size-driven phase evolution in ultrathin relaxor films

Relaxor ferroelectrics (relaxors) are a special class of ferroelectrics with polar nanodomains (PNDs), which present characteristics such as slim hysteresis loops and strong dielectric relaxation. Applications such as nanoelectromechanical systems, capacitive-energy storage and pyroelectric-energy harvesters require thin-film relaxors. Hence, understanding relaxor behaviour in the ultrathin limit is of both fundamental and technological importance. Here the evolution of relaxor phases and PNDs with thickness is explored in prototypical thin relaxor films. Epitaxial 0.68PbMg1/3Nb2/3O3-0.32PbTiO3 films of various nanometre thicknesses are grown by pulsed-laser deposition and characterized by ferroelectric and dielectric measurements, temperature-dependent synchrotron X-ray diffuse scattering, scanning transmission electron microscopy and molecular dynamics simulations. As the film thickness approaches the length of the long axis of the PNDs (25-30 nm), electrostatically driven phase instabilities induce their rotation towards the plane of the films, stabilize the relaxor behaviour and give rise to anisotropic phase evolution along the out-of-plane and in-plane directions. The complex anisotropic evolution of relaxor properties ends in a collapse of the relaxor behaviour when the film thickness reaches the smallest dimension of the PNDs (6-10 nm). These findings establish that PNDs define the critical length scale for the evolution of relaxor behaviour at the nanoscale.

Spatially fractionated minibeam radiation delivered at clinically feasible dose rates induces transient vascular permeability

Microbeam Radiation Therapy is a preclinical form of spatially fractionated radiation therapy that utilizes synchrotron X-rays to deliver highly heterogeneous dose distributions at a micrometric scale. This radiation scheme has been shown to facilitate the induction of controlled and reversible vascular permeability, enhancing treatment efficacy of systemic therapeutic agents. Despite the promising preclinical results, translating microbeam SFRT to the clinic has been hindered by a reliance on synchrotron sources that operate at dose rates orders of magnitude greater than what is possible with clinical machines. Without rapid dose delivery, the microbeam geometry is susceptible to blurring due to physiologic motion when delivered at clinical dose rates. Therefore, larger beam widths, spaced further apart (minibeams) were employed to determine whether such effects can be observed with clinically achievable doses and dose rates. Vascular permeability was assessed in the chick chorioallantoic membrane vasculature following minibeam irradiation delivered at peak doses (10 Gy) and dose rates (10 Gy/s and 0.05 Gy/s) approaching clinical relevance. Transient, reversible permeability could be induced at these dose rates beginning 1-2 h post-irradiation. This was followed by temporary vascular occlusion in the beam path that was resolved by 7 h when delivered at 10 Gy/s but persisted longer when delivered at 0.05 Gy/s. Despite these changes, vascular function was maintained at both dose rates by 24 h post-IR, differing only in the degree of regeneration. The induction of permeability was also maintained when using a clinical orthovoltage system further supporting the potential clinical application of minibeam radiation therapy.

Deep learning for 3D vascular segmentation in hierarchical phase contrast tomography: a case study on kidney

Automated blood vessel segmentation is critical for biomedical image analysis, as vessel morphology changes are associated with numerous pathologies. Still, precise segmentation is difficult due to the complexity of vascular structures, anatomical variations across patients, the scarcity of annotated public datasets, and the quality of images. Our goal is to provide a foundation on the topic and identify a robust baseline model for application to vascular segmentation using a new imaging modality, Hierarchical Phase-Contrast Tomography (HiP-CT). We begin with an extensive review of current machine-learning approaches for vascular segmentation across various organs. Our work introduces a meticulously curated training dataset, verified by double annotators, consisting of vascular data from three kidneys imaged using HiP-CT as part of the Human Organ Atlas Project. HiP-CT pioneered at the European Synchrotron Radiation Facility in 2020, revolutionizes 3D organ imaging by offering a resolution of around 20 μm/voxel and enabling highly detailed localised zooms up to 1-2 μm/voxel without physical sectioning. We leverage the nnU-Net framework to evaluate model performance on this high-resolution dataset, using both known and novel samples, and implementing metrics tailored for vascular structures. Our comprehensive review and empirical analysis on HiP-CT data sets a new standard for evaluating machine learning models in high-resolution organ imaging. Our three experiments yielded Dice similarity coefficient (DSC) scores of 0.9523, 0.9410, and 0.8585, respectively. Nevertheless, DSC primarily assesses voxel-to-voxel concordance, overlooking several crucial characteristics of the vessels and should not be the sole metric for deciding the performance of vascular segmentation. Our results show that while segmentations yielded reasonably high scores-such as centerline DSC ranging from 0.82 to 0.88, certain errors persisted. Specifically, large vessels that collapsed due to the lack of hydrostatic pressure (HiP-CT is an ex vivo technique) were segmented poorly. Moreover, decreased connectivity in finer vessels and higher segmentation errors at vessel boundaries were observed. Such errors, particularly in significant vessels, obstruct the understanding of the structures by interrupting vascular tree connectivity. Our study establishes the benchmark across various evaluation metrics, for vascular segmentation of HiP-CT imaging data, an imaging technology that has the potential to substantively shift our understanding of human vascular networks.

Synthetic data in generalizable, learning-based neuroimaging

Synthetic data have emerged as an attractive option for developing machine-learning methods in human neuroimaging, particularly in magnetic resonance imaging (MRI)-a modality where image contrast depends enormously on acquisition hardware and parameters. This retrospective paper reviews a family of recently proposed methods, based on synthetic data, for generalizable machine learning in brain MRI analysis. Central to this framework is the concept of domain randomization, which involves training neural networks on a vastly diverse array of synthetically generated images with random contrast properties. This technique has enabled robust, adaptable models that are capable of handling diverse MRI contrasts, resolutions, and pathologies, while working out-of-the-box, without retraining. We have successfully applied this method to tasks such as whole-brain segmentation (SynthSeg), skull-stripping (SynthStrip), registration (SynthMorph, EasyReg), super-resolution, and MR contrast transfer (SynthSR). Beyond these applications, the paper discusses other possible use cases and future work in our methodology. Neural networks trained with synthetic data enable the analysis of clinical MRI, including large retrospective datasets, while greatly alleviating (and sometimes eliminating) the need for substantial labeled datasets, and offer enormous potential as robust tools to address various research goals.

Absence of oncomodulin increases susceptibility to noise-induced outer hair cell death and alters mitochondrial morphology

Cochlear outer hair cells (OHCs) play a fundamental role in the hearing sensitivity and frequency selectivity of mammalian hearing and are especially vulnerable to noise-induced damage. The OHCs depend on Ca2+ homeostasis, which is a balance between Ca2+ influx and extrusion, as well as Ca2+ buffering by proteins and organelles. Alterations in OHC Ca2+ homeostasis is not only an immediate response to noise, but also associated with impaired auditory function. However, there is little known about the contribution of Ca2+ buffering proteins and organelles to the vulnerability of OHCs to noise. In this study, we used a knockout (KO) mouse model where oncomodulin (Ocm), the major Ca2+ binding protein preferentially expressed in OHCs, is deleted. We show that Ocm KO mice were more susceptible to noise induced hearing loss compared to wildtype (WT) mice. Following noise exposure (106 dB SPL, 2 h), Ocm KO mice had higher threshold shifts and increased OHC loss and TUNEL staining, compared to age-matched WT mice. Mitochondrial morphology was significantly altered in Ocm KO OHCs compared to WT OHCs. Before noise exposure, Ocm KO OHCs showed decreased mitochondrial abundance, volume, and branching compared to WT OHCs, as measured by immunocytochemical staining of outer mitochondrial membrane protein, TOM20. Following noise exposure, mitochondrial proteins were barely visible in Ocm KO OHCs. Using a mammalian cell culture model of prolonged cytosolic Ca2+ overload, we show that OCM has protective effects against changes in mitochondrial morphology and apoptosis. These experiments suggest that disruption of Ca2+ buffering leads to an increase in noise vulnerability and mitochondrial-associated changes in OHCs.

Astrocytic GLUT1 reduction paradoxically improves central and peripheral glucose homeostasis

Astrocytes are considered an essential source of blood-borne glucose or its metabolites to neurons. Nonetheless, the necessity of the main astrocyte glucose transporter, i.e., GLUT1, for brain glucose metabolism has not been defined. Unexpectedly, we found that brain glucose metabolism was paradoxically augmented in mice with astrocytic GLUT1 reduction (GLUT1ΔGFAP mice). These mice also exhibited improved peripheral glucose metabolism especially in obesity, rendering them metabolically healthier. Mechanistically, we observed that GLUT1-deficient astrocytes exhibited increased insulin receptor-dependent ATP release, and that both astrocyte insulin signaling and brain purinergic signaling are essential for improved brain function and systemic glucose metabolism. Collectively, we demonstrate that astrocytic GLUT1 is central to the regulation of brain energetics, yet its depletion triggers a reprogramming of brain metabolism sufficient to sustain energy requirements, peripheral glucose homeostasis, and cognitive function.

SARC-UNet: A coronary artery segmentation method based on spatial attention and residual convolution

BACKGROUND AND OBJECTIVE

Coronary artery segmentation is a pivotal field that has received increasing attention in recent years. However, this task remains challenging because of the inhomogeneous distributions of the contrast agent and dim light, resulting in noise, vascular breakages and small vessel losses in the obtained segmentation results.

METHODS

To acquire better automatic blood vessel segmentation results for coronary angiography images, a UNet-based segmentation network (SARC-UNet) is constructed for coronary artery segmentation; this approach is based on residual convolution and spatial attention. First, we use the low-light image enhancement (LIME) approach to increase the contrast and clarity levels of coronary angiography images. Then, we design two residual convolution fusion modules (RCFM1 and RCFM2) that can successfully fuse the local and global information of coronary images while also capturing the characteristics of finer-grained blood vessels, hence preventing the loss of tiny blood vessels in the segmentation findings. Finally, using a cascaded waterfall structure, we create a new location-enhanced spatial attention (LESA) mechanism that can efficiently improve the long-distance dependencies between coronary vascular pixel features, eradicating vascular ruptures and noise in the segmentation results.

RESULTS

This article subjectively and objectively evaluates the experimental results. This method has performed well on five general indicators. Furthermore, it outperforms the connectivity indicators proposed in this article. This method can effectively segment blood vessels and obtain higher accuracy results.

CONCLUSIONS

Numerous experiments have shown that the suggested method outperforms the state-of-the-art approaches, particularly in terms of vessel connectivity and small blood vessel segmentation.

A Recruitment Maneuver After Apnea Testing Improves Oxygenation and Reduces Atelectasis in Organ Donors After Brain Death

BACKGROUND

Hypoxemia is the main modifiable factor preventing lungs from being transplanted from organ donors after brain death. One major contributor to impaired oxygenation in patients with brain injury is atelectasis. Apnea testing, an integral component of brain death declaration, promotes atelectasis and can worsen hypoxemia. In this study, we tested whether performing a recruitment maneuver (RM) after apnea testing could mitigate hypoxemia and atelectasis.

METHODS

During the study period, an RM (positive end-expiratory pressure of 15 cm H2O for 15 s then 30 cm H2O for 30 s) was performed immediately after apnea testing. We measured partial pressure of oxygen, arterial (PaO2) before and after RM. The primary outcomes were oxygenation (PaO2 to fraction of inspired oxygen [FiO2] ratio) and the severity of radiographic atelectasis (proportion of lung without aeration on computed tomography scans after brain death, quantified using an image analysis algorithm) in those who became organ donors. Outcomes in RM patients were compared with control patients undergoing apnea testing without RM in the previous 2 years.

RESULTS

Recruitment maneuver was performed in 54 patients after apnea testing, with a median immediate increase in PaO2 of 63 mm Hg (interquartile range 0-109, p = 0.07). Eighteen RM cases resulted in hypotension, but none were life-threatening. Of this cohort, 37 patients became organ donors, compared with 37 donors who had apnea testing without RM. The PaO2:FiO2 ratio was higher in the RM group (355 ± 129 vs. 288 ± 127, p = 0.03), and fewer had hypoxemia (PaO2:FiO2 ratio < 300 mm Hg, 22% vs. 57%; p = 0.04) at the start of donor management. The RM group showed less radiographic atelectasis (median 6% vs. 13%, p = 0.045). Although there was no difference in lungs transplanted (35% vs. 24%, p = 0.44), both better oxygenation and less atelectasis were associated with a higher likelihood of lungs being transplanted.

CONCLUSIONS

Recruitment maneuver after apnea testing reduces hypoxemia and atelectasis in organ donors after brain death. This effect may translate into more lungs being transplanted.

Empowering High-Throughput High-Content Analysis of Microphysiological Models: Open-Source Software for Automated Image Analysis of Microvessel Formation and Cell Invasion

Purpose

The primary aim of this study was to develop an open-source Python-based software for the automated analysis of dynamic cell behaviors in microphysiological models using non-confocal microscopy. This research seeks to address the existing gap in accessible tools for high-throughput analysis of endothelial tube formation and cell invasion in vitro, facilitating the rapid assessment of drug sensitivity.

Methods

Our approach involved annotating over 1000 2 mm Z-stacks of cancer and endothelial cell co-culture model and training machine learning models to automatically calculate cell coverage, cancer invasion depth, and microvessel dynamics. Specifically, cell coverage area was computed using focus stacking and Gaussian mixture models to generate thresholded Z-projections. Cancer invasion depth was determined using a ResNet-50 binary classification model, identifying which Z-planes contained invaded cells and measuring the total invasion depth. Lastly, microvessel dynamics were assessed through a U-Net Xception-style segmentation model for vessel prediction, the DisPerSE algorithm to extract an embedded graph, then graph analysis to quantify microvessel length and connectivity. To further validate our software, we reanalyzed an image set from a high-throughput drug screen involving a chemotherapy agent on a 3D cervical and endothelial co-culture model. Lastly, we applied this software to two naive image datasets from coculture lumen and microvascular fragment models.

Results

The software accurately measured cell coverage, cancer invasion, and microvessel length, yielding drug sensitivity IC50 values with a 95% confidence level compared to manual calculations. This approach significantly reduced the image processing time from weeks down to h. Furthermore, the software was able to calculate cell coverage, microvessel length, and invasion depth from two additional microphysiological models that were imaged with confocal microscopy, highlighting the versatility of the software.

Conclusions

Our free and open source software offers an automated solution for quantifying 3D cell behavior in microphysiological models assessed using non-confocal microscopy, providing the broader Cellular and Molecular Bioengineering community with an alternative to standard confocal microscopy paired with proprietary software.This software can be found in our GitHub repository: https://github.com/fogg-lab/tissue-model-analysis-tools.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12195-024-00821-2.

3D quantification of the lacunocanalicular network on human femoral diaphysis through synchrotron radiation-based nanoCT

Osteocytes are the major actors in bone mechanobiology. Within bone matrix, they are trapped close together in a submicrometric interconnected network: the lacunocanalicular network (LCN). The interstitial fluid circulating within the LCN transmits the mechanical information to the osteocytes that convert it into a biochemical signal. Understanding the interstitial fluid dynamics is necessary to better understand the bone mechanobiology. Due to the submicrometric dimensions of the LCN, making it difficult to experimentally investigate fluid dynamics, numerical models appear as a relevant tool for such investigation. To develop such models, there is a need for geometrical and morphological data on the human LCN. This study aims at providing morphological data on the human LCN from measurement of 27 human femoral diaphysis bone samples using synchrotron radiation nano-computed tomography with an isotropic voxel size of 100 nm. Except from the canalicular diameter, the canalicular morphological parameters presented a high variability within one sample. Some differences in terms of both lacunar and canalicular morphology were observed between the male and female populations. But it has to be highlighted that all the canaliculi cannot be detected with a voxel size of 100 nm. Hence, in the current study, only a specific population of large canaliculi that could be characterize. Still, to the authors knowledge, this is the first time such a data set was introduced to the community. Further processing will be achieved in order to provide new insight on the LCN permeability.

Systematic review and meta-analysis of automated methods for quantifying enlarged perivascular spaces in the brain

Research into magnetic resonance imaging (MRI)-visible perivascular spaces (PVS) has recently increased, as results from studies in different diseases and populations are cementing their association with sleep, disease phenotypes, and overall health indicators. With the establishment of worldwide consortia and the availability of large databases, computational methods that allow to automatically process all this wealth of information are becoming increasingly relevant. Several computational approaches have been proposed to assess PVS from MRI, and efforts have been made to summarise and appraise the most widely applied ones. We systematically reviewed and meta-analysed all publications available up to September 2023 describing the development, improvement, or application of computational PVS quantification methods from MRI. We analysed 67 approaches and 60 applications of their implementation, from 112 publications. The two most widely applied were the use of a morphological filter to enhance PVS-like structures, with Frangi being the choice preferred by most, and the use of a U-Net configuration with or without residual connections. Older adults or population studies comprising adults from 18 years old onwards were, overall, more frequent than studies using clinical samples. PVS were mainly assessed from T2-weighted MRI acquired in 1.5T and/or 3T scanners, although combinations using it with T1-weighted and FLAIR images were also abundant. Common associations researched included age, sex, hypertension, diabetes, white matter hyperintensities, sleep and cognition, with occupation-related, ethnicity, and genetic/hereditable traits being also explored. Despite promising improvements to overcome barriers such as noise and differentiation from other confounds, a need for joined efforts for a wider testing and increasing availability of the most promising methods is now paramount.

VESCL: an open source 2D vessel contouring library

PURPOSE

VESCL (pronounced 'vessel') is a novel vessel contouring library for computer-assisted 2D vessel contouring and segmentation. VESCL facilitates manual vessel segmentation in 2D medical images to generate gold-standard datasets for training, testing, and validating automatic vessel segmentation.

METHODS

VESCL is an open-source C++ library designed for easy integration into medical image processing systems. VESCL provides an intuitive interface for drawing variable-width parametric curves along vessels in 2D images. It includes highly optimized localized filtering to automatically fit drawn curves to the nearest vessel centerline and automatically determine the varying vessel width along each curve. To support a variety of segmentation paradigms, VESCL can export multiple segmentation representations including binary segmentations, occupancy maps, and distance fields.

RESULTS

VESCL provides sub-pixel resolution for vessel centerlines and vessel widths. It is optimized to segment small vessels with single- or sub-pixel widths that are visible to the human eye but hard to segment automatically via conventional filters. When tested on neurovascular digital subtraction angiography (DSA), VESCL's intuitive hand-drawn input with automatic curve fitting increased the speed of fully manual segmentation by 22× over conventional methods and by 3× over the best publicly available computer-assisted manual segmentation method. Accuracy was shown to be within the range of inter-operator variability of gold standard manually segmented data from a publicly available dataset of neurovascular DSA images as measured using Dice scores. Preliminary tests showed similar improvements for segmenting DSA of coronary arteries and RGB images of retinal arteries.

CONCLUSION

VESCL is an open-source C++ library for contouring vessels in 2D images which can be used to reduce the tedious, labor-intensive process of manually generating gold-standard segmentations for training, testing, and comparing automatic segmentation methods.

Enhancing vascular network visualization in 3D photoacoustic imaging: in vivo experiments with a vasculature filter

Filter-based vessel enhancement algorithms facilitate the extraction of vascular networks from medical images. Traditional filter-based algorithms struggle with high noise levels in images with false vessel extraction, and a low standard deviation (σ) value may introduce gaps at the centers of wide vessels. In this paper, a robust technique with less sensitivity to parameter tuning and better noise suppression than other filter-based methods for two-dimensional and three-dimensional images is implemented. In this study, we propose a filter that employs non-local means (NLM) for denoising, applying the vesselness function to suppress blob-like structures and filling the gaps in wide vessels without compromising edge quality or details. Acoustic resolution photoacoustic microscopy (AR-PAM) systems generate high-resolution volumetric photoacoustic images, but their vascular structure imaging suffers from out-of-focal signal-to-noise ratio (SNR) and lateral resolution loss. Implementing a synthetic aperture focusing technique (SAFT) based on a virtual detector (VD) improves out-of-focal region resolution and SNR. Combining the proposed filter with the SAFT algorithm enhances vascular structural imaging in AR-PAM systems. The proposed method is robust and applicable for animal tissues with less error of vasculature structure extraction in comparison to traditional fliter-based methods like Frangi and Sato filter. Also, the method is faster in terms of processing speed and less tuning parameters. We applied the method to a digital phantom to validate our approach and conducted in vivo experiments to demonstrate its superiority for real volumetric tissue imaging.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training

Liver vessel segmentation in magnetic resonance imaging data is important for the computational analysis of vascular remodeling, associated with a wide spectrum of diffuse liver diseases. Existing approaches rely on contrast enhanced imaging data, but the necessary dedicated imaging sequences are not uniformly acquired. Images without contrast enhancement are acquired more frequently, but vessel segmentation is challenging, and requires large-scale annotated data. We propose a multi-task learning framework to segment vessels in liver MRI without contrast. It exploits auxiliary contrast enhanced MRI data available only during training to reduce the need for annotated training examples. Our approach draws on paired native and contrast enhanced data with and without vessel annotations for model training. Results show that auxiliary data improves the accuracy of vessel segmentation, even if they are not available during inference. The advantage is most pronounced if only few annotations are available for training, since the feature representation benefits from the shared task structure. A validation of this approach to augment a model for brain tumor segmentation confirms its benefits across different domains. An auxiliary informative imaging modality can augment expert annotations even if it is only available during training.

Compensation of small data with large filters for accurate liver vessel segmentation from contrast-enhanced CT images

BACKGROUND

Segmenting liver vessels from contrast-enhanced computed tomography images is essential for diagnosing liver diseases, planning surgeries and delivering radiotherapy. Nevertheless, identifying vessels is a challenging task due to the tiny cross-sectional areas occupied by vessels, which has posed great challenges for vessel segmentation, such as limited features to be learned and difficult to construct high-quality as well as large-volume data.

METHODS

We present an approach that only requires a few labeled vessels but delivers significantly improved results. Our model starts with vessel enhancement by fading out liver intensity and generates candidate vessels by a classifier fed with a large number of image filters. Afterwards, the initial segmentation is refined using Markov random fields.

RESULTS

In experiments on the well-known dataset 3D-IRCADb, the averaged Dice coefficient is lifted to 0.63, and the mean sensitivity is increased to 0.71. These results are significantly better than those obtained from existing machine-learning approaches and comparable to those generated from deep-learning models.

CONCLUSION

Sophisticated integration of a large number of filters is able to pinpoint effective features from liver images that are sufficient to distinguish vessels from other liver tissues under a scarcity of large-volume labeled data. The study can shed light on medical image segmentation, especially for those without sufficient data.

Loss of spontaneous vasomotion precedes impaired cerebrovascular reactivity and microbleeds in a mouse model of cerebral amyloid angiopathy

Background

Cerebral amyloid angiopathy (CAA) is a cerebral small vessel disease in which amyloid-β accumulates in vessel walls. CAA is a leading cause of symptomatic lobar intracerebral hemorrhage and an important contributor to age-related cognitive decline. Recent work has suggested that vascular dysfunction may precede symptomatic stages of CAA, and that spontaneous slow oscillations in arteriolar diameter (termed vasomotion), important for amyloid-β clearance, may be impaired in CAA.

Methods

To systematically study the progression of vascular dysfunction in CAA, we used the APP23 mouse model of amyloidosis, which is known to develop spontaneous cerebral microbleeds mimicking human CAA. Using in vivo 2-photon microscopy, we longitudinally imaged unanesthetized APP23 transgenic mice and wildtype littermates from 7 to 14 months of age, tracking amyloid-β accumulation and vasomotion in individual pial arterioles over time. MRI was used in separate groups of 12-, 18-, and 24-month-old APP23 transgenic mice and wildtype littermates to detect microbleeds and to assess cerebral blood flow and cerebrovascular reactivity with pseudo-continuous arterial spin labeling.

Results

We observed a significant decline in vasomotion with age in APP23 mice, while vasomotion remained unchanged in wildtype mice with age. This decline corresponded in timing to initial vascular amyloid-β deposition (∼8-10 months of age), although was more strongly correlated with age than with vascular amyloid-β burden in individual arterioles. Declines in vasomotion preceded the development of MRI-visible microbleeds and the loss of smooth muscle actin in arterioles, both of which were observed in APP23 mice by 18 months of age. Additionally, evoked cerebrovascular reactivity was intact in APP23 mice at 12 months of age, but significantly lower in APP23 mice by 24 months of age.

Conclusions

Our findings suggest that a decline in spontaneous vasomotion is an early, potentially pre-symptomatic, manifestation of CAA and vascular dysfunction, and a possible future treatment target.

Loss of developmentally derived Irf8+ macrophages promotes hyperinnervation and arrhythmia in the adult zebrafish heart

Recent developments in cardiac macrophage biology have broadened our understanding of the critical functions of macrophages in the heart. As a result, there is further interest in understanding the independent contributions of distinct subsets of macrophage to cardiac development and function. Here, we demonstrate that genetic loss of interferon regulatory factor 8 (Irf8)-positive embryonic-derived macrophages significantly disrupts cardiac conduction, chamber function, and innervation in adult zebrafish. At 4 months post-fertilization (mpf), homozygous irf8st96/st96 mutants have significantly shortened atrial action potential duration and significant differential expression of genes involved in cardiac contraction. Functional in vivo assessments via electro- and echocardiograms at 12 mpf reveal that irf8 mutants are arrhythmogenic and exhibit diastolic dysfunction and ventricular stiffening. To identify the molecular drivers of the functional disturbances in irf8 null zebrafish, we perform single cell RNA sequencing and immunohistochemistry, which reveal increased leukocyte infiltration, epicardial activation, mesenchymal gene expression, and fibrosis. Irf8 null hearts are also hyperinnervated and have aberrant axonal patterning, a phenotype not previously assessed in the context of cardiac macrophage loss. Gene ontology analysis supports a novel role for activated epicardial-derived cells (EPDCs) in promoting neurogenesis and neuronal remodeling in vivo. Together, these data uncover significant cardiac abnormalities following embryonic macrophage loss and expand our knowledge of critical macrophage functions in heart physiology and governing homeostatic heart health.

Longitudinal alterations in brain perfusion and vascular reactivity in the zQ175DN mouse model of Huntington's disease

BACKGROUND

Huntington's disease (HD) is marked by a CAG-repeat expansion in the huntingtin gene that causes neuronal dysfunction and loss, affecting mainly the striatum and the cortex. Alterations in the neurovascular coupling system have been shown to lead to dysregulated energy supply to brain regions in several neurological diseases, including HD, which could potentially trigger the process of neurodegeneration. In particular, it has been observed in cross-sectional human HD studies that vascular alterations are associated to impaired cerebral blood flow (CBF). To assess whether whole-brain changes in CBF are present and follow a pattern of progression, we investigated both resting-state brain perfusion and vascular reactivity longitudinally in the zQ175DN mouse model of HD.

METHODS

Using pseudo-continuous arterial spin labelling (pCASL) MRI in the zQ175DN model of HD and age-matched wild-type (WT) mice, we assessed whole-brain, resting-state perfusion at 3, 6 and 9 and 13 months of age, and assessed hypercapnia-induced cerebrovascular reactivity (CVR), at 4.5, 6, 9 and 15 months of age.

RESULTS

We found increased perfusion in cortical regions of zQ175DN HET mice at 3 months of age, and a reduction of this anomaly at 6 and 9 months, ages at which behavioural deficits have been reported. On the other hand, under hypercapnia, CBF was reduced in zQ175DN HET mice as compared to the WT: for multiple brain regions at 6 months of age, for only somatosensory and retrosplenial cortices at 9 months of age, and brain-wide by 15 months. CVR impairments in cortical regions, the thalamus and globus pallidus were observed in zQ175DN HET mice at 9 months, with whole brain reactivity diminished at 15 months of age. Interestingly, blood vessel density was increased in the motor cortex at 3 months, while average vessel length was reduced in the lateral portion of the caudate putamen at 6 months of age.

CONCLUSION

Our findings reveal early cortical resting-state hyperperfusion and impaired CVR at ages that present motor anomalies in this HD model, suggesting that further characterization of brain perfusion alterations in animal models is warranted as a potential therapeutic target in HD.

Enlarged perivascular spaces are associated with white matter injury, cognition and inflammation in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy

Enlarged perivascular spaces have been previously reported in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy, but their significance and pathophysiology remains unclear. We investigated associations of white matter enlarged perivascular spaces with classical imaging measures, cognitive measures and plasma proteins to better understand what enlarged perivascular spaces represent in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy and whether radiographic measures of enlarged perivascular spaces would be of value in future therapeutic discovery studies for cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy. Twenty-four individuals with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy and 24 age- and sex-matched controls were included. Disease status was determined based on the presence of NOTCH3 mutation. Brain imaging measures of white matter hyperintensity, brain parenchymal fraction, white matter enlarged perivascular space volumes, clinical and cognitive measures as well as plasma proteomics were used in models. White matter enlarged perivascular space volumes were calculated via a novel, semiautomated pipeline, and levels of 7363 proteins were quantified in plasma using the SomaScan assay. The relationship of enlarged perivascular spaces with global burden of white matter hyperintensity, brain atrophy, functional status, neurocognitive measures and plasma proteins was modelled with linear regression models. Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy and control groups did not exhibit differences in mean enlarged perivascular space volumes. However, increased enlarged perivascular space volumes in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy were associated with increased white matter hyperintensity volume (β = 0.57, P = 0.05), Clinical Dementia Rating Sum-of-Boxes score (β = 0.49, P = 0.04) and marginally with decreased brain parenchymal fraction (β = -0.03, P = 0.10). In interaction term models, the interaction term between cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy disease status and enlarged perivascular space volume was associated with increased white matter hyperintensity volume (β = 0.57, P = 0.02), Clinical Dementia Rating Sum-of-Boxes score (β = 0.52, P = 0.02), Mini-Mental State Examination score (β = -1.49, P = 0.03) and marginally with decreased brain parenchymal fraction (β = -0.03, P = 0.07). Proteins positively associated with enlarged perivascular space volumes were found to be related to leukocyte migration and inflammation, while negatively associated proteins were related to lipid metabolism. Two central hub proteins were identified in protein networks associated with enlarged perivascular space volumes: CXC motif chemokine ligand 8/interleukin-8 and C-C motif chemokine ligand 2/monocyte chemoattractant protein 1. The levels of CXC motif chemokine ligand 8/interleukin-8 were also associated with increased white matter hyperintensity volume (β = 42.86, P = 0.03), and levels of C-C motif chemokine ligand 2/monocyte chemoattractant protein 1 were further associated with decreased brain parenchymal fraction (β = -0.0007, P < 0.01) and Mini-Mental State Examination score (β = -0.02, P < 0.01) and increased Trail Making Test B completion time (β = 0.76, P < 0.01). No proteins were associated with all three studied imaging measures of pathology (brain parenchymal fraction, enlarged perivascular spaces, white matter hyperintensity). Based on associations uncovered between enlarged perivascular space volumes and cognitive functions, imaging and plasma proteins, we conclude that white matter enlarged perivascular space volumes may capture pathologies contributing to chronic brain dysfunction and degeneration in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy.

Value-related learning in the olfactory bulb occurs through pathway-dependent perisomatic inhibition of mitral cells

Associating values to environmental cues is a critical aspect of learning from experiences, allowing animals to predict and maximise future rewards. Value-related signals in the brain were once considered a property of higher sensory regions, but their wide distribution across many brain regions is increasingly recognised. Here, we investigate how reward-related signals begin to be incorporated, mechanistically, at the earliest stage of olfactory processing, namely, in the olfactory bulb. In head-fixed mice performing Go/No-Go discrimination of closely related olfactory mixtures, rewarded odours evoke widespread inhibition in one class of output neurons, that is, in mitral cells but not tufted cells. The temporal characteristics of this reward-related inhibition suggest it is odour-driven, but it is also context-dependent since it is absent during pseudo-conditioning and pharmacological silencing of the piriform cortex. Further, the reward-related modulation is present in the somata but not in the apical dendritic tuft of mitral cells, suggesting an involvement of circuit components located deep in the olfactory bulb. Depth-resolved imaging from granule cell dendritic gemmules suggests that granule cells that target mitral cells receive a reward-related extrinsic drive. Thus, our study supports the notion that value-related modulation of olfactory signals is a characteristic of olfactory processing in the primary olfactory area and narrows down the possible underlying mechanisms to deeper circuit components that contact mitral cells perisomatically.

Tailoring the Morphology of a Diketopyrrolopyrrole-based Polymer as Films or Wires for High-Performance OFETs using Solution Shearing

Conjugated polymers often show efficient charge carrier transport along their backbone which is a primary factor in the electrical behavior of Organic Field Effect Transistor (OFETs) devices fabricated from these materials. Herein, a solution shearing procedure is reported to fabricate micro/nano wires from a diketopyrrolopyrrole (DPP)-based polymer. Millimeter to nanometer long polymer wires orientated in the coating direction are developed after a thorough analysis of the deposition conditions. It shows several morphological regimes-film, transition, and wires and experimentally derive a phase diagram for the parameters coating speed and surface energy of the substrate. The as-fabricated wires are isolated, which is confirmed by optical, atomic force, and scanning electron microscopy. Beside the macroscopic alignment of wires, cross-polarized optical microscopy images show strong birefringence suggesting a high degree of molecular orientation. This is further substantiated by polarized UV-Vis-NIR spectroscopy, selected area electron diffraction transmission electron microscopy, and grazing-incidence wide-angle X-ray scattering. Finally, an enhanced electrical performance of single wire OFETs is observed with a 15-fold increase in effective charge carrier mobility to 1.57 cm2 V-1 s-1 over devices using films (0.1 cm2 V-1 s-1 ) with similar values for on/off current ratio and threshold voltage.

Three-dimensional identification of microvascular pathology and neurovascular inflammation in severe white matter hyperintensity: a case report

White matter hyperintensities (WMH) are the most prevalent markers of cerebral small vessel disease (SVD), which is the major vascular risk factor for dementia. Microvascular pathology and neuroinflammation are suggested to drive the transition from normal-appearing white matter (NAWM) to WMH, particularly in individuals with hypertension. However, current imaging techniques cannot capture ongoing NAWM changes. The transition from NAWM into WMH is a continuous process, yet white matter lesions are often examined dichotomously, which may explain their underlying heterogeneity. Therefore, we examined microvascular and neurovascular inflammation pathology in NAWM and severe WMH three-dimensionally, along with gradual magnetic resonance imaging (MRI) fluid-attenuated inversion recovery (FLAIR) signal (sub-)segmentation. In WMH, the vascular network exhibited reduced length and complexity compared to NAWM. Neuroinflammation was more severe in WMH. Vascular inflammation was more pronounced in NAWM, suggesting its potential significance in converting NAWM into WMH. Moreover, the (sub-)segmentation of FLAIR signal displayed varying degrees of vascular pathology, particularly within WMH regions. These findings highlight the intricate interplay between microvascular pathology and neuroinflammation in the transition from NAWM to WMH. Further examination of neurovascular inflammation across MRI-visible alterations could aid deepening our understanding on WMH conversion, and therewith how to improve the prognosis of SVD.

A cognitive deep learning approach for medical image processing

In ophthalmic diagnostics, achieving precise segmentation of retinal blood vessels is a critical yet challenging task, primarily due to the complex nature of retinal images. The intricacies of these images often hinder the accuracy and efficiency of segmentation processes. To overcome these challenges, we introduce the cognitive DL retinal blood vessel segmentation (CoDLRBVS), a novel hybrid model that synergistically combines the deep learning capabilities of the U-Net architecture with a suite of advanced image processing techniques. This model uniquely integrates a preprocessing phase using a matched filter (MF) for feature enhancement and a post-processing phase employing morphological techniques (MT) for refining the segmentation output. Also, the model incorporates multi-scale line detection and scale space methods to enhance its segmentation capabilities. Hence, CoDLRBVS leverages the strengths of these combined approaches within the cognitive computing framework, endowing the system with human-like adaptability and reasoning. This strategic integration enables the model to emphasize blood vessels, accurately segment effectively, and proficiently detect vessels of varying sizes. CoDLRBVS achieves a notable mean accuracy of 96.7%, precision of 96.9%, sensitivity of 99.3%, and specificity of 80.4% across all of the studied datasets, including DRIVE, STARE, HRF, retinal blood vessel and Chase-DB1. CoDLRBVS has been compared with different models, and the resulting metrics surpass the compared models and establish a new benchmark in retinal vessel segmentation. The success of CoDLRBVS underscores its significant potential in advancing medical image processing, particularly in the realm of retinal blood vessel segmentation.

Computational study on the effects of central retinal blood vessels with asymmetric geometries on optic nerve head biomechanics

Optic nerve head (ONH) biomechanics are associated with glaucoma progression and have received considerable attention. Central retinal vessels (CRVs) oriented asymmetrically in the ONH are the single blood supply source to the retina and are believed to act as mechanically stable elements in the ONH in response to intraocular pressure (IOP). However, these mechanical effects are considered negligible in ONH biomechanical studies and received less attention. This study investigated the effects of CRVs on ONH biomechanics taking into consideration three-dimensional asymmetric CRV geometries. A CRV geometry was constructed based on CRV centerlines extracted from optical coherence tomography ONH images in eight healthy subjects and superimposed in the idealized ONH geometry established in previous studies. Mechanical analyses of the ONH in response to the IOP were conducted in the cases with and without CRVs for comparison. Obtained results demonstrated that the CRVs induced anisotropic ONH deformation, particularly in the lamina cribrosa and the associated upper neural tissues (prelamina) with wide ranges of spatial strain distributions. These results indicated that the CRVs result in anisotropic deformation with local strain concentration, rather than function to mechanically support in response to the IOP as in the conventional thinking in ophthalmology.

Multi-Level Residual Dual Attention Network for Major Cerebral Arteries Segmentation in MRA Toward Diagnosis of Cerebrovascular Disorders

Segmentation of major brain vessels is very important for the diagnosis of cerebrovascular disorders and subsequent surgical planning. Vessel segmentation is an important preprocessing step for a wide range of algorithms for the automatic diagnosis or treatment of several vascular pathologies and as such, it is valuable to have a well-performing vascular segmentation pipeline. In this article, we propose an end-to-end multiscale residual dual attention deep neural network for resilient major brain vessel segmentation. In the proposed network, the encoder and decoder blocks of the U-Net are replaced with the multi-level atrous residual blocks to enhance the learning capability by increasing the receptive field to extract the various semantic coarse- and fine-grained features. Dual attention block is incorporated in the bottleneck to perform effective multiscale information fusion to obtain detailed structure of blood vessels. The methods were evaluated on the publicly available TubeTK data set. The proposed method outperforms the state-of-the-art techniques with dice of 0.79 on the whole-brain prediction. The statistical and visual assessments indicate that proposed network is robust to outliers and maintains higher consistency in vessel continuity than the traditional U-Net and its variations.

Dermal features derived from optoacoustic tomograms via machine learning correlate microangiopathy phenotypes with diabetes stage

Skin microangiopathy has been associated with diabetes. Here we show that skin-microangiopathy phenotypes in humans can be correlated with diabetes stage via morphophysiological cutaneous features extracted from raster-scan optoacoustic mesoscopy (RSOM) images of skin on the leg. We obtained 199 RSOM images from 115 participants (40 healthy and 75 with diabetes), and used machine learning to segment skin layers and microvasculature to identify clinically explainable features pertaining to different depths and scales of detail that provided the highest predictive power. Features in the dermal layer at the scale of detail of 0.1-1 mm (such as the number of junction-to-junction branches) were highly sensitive to diabetes stage. A 'microangiopathy score' compiling the 32 most-relevant features predicted the presence of diabetes with an area under the receiver operating characteristic curve of 0.84. The analysis of morphophysiological cutaneous features via RSOM may allow for the discovery of diabetes biomarkers in the skin and for the monitoring of diabetes status.

Reconfiguration and loss of peritubular capillaries in chronic kidney disease

Functional and structural alterations of peritubular capillaries (PTCs) are a major determinant of chronic kidney disease (CKD). Using a software-based algorithm for semiautomatic segmentation and morphometric quantification, this study analyzes alterations of PTC shape associated with chronic tubulointerstitial injury in three mouse models and in human biopsies. In normal kidney tissue PTC shape was predominantly elongated, whereas the majority of PTCs associated with chronic tubulointerstitial injury had a rounder shape. This was reflected by significantly reduced PTC luminal area, perimeter and diameters as well as by significantly increased circularity and roundness. These morphological alterations were consistent in all mouse models and human kidney biopsies. The mean circularity of PTCs correlated significantly with categorized glomerular filtration rates and the degree of interstitial fibrosis and tubular atrophy (IFTA) and classified the presence of CKD or IFTA. 3D reconstruction of renal capillaries revealed not only a significant reduction, but more importantly a substantial simplification and reconfiguration of the renal microvasculature in mice with chronic tubulointerstitial injury. Computational modelling predicted that round PTCs can deliver oxygen more homogeneously to the surrounding tissue. Our findings indicate that alterations of PTC shape represent a common and uniform reaction to chronic tubulointerstitial injury independent of the underlying kidney disease.

The Co-Expression Pattern of Calcium-Binding Proteins with γ-Aminobutyric Acid and Glutamate Transporters in the Amygdala of the Guinea Pig: Evidence for Glutamatergic Subpopulations

The amygdala has large populations of neurons utilizing specific calcium-binding proteins such as parvalbumin (PV), calbindin (CB), or calretinin (CR). They are considered specialized subsets of γ-aminobutyric acid (GABA) interneurons; however, many of these cells are devoid of GABA or glutamate decarboxylase. The neurotransmitters used by GABA-immunonegative cells are still unknown, but it is suggested that a part may use glutamate. Thus, this study investigates in the amygdala of the guinea pig relationships between PV, CB, or CR-containing cells and GABA transporter (VGAT) or glutamate transporter type 2 (VGLUT2), markers of GABAergic and glutamatergic neurons, respectively. The results show that although most neurons using PV, CB, and CR co-expressed VGAT, each of these populations also had a fraction of VGLUT2 co-expressing cells. For almost all neurons using PV (~90%) co-expressed VGAT, while ~1.5% of them had VGLUT2. The proportion of neurons using CB and VGAT was smaller than that for PV (~80%), while the percentage of cells with VGLUT2 was larger (~4.5%). Finally, only half of the neurons using CR (~53%) co-expressed VGAT, while ~3.5% of them had VGLUT2. In conclusion, the populations of neurons co-expressing PV, CB, and CR are in the amygdala, primarily GABAergic. However, at least a fraction of neurons in each of them co-express VGLUT2, suggesting that these cells may use glutamate. Moreover, the number of PV-, CB-, and CR-containing neurons that may use glutamate is probably larger as they can utilize VGLUT1 or VGLUT3, which are also present in the amygdala.

Shape-Aware 3D Small Vessel Segmentation with Local Contrast Guided Attention

The automated segmentation and analysis of small vessels from in vivo imaging data is an important task for many clinical applications. While current filtering and learning methods have achieved good performance on the segmentation of large vessels, they are sub-optimal for small vessel detection due to their apparent geometric irregularity and weak contrast given the relatively limited resolution of existing imaging techniques. In addition, for supervised learning approaches, the acquisition of accurate pixel-wise annotations in these small vascular regions heavily relies on skilled experts. In this work, we propose a novel self-supervised network to tackle these challenges and improve the detection of small vessels from 3D imaging data. First, our network maximizes a novel shape-aware flux-based measure to enhance the estimation of small vasculature with non-circular and irregular appearances. Then, we develop novel local contrast guided attention(LCA) and enhancement(LCE) modules to boost the vesselness responses of vascular regions of low contrast. In our experiments, we compare with four filtering-based methods and a state-of-the-art self-supervised deep learning method in multiple 3D datasets to demonstrate that our method achieves significant improvement in all datasets. Further analysis and ablation studies have also been performed to assess the contributions of various modules to the improved performance in 3D small vessel segmentation. Our code is available at https://github.com/dengchihwei/LCNetVesselSeg.

Predictors of female age, health and attractiveness perception from skin feature analysis of digital portraits in five ethnic groups

OBJECTIVE

Research indicates the impact of skin colour, tone evenness and surface topography on ratings of age, health and attractiveness in women. In addition to subjective assessments, these effects have been quantified with objective measures derived from skin image analysis. Signs of skin ageing may manifest differently across ethnic groups. However, comparisons have been limited to research with two ethnic groups, preventing conclusions about an ethnicity-specific ranking of skin ageing signs.

METHODS

We report results from a multi-ethnic and multi-centre study in which faces of women (n = 180; aged 20-69 years) from five ethnic groups were imaged. Facial images were rated for age, health and attractiveness by members of the same ethnic group (each n = 120). Digital image analysis was used to quantify skin colour, gloss, tone evenness and wrinkling/sagging. We assessed associations between face ratings and skin image measurements in the total sample (i.e. all ethnic groups) and separately by ethnicity.

RESULTS

Skin image analysis revealed differences between ethnic groups, including skin colour, gloss, tone evenness, wrinkling and sagging. Differences in the relative predictive utility of individual skin features in accounting for ratings of age, health and attractiveness also were observed between ethnic groups. Facial wrinkling and sagging were the best predictors of face ratings in each ethnic group, with some differences in the type (or predictive magnitude) of skin features.

CONCLUSION

The current findings corroborate previous reports of differences between ethnic groups in female facial skin and indicate differential effects of skin features on ratings of age, health and attractiveness, within and between ethnic groups. Facial wrinkling and sagging were the best predictors of age and attractiveness ratings, and skin tone evenness and gloss had an additional role in ratings of health.

Targeted capillary photothrombosis via multiphoton excitation of Rose Bengal

Microvascular stalling, the process occurring when a capillary temporarily loses perfusion, has gained increasing interest in recent years through its demonstrated presence in various neuropathologies. Studying the impact of such stalls on the surrounding brain tissue is of paramount importance to understand their role in such diseases. Despite efforts trying to study the stalling events, investigations are hampered by their elusiveness and scarcity. In an attempt to alleviate these hurdles, we present here a novel methodology enabling transient occlusions of targeted microvascular segments through multiphoton excitation of Rose Bengal, an established photothrombotic agent. With n = 7 mice C57BL/6 J (5 males and 2 females) and 95 photothrombosis trials, we demonstrate the ability of triggering reversible blockages by illuminating a capillary segment during ∼300 s at 1000 nm, using a standard Ti:Sapphire femtosecond laser. Furthermore, we performed concurrent Optical Coherence Microscopy (OCM) angiography imaging of the microvascular network to highlight the specificity of the targeted occlusion and its duration. Through comparison with a control group, we conclude that blood flow cessation is indeed created by the photothrombotic agent via multiphoton excitation and is temporary, followed by a flow recovery in less than 24 h. Moreover, Immunohistology points toward a stalling mechanism driven by adherence of the neutrophil in the vascular lumen. This observation seems to be promoted by the inflammation locally created via multiphoton activation of Rose Bengal.

3D vessel extraction using a scale-adaptive hybrid parametric tracker

3D vessel extraction has great significance in the diagnosis of vascular diseases. However, accurate extraction of vessels from computed tomography angiography (CTA) data is challenging. For one thing, vessels in different body parts have a wide range of scales and large curvatures; for another, the intensity distributions of vessels in different CTA data vary considerably. Besides, surrounding interfering tissue, like bones or veins with similar intensity, also seriously affects vessel extraction. Considering all the above imaging and structural features of vessels, we propose a new scale-adaptive hybrid parametric tracker (SAHPT) to extract arbitrary vessels of different body parts. First, a geometry-intensity parametric model is constructed to calculate the geometry-intensity response. While geometry parameters are calculated to adapt to the variation in scale, intensity parameters can also be estimated to meet non-uniform intensity distributions. Then, a gradient parametric model is proposed to calculate the gradient response based on a multiscale symmetric normalized gradient filter which can effectively separate the target vessel from surrounding interfering tissue. Last, a hybrid parametric model that combines the geometry-intensity and gradient parametric models is constructed to evaluate how well it fits a local image patch. In the extraction process, a multipath spherical sampling strategy is used to solve the problem of anatomical complexity. We have conducted many quantitative experiments using the synthetic and clinical CTA data, asserting its superior performance compared to traditional or deep learning-based baselines.

Segmentation of 4D Flow MRI: Comparison between 3D Deep Learning and Velocity-Based Level Sets

A thoracic aortic aneurysm is an abnormal dilatation of the aorta that can progress and lead to rupture. The decision to conduct surgery is made by considering the maximum diameter, but it is now well known that this metric alone is not completely reliable. The advent of 4D flow magnetic resonance imaging has allowed for the calculation of new biomarkers for the study of aortic diseases, such as wall shear stress. However, the calculation of these biomarkers requires the precise segmentation of the aorta during all phases of the cardiac cycle. The objective of this work was to compare two different methods for automatically segmenting the thoracic aorta in the systolic phase using 4D flow MRI. The first method is based on a level set framework and uses the velocity field in addition to 3D phase contrast magnetic resonance imaging. The second method is a U-Net-like approach that is only applied to magnitude images from 4D flow MRI. The used dataset was composed of 36 exams from different patients, with ground truth data for the systolic phase of the cardiac cycle. The comparison was performed based on selected metrics, such as the Dice similarity coefficient (DSC) and Hausdorf distance (HD), for the whole aorta and also three aortic regions. Wall shear stress was also assessed and the maximum wall shear stress values were used for comparison. The U-Net-based approach provided statistically better results for the 3D segmentation of the aorta, with a DSC of 0.92 ± 0.02 vs. 0.86 ± 0.5 and an HD of 21.49 ± 24.8 mm vs. 35.79 ± 31.33 mm for the whole aorta. The absolute difference between the wall shear stress and ground truth slightly favored the level set method, but not significantly (0.754 ± 1.07 Pa vs. 0.737 ± 0.79 Pa). The results showed that the deep learning-based method should be considered for the segmentation of all time steps in order to evaluate biomarkers based on 4D flow MRI.

Prostate cancer tissue classification by multiphoton imaging, automated image analysis and machine learning

Prostate carcinoma, a slow-growing and often indolent tumour, is the second most commonly diagnosed cancer among men worldwide. The prognosis is mainly based on the Gleason system through prostate biopsy analysis. However, new treatment and monitoring strategies depend on a more precise diagnosis. Here, we present results by multiphoton imaging for prostate tumour samples from 120 patients that allow to obtain quantitative parameters leading to specific tumour aggressiveness signatures. An automated image analysis was developed to recognise and quantify stromal fibre and neoplastic cell regions in each image. The set of metrics was able to distinguish between non-neoplastic tissue and carcinoma areas by linear discriminant analysis and random forest with accuracy of 89% ± 3%, but between Gleason groups of only 46% ± 6%. The reactive stroma analysis improved the accuracy to 65% ± 5%, clearly demonstrating that stromal parameters should be considered as additional criteria for a more accurate diagnosis.

Estimation of laser-induced damage depth from surface image features

In laser damage experiments, damage initiation and growth are typically monitored by imaging the surface of the tested fused silica sample, ignoring their bulk morphology. The depth of a damage site in fused silica optics is considered to be proportional to its equivalent diameter. However, some damage sites experience phases with no diameter changes but growth in the bulk independently from their surface. A proportionality relationship with the damage diameter does not accurately describe the growth of such sites. In the following, an accurate estimator for damage depth is proposed, which is based on the hypothesis that the light intensity scattered by a damage site is proportional to its volume. Such an estimator, using the pixel intensity, describes the change of damage depth through successive laser irradiations, including phases in which depth and diameter variations are uncorrelated.

Plasma membrane topography governs the 3D dynamic localization of IgM B cell antigen receptor clusters

B lymphocytes recognize bacterial or viral antigens via different classes of the B cell antigen receptor (BCR). Protrusive structures termed microvilli cover lymphocyte surfaces, and are thought to perform sensory functions in screening antigen-bearing surfaces. Here, we have used lattice light-sheet microscopy in combination with tailored custom-built 4D image analysis to study the cell-surface topography of B cells of the Ramos Burkitt's Lymphoma line and the spatiotemporal organization of the IgM-BCR. Ramos B-cell surfaces were found to form dynamic networks of elevated ridges bridging individual microvilli. A fraction of membrane-localized IgM-BCR was found in clusters, which were mainly associated with the ridges and the microvilli. The dynamic ridge-network organization and the IgM-BCR cluster mobility were linked, and both were controlled by Arp2/3 complex activity. Our results suggest that dynamic topographical features of the cell surface govern the localization and transport of IgM-BCR clusters to facilitate antigen screening by B cells.

Holes in the tundra: Invasive earthworms alter soil structure and moisture in tundra soils

Human introductions have resulted in earthworms establishing in the Arctic, species known to cause cascading ecosystem change. However, few quantitative outdoor experiments have been performed that describe how these soil modifying earthworms are reshaping structures in tundra soils. In this study, we used three-dimensional (3-D) X-ray images of soil cores (approximately 10 cm diameter, 20 cm height, N = 48) to assess how earthworms (Aporrectodea sp. and Lumbricus sp.) affect soil structure and macropore networks in an outdoor mesocosm experiment that lasted four summers. Effects were assessed in both shrub-dominated (heath) and herb-dominated (meadow) tundra. Earthworms almost doubled the macroporosity in meadow soils and tripled macroporosity in heath. Interestingly, the fractal dimension of macropores decreased in response to earthworm burrowing in both systems, indicating that the presence of earthworms reduced the geometric complexity in comparison to other pore-generating processes active in the tundra. Observed effects on soil structure occurred along with a dramatically reduced soil moisture content, which was observed the first winter after earthworm introduction in the meadow. Our findings suggest that predictions of future changes in vegetation and soil carbon pools in the Arctic should include major impacts on soil properties that earthworms induce.

Simulation-based inference for non-parametric statistical comparison of biomolecule dynamics

Numerous models have been developed to account for the complex properties of the random walks of biomolecules. However, when analysing experimental data, conditions are rarely met to ensure model identification. The dynamics may simultaneously be influenced by spatial and temporal heterogeneities of the environment, out-of-equilibrium fluxes and conformal changes of the tracked molecules. Recorded trajectories are often too short to reliably discern such multi-scale dynamics, which precludes unambiguous assessment of the type of random walk and its parameters. Furthermore, the motion of biomolecules may not be well described by a single, canonical random walk model. Here, we develop a two-step statistical testing scheme for comparing biomolecule dynamics observed in different experimental conditions without having to identify or make strong prior assumptions about the model generating the recorded random walks. We first train a graph neural network to perform simulation-based inference and thus learn a rich summary statistics vector describing individual trajectories. We then compare trajectories obtained in different biological conditions using a non-parametric maximum mean discrepancy (MMD) statistical test on their so-obtained summary statistics. This procedure allows us to characterise sets of random walks regardless of their generating models, without resorting to model-specific physical quantities or estimators. We first validate the relevance of our approach on numerically simulated trajectories. This demonstrates both the statistical power of the MMD test and the descriptive power of the learnt summary statistics compared to estimates of physical quantities. We then illustrate the ability of our framework to detect changes in α-synuclein dynamics at synapses in cultured cortical neurons, in response to membrane depolarisation, and show that detected differences are largely driven by increased protein mobility in the depolarised state, in agreement with previous findings. The method provides a means of interpreting the differences it detects in terms of single trajectory characteristics. Finally, we emphasise the interest of performing various comparisons to probe the heterogeneity of experimentally acquired datasets at different levels of granularity (e.g., biological replicates, fields of view, and organelles).

Three-dimensional cerebral vasculature topological parameter extraction of transgenic zebrafish embryos with a filling-enhancement deep learning network

Quantitative analysis of zebrafish cerebral vasculature is essential for the study of vascular development and disease. We developed a method to accurately extract the cerebral vasculature topological parameters of transgenic zebrafish embryos. The intermittent and hollow vascular structures of transgenic zebrafish embryos, obtained from 3D light-sheet imaging, were transformed into continuous solid structures with a filling-enhancement deep learning network. The enhancement enables the extraction of 8 vascular topological parameters accurately. Quantitation of the zebrafish cerebral vasculature vessels with the topological parameters show a developmental pattern transition from 2.5 to 5.5 dpf.

Dopaminergic and cholinergic modulation of the amygdala is altered in female mice with oestrogen receptor β deprivation

The amygdala is modulated by dopaminergic and cholinergic neurotransmission, and this modulation is altered in mood disorders. Therefore, this study was designed to evaluate the presence/absence of quantitative alterations in the expression of main dopaminergic and cholinergic markers in the amygdala of mice with oestrogen receptor β (ERβ) knock-out which exhibit increased anxiety, using immunohistochemistry and quantitative methods. Such alterations could either contribute to increased anxiety or be a compensatory mechanism for reducing anxiety. The results show that among dopaminergic markers, the expression of tyrosine hydroxylase (TH), dopamine transporter (DAT) and dopamine D₂-like receptor (DA₂) is significantly elevated in the amygdala of mice with ERβ deprivation when compared to matched controls, whereas the content of dopamine D₁-like receptor (DA₁) is not altered by ERβ knock-out. In the case of cholinergic markers, muscarinic acetylcholine type 1 receptor (AChR_M1) and alpha-7 nicotinic acetylcholine receptor (AChR_α7) display overexpression while the content of acetylcholinesterase (AChE) and vesicular acetylcholine transporter (VAChT) remains unchanged. In conclusion, in the amygdala of ERβ knock-out female the dopaminergic and cholinergic signalling is altered, however, to determine the exact role of ERβ in the anxiety-related behaviour further studies are required.

A sex difference in mouse dopaminergic projections from the midbrain to basolateral amygdala

BACKGROUND

Dopaminergic circuits play important roles in the motivational control of behavior and dysfunction in dopaminergic circuits have been implicated in several psychiatric disorders, such as schizophrenia and depression. While these disorders exhibit different incidence rates in men and women, the potential sex differences in the underlying neural circuits are not well-understood. Previous anatomical tracing studies in mammalian species have revealed a prominent circuit projection connecting the dopaminergic midbrain ventral tegmental area (VTA) to the basolateral amygdala (BLA), which is involved in emotional processing and associative learning. However, whether there is any sex difference in this anatomical circuit remains unknown.

METHODS

To study the potential sex differences in the VTA-to-BLA dopaminergic circuit, we injected two viral vectors encoding fluorescent reporters of axons and synaptic boutons (AAV-FLEX-tdTomato and AAV-FLEX-SynaptophysinGFP, respectively) into the VTA of a mouse transgenic driver line (tyrosine hydroxylase promoter-driven Cre, or TH-Cre), which restricts the reporter expression to dopaminergic neurons. We then used confocal fluorescent microscopy to image the distribution and density of dopaminergic axons and synaptic boutons in serial sections of both male and female mouse brain.

RESULTS

We found that the overall labeling intensity of VTA-to-BLA dopaminergic projections is intermediate among forebrain dopaminergic pathways, significantly higher than the projections to the prefrontal cortex, but lower than the projections to the nucleus accumbens. Within the amygdala areas, dopaminergic axons are concentrated in BLA. Although the size of BLA and the density of dopaminergic axons within BLA are similar between male and female mice, the density of dopaminergic synaptic boutons in BLA is significantly higher in male brain than female brain.

CONCLUSIONS

Our results demonstrate an anatomical sex difference in mouse dopaminergic innervations from the VTA to BLA. This finding may provide a structural foundation to study neural circuit mechanisms underlying sex differences in motivational and emotional behaviors and related psychiatric dysfunctions.

Tracing and tracking filamentous structures across scales: A systematic review

Filamentous structures are ubiquitous in nature, are studied in diverse scientific fields, and span vastly different spatial scales. Filamentous structures in biological systems fulfill different functions and often form dynamic networks that respond to perturbations. Therefore, characterizing the properties of filamentous structures and the networks they form is important to gain better understanding of systems level functions and dynamics. Filamentous structures are captured by various imaging technologies, and analysis of the resulting imaging data addresses two problems: (i) identification (tracing) of filamentous structures in a single snapshot and (ii) characterizing the dynamics (i.e., tracking) of filamentous structures over time. Therefore, considerable research efforts have been made in developing automated methods for tracing and tracking of filamentous structures. Here, we provide a systematic review in which we present, categorize, and discuss the state-of-the-art methods for tracing and tracking of filamentous structures in sparse and dense networks. We highlight the mathematical approaches, assumptions, and constraints particular for each method, allowing us to pinpoint outstanding challenges and offer perspectives for future research aimed at gaining better understanding of filamentous structures in biological systems.

Neuron tracing from light microscopy images: automation, deep learning and bench testing

MOTIVATION

Large-scale neuronal morphologies are essential to neuronal typing, connectivity characterization and brain modeling. It is widely accepted that automation is critical to the production of neuronal morphology. Despite previous survey papers about neuron tracing from light microscopy data in the last decade, thanks to the rapid development of the field, there is a need to update recent progress in a review focusing on new methods and remarkable applications.

RESULTS

This review outlines neuron tracing in various scenarios with the goal to help the community understand and navigate tools and resources. We describe the status, examples and accessibility of automatic neuron tracing. We survey recent advances of the increasingly popular deep-learning enhanced methods. We highlight the semi-automatic methods for single neuron tracing of mammalian whole brains as well as the resulting datasets, each containing thousands of full neuron morphologies. Finally, we exemplify the commonly used datasets and metrics for neuron tracing bench testing.

Human treelike tubular structure segmentation: A comprehensive review and future perspectives

Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed.

Volumetric Characterization of Microvasculature in Ex Vivo Human Brain Samples By Serial Sectioning Optical Coherence Tomography

OBJECTIVE

Serial sectioning optical coherence tomography (OCT) enables accurate volumetric reconstruction of several cubic centimeters of human brain samples. We aimed to identify anatomical features of the ex vivo human brain, such as intraparenchymal blood vessels and axonal fiber bundles, from the OCT data in 3D, using intrinsic optical contrast.

METHODS

We developed an automatic processing pipeline to enable characterization of the intraparenchymal microvascular network in human brain samples.

RESULTS

We demonstrated the automatic extraction of the vessels down to a 20 μm in diameter using a filtering strategy followed by a graphing representation and characterization of the geometrical properties of microvascular network in 3D. We also showed the ability to extend this processing strategy to extract axonal fiber bundles from the volumetric OCT image.

CONCLUSION

This method provides a viable tool for quantitative characterization of volumetric microvascular network as well as the axonal bundle properties in normal and pathological tissues of the ex vivo human brain.