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

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.

A Benchmark Framework for Multiregion Analysis of Vesselness Filters

Vessel enhancement (aka vesselness) filters, are part of angiographic image processing for more than twenty years. Their popularity comes from their ability to enhance tubular structures while filtering out other structures, especially as a preliminary step of vessel segmentation. Choosing the right vesselness filter among the many available can be difficult, and their parametrization requires an accurate understanding of their underlying concepts and a genuine expertise. In particular, using default parameters is often not enough to reach satisfactory results on specific data. Currently, only few benchmarks are available to help the users choosing the best filter and its parameters for a given application. In this article, we present a generic framework to compare vesselness filters. We use this framework to compare seven gold standard filters. Our experiments are performed on three public datasets: the hepatic Ircad dataset (CT images), the Bullit dataset (brain MRA images) and the synthetic VascuSynth dataset. We analyse the results of these seven filters both quantitatively and qualitatively. In particular, we assess their performances in key areas: the organ of interest, the whole vascular network neighbourhood and the vessel neighbourhood split into several classes, based on their diameters. We also focus on the vessels bifurcations, which are often missed by vesselness filters. We provide the code of the benchmark, which includes up-to-date C++ implementations of the seven filters, as well as the experimental setup (parameter optimization, result analysis, etc.). An online demonstrator is also provided to help the community apply and visually compare these vesselness filters.

Anti-Interference From Noisy Labels: Mean-Teacher-Assisted Confident Learning for Medical Image Segmentation

Manually segmenting medical images is expertise-demanding, time-consuming and laborious. Acquiring massive high-quality labeled data from experts is often infeasible. Unfortunately, without sufficient high-quality pixel-level labels, the usual data-driven learning-based segmentation methods often struggle with deficient training. As a result, we are often forced to collect additional labeled data from multiple sources with varying label qualities. However, directly introducing additional data with low-quality noisy labels may mislead the network training and undesirably offset the efficacy provided by those high-quality labels. To address this issue, we propose a Mean-Teacher-assisted Confident Learning (MTCL) framework constructed by a teacher-student architecture and a label self-denoising process to robustly learn segmentation from a small set of high-quality labeled data and plentiful low-quality noisy labeled data. Particularly, such a synergistic framework is capable of simultaneously and robustly exploiting (i) the additional dark knowledge inside the images of low-quality labeled set via perturbation-based unsupervised consistency, and (ii) the productive information of their low-quality noisy labels via explicit label refinement. Comprehensive experiments on left atrium segmentation with simulated noisy labels and hepatic and retinal vessel segmentation with real-world noisy labels demonstrate the superior segmentation performance of our approach as well as its effectiveness on label denoising.

CT-derived vessel segmentation for analysis of post-radiation therapy changes in vasculature and perfusion

Vessel segmentation in the lung is an ongoing challenge. While many methods have been able to successfully identify vessels in normal, healthy, lungs, these methods struggle in the presence of abnormalities. Following radiotherapy, these methods tend to identify regions of radiographic change due to post-radiation therapytoxicities as vasculature falsely. By combining texture analysis and existing vasculature and masking techniques, we have developed a novel vasculature segmentation workflow that improves specificity in irradiated lung while preserving the sensitivity of detection in the rest of the lung. Furthermore, radiation dose has been shown to cause vascular injury as well as reduce pulmonary function post-RT. This work shows the improvements our novel vascular segmentation method provides relative to existing methods. Additionally, we use this workflow to show a dose dependent radiation-induced change in vasculature which is correlated with previously measured perfusion changes (R² = 0.72) in both directly irradiated and indirectly damaged regions of perfusion. These results present an opportunity to extend non-contrast CT-derived models of functional change following radiation therapy.

Atrous residual convolutional neural network based on U-Net for retinal vessel segmentation

Extracting features of retinal vessels from fundus images plays an essential role in computer-aided diagnosis of diseases, such as diabetes, hypertension, and cerebrovascular diseases. Although a number of deep learning-based methods have been used in this field, the accuracy of retinal vessel segmentation remains challenging due to limited densely annotated data, inter-vessel differences, and structured prediction problems, especially in areas of small blood vessels and the optic disk. In this paper, we propose an ARN model with a atrous block to address these issues, which can avoid the loss of data structure, and enlarge the receptive field, so that each convolution output contains a larger range of information. In addition, we also introduce residual convolution network to increase the network depth and improve the network performance.Some key parameters are used to measure the feasibility of the model, such as sensitivity (Se), specificity (Sp), F1-score (F1), accuracy (Acc), and area under each curve (AUC). Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed methods, which accuracy are 0.9686 on the DRIVE and 0.9746 on the CHASE DB1. The segmentation structure can assist the doctor in diagnosis more effectively.

Impact of Anti-Angiogenic Treatment on Bone Vascularization in a Murine Model of Breast Cancer Bone Metastasis Using Synchrotron Radiation Micro-CT

Bone metastases are frequent complications of breast cancer, facilitating the development of anarchic vascularization and induce bone destruction. Therefore, anti-angiogenic drugs (AAD) have been tested as a therapeutic strategy for the treatment of breast cancer bone metastasis. However, the kinetics of skeletal vascularization in response to tumor invasion under AAD is still partially understood. Therefore, the aim of this study was to explore the effect of AAD on experimental bone metastasis by analyzing the three-dimensional (3D) bone vasculature during metastatic formation and progression. Seventy-three eight-week-old female mice were treated with AAD (bevacizumab, vatalanib, or a combination of both drugs) or the vehicle (placebo) one day after injection with breast cancer cells. Mice were sacrificed eight or 22 days after tumor cell inoculation (time points T1 and T2, respectively). Synchrotron radiation microcomputed tomography (SR-μCT) was used to image bone and blood vessels with a contrast agent. Hence, 3D-bone and vascular networks were simultaneously visualized and quantitatively analyzed. At T1, the trabecular bone volume fraction was significantly increased (p < 0.05) in the combined AAD-treatment group, compared to the placebo- and single AAD-treatment groups. At T2, only the bone vasculature was reduced in the combined AAD-treatment group (p < 0.05), as judged by measurement of the blood vessel thickness. Our data suggest that, at the early stage, combined AAD treatment dampens tumor-induced bone resorption with no detectable effects on bone vessel organization while, at a later stage, it affects the structure of bone microvascularization.

Endothelial and hematopoietic hPSCs differentiation via a hematoendothelial progenitor

Background

hPSC-derived endothelial and hematopoietic cells (ECs and HCs) are an interesting source of cells for tissue engineering. Despite their close spatial and temporal embryonic development, current hPSC differentiation protocols are specialized in only one of these lineages. In this study, we generated a hematoendothelial population that could be further differentiated in vitro to both lineages.

Methods

Two hESCs and one hiPSC lines were differentiated into a hematoendothelial population, hPSC-ECs and blast colonies (hPSC-BCs) via CD144⁺-embryoid bodies (hPSC-EBs). hPSC-ECs were characterized by endothelial colony-forming assay, LDL uptake assay, endothelial activation by TNF-α, nitric oxide detection and Matrigel-based tube formation. Hematopoietic colony-forming cell assay was performed from hPSC-BCs. Interestingly, we identified a hPSC-BC population characterized by the expression of both CD144 and CD45. hPSC-ECs and hPSC-BCs were analyzed by flow cytometry and RT-qPCR; in vivo experiments have been realized by ischemic tissue injury model on a mouse dorsal skinfold chamber and hematopoietic reconstitution in irradiated immunosuppressed mouse from hPSC-ECs and hPSC-EB-CD144⁺, respectively. Transcriptomic analyses were performed to confirm the endothelial and hematopoietic identity of hESC-derived cell populations by comparing them against undifferentiated hESC, among each other’s (e.g. hPSC-ECs vs. hPSC-EB-CD144⁺) and against human embryonic liver (EL) endothelial, hematoendothelial and hematopoietic cell subpopulations.

Results

A hematoendothelial population was obtained after 84 h of hPSC-EBs formation under serum-free conditions and isolated based on CD144 expression. Intrafemorally injection of hPSC-EB-CD144⁺ contributed to the generation of CD45⁺ human cells in immunodeficient mice suggesting the existence of hemogenic ECs within hPSC-EB-CD144⁺. Endothelial differentiation of hPSC-EB-CD144⁺ yields a population of > 95% functional ECs in vitro. hPSC-ECs derived through this protocol participated at the formation of new vessels in vivo in a mouse ischemia model. In vitro, hematopoietic differentiation of hPSC-EB-CD144⁺ generated an intermediate population of > 90% CD43⁺ hPSC-BCs capable to generate myeloid and erythroid colonies. Finally, the transcriptomic analyses confirmed the hematoendothelial, endothelial and hematopoietic identity of hPSC-EB-CD144⁺, hPSC-ECs and hPSC-BCs, respectively, and the similarities between hPSC-BC-CD144⁺CD45⁺, a subpopulation of hPSC-BCs, and human EL hematopoietic stem cells/hematopoietic progenitors.

Conclusion

The present work reports a hPSC differentiation protocol into functional hematopoietic and endothelial cells through a hematoendothelial population. Both lineages were proven to display characteristics of physiological human cells, and therefore, they represent an interesting rapid source of cells for future cell therapy and tissue engineering.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13287-022-02925-w.

Quantification of vascular networks in photoacoustic mesoscopy

Mesoscopic photoacoustic imaging (PAI) enables non-invasive visualisation of tumour vasculature. The visual or semi-quantitative 2D measurements typically applied to mesoscopic PAI data fail to capture the 3D vessel network complexity and lack robust ground truths for assessment of accuracy. Here, we developed a pipeline for quantifying 3D vascular networks captured using mesoscopic PAI and tested the preservation of blood volume and network structure with topological data analysis. Ground truth data of in silico synthetic vasculatures and a string phantom indicated that learning-based segmentation best preserves vessel diameter and blood volume at depth, while rule-based segmentation with vesselness image filtering accurately preserved network structure in superficial vessels. Segmentation of vessels in breast cancer patient-derived xenografts (PDXs) compared favourably to ex vivo immunohistochemistry. Furthermore, our findings underscore the importance of validating segmentation methods when applying mesoscopic PAI as a tool to evaluate vascular networks in vivo.

Automatic quantification of perivascular spaces in T2-weighted images at 7 T MRI

Perivascular spaces (PVS) are believed to be involved in brain waste disposal. PVS are associated with cerebral small vessel disease. At higher field strengths more PVS can be observed, challenging manual assessment. We developed a method to automatically detect and quantify PVS. A machine learning approach identified PVS in an automatically positioned ROI in the centrum semiovale (CSO), based on -resolution T2-weighted TSE scans. Next, 3D PVS tracking was performed in 50 subjects (mean age 62.9 years (range 27-78), 19 male), and quantitative measures were extracted. Maps of PVS density, length, and tortuosity were created. Manual PVS annotations were available to train and validate the automatic method. Good correlation was found between the automatic and manual PVS count: ICC (absolute/consistency) is 0.64/0.75, and Dice similarity coefficient (DSC) is 0.61. The automatic method counts fewer PVS than the manual count, because it ignores the smallest PVS (length <2 mm). For 20 subjects manual PVS annotations of a second observer were available. Compared with the correlation between the automatic and manual PVS, higher inter-observer ICC was observed (0.85/0.88), but DSC was lower (0.49 in 4 persons). Longer PVS are observed posterior in the CSO compared with anterior in the CSO. Higher PVS tortuosity are observed in the center of the CSO compared with the periphery of the CSO. Our fully automatic method can detect PVS in a 2D slab in the CSO, and extract quantitative PVS parameters by performing 3D tracking. This method enables automated quantitative analysis of PVS.

Scalable Deep Learning Algorithm to Compute Percent Pulmonary Contusion among Patients with Rib Fractures

BACKGROUND

Pulmonary contusion exists along a spectrum of severity, yet is commonly binarily classified as present or absent. We aimed to develop a deep learning algorithm to automate percent pulmonary contusion computation and exemplify how transfer learning could facilitate large-scale validation. We hypothesized our deep learning algorithm could automate percent pulmonary contusion computation and that greater percent contusion would be associated with higher odds of adverse inpatient outcomes among patients with rib fractures.

METHODS

We evaluated admission-day chest computed tomography (CT) scans of adults aged ≥18 years admitted to our institution with multiple rib fractures and pulmonary contusions (2010-2020). We adapted a pre-trained convolutional neural network that segments 3-dimensional lung volumes and segmented contused lung parenchyma, pulmonary blood vessels, and computed percent pulmonary contusion. Exploratory analysis evaluated associations between percent pulmonary contusion (quartiles) and odds of mechanical ventilation, mortality, and prolonged hospital length-of-stay using multivariable logistic regression. Sensitivity analysis included pulmonary blood vessel volumes during percent contusion computation.

RESULTS

A total of 332 patients met inclusion criteria (median 5 rib fractures), among whom 28% underwent mechanical ventilation and 6% died. The study population's median (IQR) percent pulmonary contusion was 4(2-8)%. Compared to the lowest quartile of percent pulmonary contusion, each increasing quartile was associated with higher adjusted odds of undergoing mechanical ventilation (OR[95%CI]: 1.5[1.1-2.1]) and prolonged hospitalization (OR[95%CI]: 1.6[1.1-2.2]), but not with mortality (OR[95%CI]: 1.1 [0.6-2.0]. Findings were similar on sensitivity analysis.

CONCLUSION

We developed a scalable deep learning algorithm to automate percent pulmonary contusion calculating using chest CTs of adults admitted with rib fractures. Open code sharing and collaborative research is needed to validate our algorithm and exploratory analysis at large scale. Transfer learning can help harness the full potential of big data and high-performing algorithms to bring precision medicine to the bedside.

LEVEL OF EVIDENCE

IV.

Molecular organization and mechanics of single vimentin filaments revealed by super-resolution imaging

Intermediate filaments (IFs) are involved in key cellular functions including polarization, migration, and protection against large deformations. These functions are related to their remarkable ability to extend without breaking, a capacity that should be determined by the molecular organization of subunits within filaments. However, this structure-mechanics relationship remains poorly understood at the molecular level. Here, using super-resolution microscopy (SRM), we show that vimentin filaments exhibit a ~49-nanometer axial repeat both in cells and in vitro. As unit-length filaments (ULFs) were measured at ~59 nanometers, this demonstrates a partial overlap of ULFs during filament assembly. Using an SRM-compatible stretching device, we also provide evidence that the extensibility of vimentin is due to the unfolding of its subunits and not to their sliding, thus establishing a direct link between the structural organization and its mechanical properties. Overall, our results pave the way for future studies of IF assembly, mechanical, and structural properties in cells.

Automated Extraction of Ground Fissures Due to Coal Mining Subsidence Based on UAV Photogrammetry

Widespread ground fissures caused by coal mining subsidence are a main cause of ecological destruction in coal mining areas, and the rapid monitoring of ground fissures is essential for ecological restoration. Traditional fissure monitoring technologies are time consuming and laborious. Therefore, we developed a method to automatically extract ground fissures from high-resolution UAV images. First, a multiscale Hessian-based enhancement filter was utilized to enhance the ground fissures in grayscale images. Then, a simple single-thresholding operation was applied to segment the enhanced image to generate a binary ground fissure map. Finally, incomplete path opening was performed to eliminate the noises in the fissure extraction results. We selected the N1212 working face of the Ningtiaota Coal Mine in Shenmu County, China, as the study area. The results indicated that the ranges of correctness, completeness, and the kappa coefficient of the extracted results were 66.23–79.00%, 69.03–73.22%, and 67.91–75.88%, respectively. Image resolution is the key factor for successful fissure detection; the method proposed in this paper can extract ground fissures with a width greater than one pixel (2.64 cm), and the detection ratio for fissures with a width greater than two pixels was over 87%. Our research has solved the problem of the rapid monitoring of ground fissures to a certain extent and can act as a valuable tool for ecological restoration in mining areas.

Zebrafish vascular quantification: a tool for quantification of three-dimensional zebrafish cerebrovascular architecture by automated image analysis

Zebrafish transgenic lines and light sheet fluorescence microscopy allow in-depth insights into three-dimensional vascular development in vivo. However, quantification of the zebrafish cerebral vasculature in 3D remains highly challenging. Here, we describe and test an image analysis workflow for 3D quantification of the total or regional zebrafish brain vasculature, called zebrafish vasculature quantification (ZVQ). It provides the first landmark- or object-based vascular inter-sample registration of the zebrafish cerebral vasculature, producing population average maps allowing rapid assessment of intra- and inter-group vascular anatomy. ZVQ also extracts a range of quantitative vascular parameters from a user-specified region of interest, including volume, surface area, density, branching points, length, radius and complexity. Application of ZVQ to 13 experimental conditions, including embryonic development, pharmacological manipulations and morpholino-induced gene knockdown, shows that ZVQ is robust, allows extraction of biologically relevant information and quantification of vascular alteration, and can provide novel insights into vascular biology. To allow dissemination, the code for quantification, a graphical user interface and workflow documentation are provided. Together, ZVQ provides the first open-source quantitative approach to assess the 3D cerebrovascular architecture in zebrafish.

Summary: An image analysis workflow pipeline for 3D quantification of the total or regional zebrafish brain vasculature, called zebrafish vasculature quantification or ZVQ.

Self-Supervised Vessel Enhancement Using Flow-Based Consistencies

Vessel segmentation is an essential task in many clinical applications. Although supervised methods have achieved state-of-art performance, acquiring expert annotation is laborious and mostly limited for two-dimensional datasets with a small sample size. On the contrary, unsupervised methods rely on handcrafted features to detect tube-like structures such as vessels. However, those methods require complex pipelines involving several hyper-parameters and design choices rendering the procedure sensitive, dataset-specific, and not generalizable. We propose a self-supervised method with a limited number of hyper-parameters that is generalizable across modalities. Our method uses tube-like structure properties, such as connectivity, profile consistency, and bifurcation, to introduce inductive bias into a learning algorithm. To model those properties, we generate a vector field that we refer to as a flow. Our experiments on various public datasets in 2D and 3D show that our method performs better than unsupervised methods while learning useful transferable features from unlabeled data. Unlike generic self-supervised methods, the learned features learn vessel-relevant features that are transferable for supervised approaches, which is essential when the number of annotated data is limited.

Retinal Oxygen Metabolism and Haemodynamics in Patients With Multiple Sclerosis and History of Optic Neuritis

Vascular changes and alterations of oxygen metabolism are suggested to be implicated in multiple sclerosis (MS) pathogenesis and progression. Recently developed in vivo retinal fundus imaging technologies provide now an opportunity to non-invasively assess metabolic changes in the neural retina. This study was performed to assess retinal oxygen metabolism, peripapillary capillary density (CD), large vessel density (LVD), retinal nerve fiber layer thickness (RNFLT) and ganglion cell inner plexiform layer thickness (GCIPLT) in patients with diagnosed relapsing multiple sclerosis (RMS) and history of unilateral optic neuritis (ON). 16 RMS patients and 18 healthy controls (HC) were included in this study. Retinal oxygen extraction was modeled using O₂ saturations and Doppler optical coherence tomography (DOCT) derived retinal blood flow (RBF) data. CD and LVD were assessed using optical coherence tomography (OCT) angiography. RNFLT and GCIPLT were measured using structural OCT. Measurements were performed in eyes with (MS+ON) and without (MS-ON) history for ON in RMS patients and in one eye in HC. Total oxygen extraction was lowest in MS+ON (1.8 ± 0.2 μl O₂/min), higher in MS-ON (2.1 ± 0.5 μl O₂/min, p = 0.019 vs. MS+ON) and highest in HC eyes (2.3 ± 0.6 μl O₂/min, p = 0.002 vs. MS, ANOVA p = 0.031). RBF was lower in MS+ON (33.2 ± 6.0 μl/min) compared to MS-ON (38.3 ± 4.6 μl/min, p = 0.005 vs. MS+ON) and HC eyes (37.2 ± 4.7 μl/min, p = 0.014 vs. MS+ON, ANOVA p = 0.010). CD, LVD, RNFLT and GCIPL were significantly lower in MS+ON eyes. The present data suggest that structural alterations in the retina of RMS patients are accompanied by changes in oxygen metabolism, which are more pronounced in MS+ON than in MS-ON eyes. Whether these alterations promote MS onset and progression or occur as consequence of disease warrants further investigation. Clinical Trial Registration: ClinicalTrials.gov registry, NCT03401879.

Hemodynamic changes after intracranial aneurysm growth

OBJECTIVE

For accurate risk assessment of unruptured intracranial aneurysms, it is important to understand the underlying mechanisms that lead to rupture. It is known that hemodynamic anomalies contribute to aneurysm growth and rupture, and that growing aneurysms carry higher rupture risks. However, it is unknown how growth affects hemodynamic characteristics. In this study, the authors assessed how hemodynamic characteristics change over the course of aneurysm growth.

METHODS

The authors included patients with observed aneurysm growth on longitudinal MRA in the period between 2012 and 2016. Patient-specific vascular models were created from baseline and follow-up images. Subsequently, intraaneurysmal hemodynamic characteristics were computed using computational fluid dynamics. The authors computed the normalized wall shear stress, oscillatory shear index, and low shear area to quantify hemodynamic characteristics. Differences between baseline and follow-up measurements were analyzed using paired t-tests.

RESULTS

Twenty-five patients with a total of 31 aneurysms were included. The aneurysm volume increased by a median (IQR) of 26 (9-39) mm3 after a mean follow-up period of 4 (range 0.4-10.9) years. The median wall shear stress decreased significantly after growth. Other hemodynamic parameters did not change significantly, although large individual changes with large variability were observed.

CONCLUSIONS

Hemodynamic characteristics change considerably after aneurysm growth. On average, wall shear stress values decrease after growth, but there is a large variability in hemodynamic changes between aneurysms.

Segmentation and Automatic Identification of Vasculature in Coronary Angiograms

Coronary angiography is the “gold standard” for the diagnosis of coronary heart disease, of which vessel segmentation and identification technologies are paid much attention to. However, because of the characteristics of coronary angiograms, such as the complex and variable morphology of coronary artery structure and the noise caused by various factors, there are many difficulties in these studies. To conquer these problems, we design a preprocessing scheme including block-matching and 3D filtering, unsharp masking, contrast-limited adaptive histogram equalization, and multiscale image enhancement to improve the quality of the image and enhance the vascular structure. To achieve vessel segmentation, we use the C-V model to extract the vascular contour. Finally, we propose an improved adaptive tracking algorithm to realize automatic identification of the vascular skeleton. According to our experiments, the vascular structures can be successfully highlighted and the background is restrained by the preprocessing scheme, the continuous contour of the vessel is extracted accurately by the C-V model, and it is verified that the proposed tracking method has higher accuracy and stronger robustness compared with the existing adaptive tracking method.

A bibliometric of publication trends in medical image segmentation: Quantitative and qualitative analysis

PURPOSE

Medical images are important in diagnosing disease and treatment planning. Computer algorithms that describe anatomical structures that highlight regions of interest and remove unnecessary information are collectively known as medical image segmentation algorithms. The quality of these algorithms will directly affect the performance of the following processing steps. There are many studies about the algorithms of medical image segmentation and their applications, but none involved a bibliometric of medical image segmentation.

METHODS

This bibliometric work investigated the academic publication trends in medical image segmentation technology. These data were collected from the Web of Science (WoS) Core Collection and the Scopus. In the quantitative analysis stage, important visual maps were produced to show publication trends from five different perspectives including annual publications, countries, top authors, publication sources, and keywords. In the qualitative analysis stage, the frequently used methods and research trends in the medical image segmentation field were analyzed from 49 publications with the top annual citation rates.

RESULTS

The analysis results showed that the number of publications had increased rapidly by year. The top related countries include the Chinese mainland, the United States, and India. Most of these publications were conference papers, besides there are also some top journals. The research hotspot in this field was deep learning-based medical image segmentation algorithms based on keyword analysis. These publications were divided into three categories: reviews, segmentation algorithm publications, and other relevant publications. Among these three categories, segmentation algorithm publications occupied the vast majority, and deep learning neural network-based algorithm was the research hotspots and frontiers.

CONCLUSIONS

Through this bibliometric research work, the research hotspot in the medical image segmentation field is uncovered and can point to future research in the field. It can be expected that more researchers will focus their work on deep learning neural network-based medical image segmentation.

Detection of vessel bifurcations in CT scans for automatic objective assessment of deformable image registration accuracy

PURPOSE

Objective assessment of deformable image registration (DIR) accuracy often relies on the identification of anatomical landmarks in image pairs, a manual process known to be extremely time-expensive. The goal of this study is to propose a method to automatically detect vessel bifurcations in images and assess their use for the computation of target registration errors (TREs).

MATERIALS AND METHODS

Three image datasets were retrospectively analyzed. The first dataset included 10 pairs of inhale/exhale phases from lung 4DCTs and full inhale and exhale breath-hold CT scans from 10 patients presenting with chronic obstructive pulmonary disease, with 300 corresponding landmarks available for each case (DIR-Lab). The second dataset included six pairs of inhale/exhale phases from lung 4DCTs (POPI dataset), with 100 pairs of landmarks for each case. The third dataset included 28 pairs of pre/post-radiotherapy liver contrast-enhanced CT scans, each with five manually picked vessel bifurcation correspondences. For all images, the vasculature was autosegmented by computing and thresholding a vesselness image. Images of the vasculature centerline were computed, and bifurcations were detected based on centerline voxel neighbors' count. The vasculature segmentations were independently registered using a Demons algorithm between representations of their surface with distance maps. Detected bifurcations were considered as corresponding when distant by less than 5 mm after vasculature DIR. The selected pairs of bifurcations were used to calculate TRE after registration of the images considering three algorithms: rigid registration, Anaconda, and a Demons algorithm. For comparison with the ground truth, TRE values calculated using the automatically detected correspondences were interpolated in the whole organs to generate TRE maps. The performance of the method in automatically calculating TRE after image registration was quantified by measuring the correlation with the TRE obtained when using the ground truth landmarks.

RESULTS

The median Pearson correlation coefficients between ground truth TRE and corresponding values in the generated TRE maps were r = 0.81 and r = 0.67 for the lung and liver cases, respectively. The correlation coefficients between mean TRE for each case were r = 0.99 and r = 0.64 for the lung and liver cases, respectively.

CONCLUSION

For lungs or liver CT scans DIR, a strong correlation was obtained between TRE calculated using manually picked or landmarks automatically detected with the proposed method. This tool should be particularly useful in studies requiring assessing the reliability of a high number of DIRs.

Fiber finding algorithm using stepwise tracing to identify biopolymer fibers in noisy 3D images

We present a novel fiber finding algorithm (FFA) that will permit researchers to detect and return traces of individual biopolymers. Determining the biophysical properties and structural cues of biopolymers can permit researchers to assess the progression and severity of disease. Confocal microscopy images are a useful method for observing biopolymer structures in three dimensions, but their utility for identifying individual biopolymers is impaired by noise inherent in the acquisition process, including convolution from the point spread function (PSF). The new, iterative FFA we present here 1) measures a microscope's PSF and uses it as a metric for identifying fibers against the background; 2) traces each fiber within a cone angle; and 3) blots out the identified trace before identifying another fiber. Blotting out the identified traces in each iteration allows the FFA to detect and return traces of single fibers accurately and efficiently-even within fiber bundles. We used the FFA to trace unlabeled collagen type I fibers-a biopolymer used to mimic the extracellular matrix in in vitro cancer assays-imaged by confocal reflectance microscopy in three dimensions, enabling quantification of fiber contour length, persistence length, and three-dimensional (3D) mesh size. Based on 3D confocal reflectance microscopy images and the PSF, we traced and measured the fibers to confirm that colder gelation temperatures increased fiber contour length, persistence length, and 3D mesh size-thereby demonstrating the FFA's use in quantifying biopolymers' structural and physical cues from noisy microscope images.

Super-Resolution Imaging of the Actin Cytoskeleton in Living Cells Using TIRF-SIM

Super-resolution (SR) imaging techniques have advanced rapidly in recent years, but only a subset of these techniques is gentle enough to be used by cell biologists to study living cells with minimal photodamage. Our research is focused on studies of the dynamic remodeling of the actin cytoskeleton in living pancreatic beta cells during insulin secretion. These studies require super-resolution light microscopic techniques that are gentle enough to record rapid changes of the actin cytoskeleton in real time. In this chapter, we describe an SR technique that breaks the diffraction limit of the conventional light microscope called TIRF-SIM. Using this SR techniques, we have been able to show that (1) microvilli on pancreatic beta cells translocate in the plane of the plasma membrane and (2) the cortical actin network reorganizes when cells are stimulated to secrete insulin. We describe the FIJI plugins that were used to process and analyze the TIRF-SIM images to obtain quantitative data.

Localized blood-brain barrier opening in infiltrating gliomas with MRI-guided acoustic emissions-controlled focused ultrasound

Pharmacological treatment of gliomas and other brain-infiltrating tumors remains challenging due to limited delivery of most therapeutics across the blood-brain barrier (BBB). Transcranial MRI-guided focused ultrasound (FUS), an emerging technology for noninvasive brain treatments, enables transient opening of the BBB through acoustic activation of circulating microbubbles. Here, we evaluate the safety and utility of transcranial microbubble-enhanced FUS (MB-FUS) for spatially targeted BBB opening in patients with infiltrating gliomas. In this Phase 0 clinical trial (NCT03322813), we conducted comparative and quantitative analyses of FUS exposures (sonications) and their effects on gliomas using MRI, histopathology, microbubble acoustic emissions (harmonic dose [HD]), and fluorescence-guided surgery metrics. Contrast-enhanced MRI and histopathology indicated safe and reproducible BBB opening in all patients. These observations occurred using a power cycling closed feedback loop controller, with the power varying by nearly an order of magnitude on average. This range underscores the need for monitoring and titrating the exposure on a patient-by-patient basis. We found a positive correlation between microbubble acoustic emissions (HD) and MR-evident BBB opening (P = 0.07) and associated interstitial changes (P < 0.01), demonstrating the unique capability to titrate the MB-FUS effects in gliomas. Importantly, we identified a 2.2-fold increase of fluorescein accumulation in MB-FUS-treated compared to untreated nonenhancing tumor tissues (P < 0.01) while accounting for vascular density. Collectively, this study demonstrates the capabilities of MB-FUS for safe, localized, controlled BBB opening and highlights the potential of this technology to improve the surgical and pharmacologic treatment of brain tumors.

Optical coherence tomography of arteriolar diameter and capillary perfusion during spreading depolarizations

Spreading depolarization (SD) is associated with profound oligemia and reduced oxygen availability in the mouse cortex during the depolarization phase. Coincident pial arteriolar constriction has been implicated as the primary mechanism for the oligemia. However, where in the vascular bed the hemodynamic response starts has been unclear. To resolve the origin of the hemodynamic response, we used optical coherence tomography (OCT) to simultaneously monitor changes in the vascular tree from capillary bed to pial arteries in mice during two consecutive SDs 15 minutes apart. We found that capillary flow dropped several seconds before pial arteriolar constriction. Moreover, penetrating arterioles constricted before pial arteries suggesting upstream propagation of constriction. Smaller caliber distal pial arteries constricted stronger than larger caliber proximal arterioles, suggesting that the farther the constriction propagates, the weaker it gets. Altogether, our data indicate that the hemodynamic response to cortical SD originates in the capillary bed.

Survey on Liver Tumour Resection Planning System: Steps, Techniques, and Parameters

Preoperative planning for liver surgical treatments is an essential planning tool that aids in reducing the risks of surgical resection. Based on the computed tomography (CT) images, the resection can be planned before the actual tumour resection surgery. The computer-aided system provides an overview of the spatial relationships of the liver organ and its internal structures, tumours, and vasculature. It also allows for an accurate calculation of the remaining liver volume after resection. The aim of this paper was to review the main stages of the computer-aided system that helps to evaluate the risk of resection during liver cancer surgical treatments. The computer-aided system assists with surgical planning by enabling physicians to get volumetric measurements and visualise the liver, tumours, and surrounding vasculature. In this paper, it is concluded that for accurate planning of tumour resections, the liver organ and its internal structures should be segmented to understand the clear spatial relationship between them, thus allowing for a safer resection. This paper presents the main proposed segmentation techniques for each stage in the computer-aided system, namely the liver organ, tumours, and vessels. From the reviewed methods, it has been found that instead of relying on a single specific technique, a combination of a group of techniques would give more accurate segmentation results. The extracted masks from the segmentation algorithms are fused together to give the surgeons the 3D visualisation tool to study the spatial relationships of the liver and to calculate the required resection planning parameters.

Quantifying the pattern of retinal vascular orientation in diabetic retinopathy using optical coherence tomography angiography

Quantitative imaging using optical coherence tomography angiography (OCTA) could provide objective tools for the detection and characterization of diabetic retinopathy (DR). In this study, an operator combining the second derivative and Gaussian multiscale convolution is applied to identify the retinal orientation at each pixel in the OCTA image. We quantified the pattern of retinal vascular orientation and developed three novel quantitative metrics including vessel preferred orientation, vessel anisotropy, and vessel area. Each of eight 45º sectors of the circular disk centered at the macular region was defined as the region of interest. Significant sectoral differences were observed in the preferred orientation (p < 0.0001) and vessel area (p < 0.0001) in the 34 healthy subjects, whereas vessel anisotropy did not demonstrate a significant difference among the eight sectors (p = 0.054). Differential retinal microvascular orientation patterns were observed between healthy controls (n = 34) and the DR subjects (n = 7). The vessel area characterized from the vascular orientation pattern was shown to be strongly correlated with the traditionally reported vessel density (Pearson R > 0.97, p < 0.0001). With three metrics calculated from the vascular orientation pattern simultaneously and sectorally, our quantitative assessment for retinal microvasculature provides more information than vessel density alone and thereby may enhance the detection of DR. These preliminary results suggest the feasibility and advantage of our vessel orientation-based quantitative approach using OCTA to characterize DR-associated changes in retinal microvasculature.

Scanning electron microscopy preparation of the cellular actin cortex: A quantitative comparison between critical point drying and hexamethyldisilazane drying

The cellular cortex is an approximately 200-nm-thick actin network that lies just beneath the cell membrane. It is responsible for the mechanical properties of cells, and as such, it is involved in many cellular processes, including cell migration and cellular interactions with the environment. To develop a clear view of this dense structure, high-resolution imaging is essential. As one such technique, electron microscopy, involves complex sample preparation procedures. The final drying of these samples has significant influence on potential artifacts, like cell shrinkage and the formation of artifactual holes in the actin cortex. In this study, we compared the three most used final sample drying procedures: critical-point drying (CPD), CPD with lens tissue (CPD-LT), and hexamethyldisilazane drying. We show that both hexamethyldisilazane and CPD-LT lead to fewer artifactual mesh holes within the actin cortex than CPD. Moreover, CPD-LT leads to significant reduction in cell height compared to hexamethyldisilazane and CPD. We conclude that the final drying procedure should be chosen according to the reduction in cell height, and so CPD-LT, or according to the spatial separation of the single layers of the actin cortex, and so hexamethyldisilazane.

Past, present and future role of retinal imaging in neurodegenerative disease

Retinal imaging technology is rapidly advancing and can provide ever-increasing amounts of information about the structure, function and molecular composition of retinal tissue in humans in vivo. Most importantly, this information can be obtained rapidly, non-invasively and in many cases using Food and Drug Administration-approved devices that are commercially available. Technologies such as optical coherence tomography have dramatically changed our understanding of retinal disease and in many cases have significantly improved their clinical management. Since the retina is an extension of the brain and shares a common embryological origin with the central nervous system, there has also been intense interest in leveraging the expanding armamentarium of retinal imaging technology to understand, diagnose and monitor neurological diseases. This is particularly appealing because of the high spatial resolution, relatively low-cost and wide availability of retinal imaging modalities such as fundus photography or OCT compared to brain imaging modalities such as magnetic resonance imaging or positron emission tomography. The purpose of this article is to review and synthesize current research about retinal imaging in neurodegenerative disease by providing examples from the literature and elaborating on limitations, challenges and future directions. We begin by providing a general background of the most relevant retinal imaging modalities to ensure that the reader has a foundation on which to understand the clinical studies that are subsequently discussed. We then review the application and results of retinal imaging methodologies to several prevalent neurodegenerative diseases where extensive work has been done including sporadic late onset Alzheimer's Disease, Parkinson's Disease and Huntington's Disease. We also discuss Autosomal Dominant Alzheimer's Disease and cerebrovascular small vessel disease, where the application of retinal imaging holds promise but data is currently scarce. Although cerebrovascular disease is not generally considered a neurodegenerative process, it is both a confounder and contributor to neurodegenerative disease processes that requires more attention. Finally, we discuss ongoing efforts to overcome the limitations in the field and unmet clinical and scientific needs.

Martins CM, Ferreira MM, Rego VG, Boaro JMC, Cipriano CLS, Silva AC, de Paiva AC, Junior GB, de Almeida JDS, Nunes RA, Mogami R, Gattass M. Segmentation and quantification of COVID-19 infections in CT using pulmonary vessels extraction and deep learning

At the end of 2019, the World Health Organization (WHO) reported pneumonia that started in Wuhan, China, as a global emergency problem. Researchers quickly advanced in research to try to understand this COVID-19 and sough solutions for the front-line professionals fighting this fatal disease. One of the tools to aid in the detection, diagnosis, treatment, and prevention of this disease is computed tomography (CT). CT images provide valuable information on how this new disease affects the lungs of patients. However, the analysis of these images is not trivial, especially when researchers are searching for quick solutions. Detecting and evaluating this disease can be tiring, time-consuming, and susceptible to errors. Thus, in this study, we aim to automatically segment infections caused by COVID19 and provide quantitative measures of these infections to specialists, thus serving as a support tool. We use a database of real clinical cases from Pedro Ernesto University Hospital of the State of Rio de Janeiro, Brazil. The method involves five steps: lung segmentation, segmentation and extraction of pulmonary vessels, infection segmentation, infection classification, and infection quantification. For the lung segmentation and infection segmentation tasks, we propose modifications to the traditional U-Net, including batch normalization, leaky ReLU, dropout, and residual block techniques, and name it as Residual U-Net. The proposed method yields an average Dice value of 77.1% and an average specificity of 99.76%. For quantification of infectious findings, the proposed method achieves results like that of specialists, and no measure presented a value of ρ < 0.05 in the paired t-test. The results demonstrate the potential of the proposed method as a tool to help medical professionals combat COVID-19. fight the COVID-19.

Review of Various Tasks Performed in the Preprocessing Phase of a Diabetic Retinopathy Diagnosis System

Diabetic Retinopathy (DR) is a major cause of blindness in diabetic patients. The increasing population of diabetic patients and difficulty to diagnose it at an early stage are limiting the screening capabilities of manual diagnosis by ophthalmologists. Color fundus images are widely used to detect DR lesions due to their comfortable, cost-effective and non-invasive acquisition procedure. Computer Aided Diagnosis (CAD) of DR based on these images can assist ophthalmologists and help in saving many sight years of diabetic patients. In a CAD system, preprocessing is a crucial phase, which significantly affects its performance. Commonly used preprocessing operations are the enhancement of poor contrast, balancing the illumination imbalance due to the spherical shape of a retina, noise reduction, image resizing to support multi-resolution, color normalization, extraction of a field of view (FOV), etc. Also, the presence of blood vessels and optic discs makes the lesion detection more challenging because these two artifacts exhibit specific attributes, which are similar to those of DR lesions. Preprocessing operations can be broadly divided into three categories: 1) fixing the native defects, 2) segmentation of blood vessels, and 3) localization and segmentation of optic discs. This paper presents a review of the state-of-the-art preprocessing techniques related to three categories of operations, highlighting their significant aspects and limitations. The survey is concluded with the most effective preprocessing methods, which have been shown to improve the accuracy and efficiency of the CAD systems.

Automated segmentation of choroidal layers from 3-dimensional macular optical coherence tomography scans

BACKGROUND

Changes in choroidal thickness are associated with various ocular diseases, and the choroid can be imaged using spectral-domain optical coherence tomography (SD-OCT) and enhanced depth imaging OCT (EDI-OCT).

NEW METHOD

Eighty macular SD-OCT volumes from 80 patients were obtained using the Zeiss Cirrus machine. Eleven additional control subjects had two Cirrus scans done in one visit along with enhanced depth imaging (EDI-OCT) using the Heidelberg Spectralis machine. To automatically segment choroidal layers from the OCT volumes, our graph-theoretic approach was utilized. The segmentation results were compared with reference standards from two independent graders, and the accuracy of automated segmentation was calculated using unsigned/signed border positioning/thickness errors and Dice similarity coefficient (DSC). The repeatability and reproducibility of our choroidal thicknesses were determined by intraclass correlation coefficient (ICC), coefficient of variation (CV), and repeatability coefficient (RC).

RESULTS

The mean unsigned/signed border positioning errors for the choroidal inner and outer surfaces are 3.39 ± 1.26 µm (mean ± standard deviation)/- 1.52 ± 1.63 µm and 16.09 ± 6.21 µm/4.73 ± 9.53 µm, respectively. The mean unsigned/signed choroidal thickness errors are 16.54 ± 6.47 µm/6.25 ± 9.91 µm, and the mean DSC is 0.949 ± 0.025. The ICC (95% confidence interval), CV, RC values are 0.991 (0.977-0.997), 2.48%, 14.25 µm for the repeatability and 0.991 (0.977-0.997), 2.49%, 14.30 µm for the reproducibility studies, respectively.

COMPARISON WITH EXISTING METHOD(S)

The proposed method outperformed our previous method using choroidal vessel segmentation and inter-grader variability.

CONCLUSIONS

This automated segmentation method can reliably measure choroidal thickness using different OCT platforms.

Temporal Relationship Between Visual Field, Retinal and Microvascular Pathology Following 125I-Plaque Brachytherapy for Uveal Melanoma

Purpose

To define the temporal relationship of vascular versus neuronal abnormalities in radiation retinopathy.

Methods

Twenty-five patients with uveal melanoma treated with brachytherapy and sixteen controls were tested. Functional outcome measures included visual acuity and threshold perimetry (HVF 10-2), while structural outcomes included retinal thickness by OCT and vascular measures by OCT angiography and digital fundus photography. The degree of structural abnormality was determined by intereye asymmetry compared with normal subject asymmetry. Diagnostic sensitivity and specificity of each measure were determined using receiver operating characteristic curves. The relationships between the outcome measures were quantified by Spearman correlation. The effect of time from brachytherapy on visual function, retinal layer thickness, and capillary density was also determined.

Results

Within the first 2 years of brachytherapy, outcome measures revealed visual field loss and microvascular abnormalities in 38% and 31% of subjects, respectively. After 2 years, they became more prevalent, increasing to 67% and 67%, respectively, as did retinal thinning (50%). Visual field loss, loss of capillary density, and inner retinal thickness were highly correlated with one another. Diagnostic sensitivity and specificity were highest for abnormalities in digital fundus photography, visual field loss within the central 10°, and decrease in vessel density.

Conclusions

Using quantitative approaches, radiation microvasculopathy and visual field defects were detected earlier than loss of inner retinal structure after brachytherapy. Strong correlations eventually developed between vascular pathology, change in retinal thickness, neuronal dysfunction, and radiation dose. Radiation-induced ischemia seems to be a primary early manifestation of radiation retinopathy preceding visual loss.

Krüppel-like factor 1 is a core cardiomyogenic trigger in zebrafish

Cardiac regeneration requires dedifferentiation and proliferation of mature cardiomyocytes, but the mechanisms underlying this plasticity remain unclear. Here, we identify a potent cardiomyogenic role for Krüppel-like factor 1 (Klf1/Eklf), which is induced in adult zebrafish myocardium upon injury. Myocardial inhibition of Klf1 function does not affect heart development, but it severely impairs regeneration. Transient Klf1 activation is sufficient to expand mature myocardium in uninjured hearts. Klf1 directs epigenetic reprogramming of the cardiac transcription factor network, permitting coordinated cardiomyocyte dedifferentiation and proliferation. Myocardial expansion is supported by Klf1-induced rewiring of mitochondrial metabolism from oxidative respiration to anabolic pathways. Our findings establish Klf1 as a core transcriptional regulator of cardiomyocyte renewal in adult zebrafish hearts.

Automated and semi-automated enhancement, segmentation and tracing of cytoskeletal networks in microscopic images: A review

Cytoskeletal filaments are structures of utmost importance to biological cells and organisms due to their versatility and the significant functions they perform. These biopolymers are most often organised into network-like scaffolds with a complex morphology. Understanding the geometrical and topological organisation of these networks provides key insights into their functional roles. However, this non-trivial task requires a combination of high-resolution microscopy and sophisticated image processing/analysis software. The correct analysis of the network structure and connectivity needs precise segmentation of microscopic images. While segmentation of filament-like objects is a well-studied concept in biomedical imaging, where tracing of neurons and blood vessels is routine, there are comparatively fewer studies focusing on the segmentation of cytoskeletal filaments and networks from microscopic images. The developments in the fields of microscopy, computer vision and deep learning, however, began to facilitate the task, as reflected by an increase in the recent literature on the topic. Here, we aim to provide a short summary of the research on the (semi-)automated enhancement, segmentation and tracing methods that are particularly designed and developed for microscopic images of cytoskeletal networks. In addition to providing an overview of the conventional methods, we cover the recently introduced, deep-learning-assisted methods alongside the advantages they offer over classical methods.

Optical Coherence Tomography Angiography Monitors Cutaneous Wound Healing under Angiogenesis-Promoting Treatment in Diabetic and Non-Diabetic Mice

During wound healing, the rapid re-establishment of a functional microcirculation in the wounded tissue is of utmost importance. We applied optical coherence tomography (OCT) angiography to evaluate vascular remodeling in an excisional wound model in the pinnae of C57BL/6 and db/db mice receiving different proangiogenic topical treatments. Analysis of the high-resolution OCT angiograms, including the four quantitative parameters vessel density, vessel length, number of bifurcations, and vessel tortuosity, revealed changes of the microvasculature and allowed identification of the overlapping wound healing phases hemostasis, inflammation, proliferation, and remodeling. Angiograms acquired in the inflammatory phase in the first days showed a dilation of vessels and recruitment of pre-existing capillaries. In the proliferative phase, angiogenesis with the sprouting of new capillaries into the wound tissue led to an increase of the OCT angiography parameters vessel density, normalized vessel length, number of bifurcations, and vessel tortuosity by 28–47%, 39–52%, 33–48%, and 3–8% versus baseline, respectively. After the peak observed on study days four to seven, the parameters slowly decreased but remained still elevated 18 days after wounding, indicating a continuing remodeling phase. Our study suggests that OCT angiography has the potential to serve as a valuable preclinical research tool in studies investigating impaired vascular remodeling during wound healing and potential new treatment strategies.

Deep learning differentiates between healthy and diabetic mouse ears from optical coherence tomography angiography images

We trained a deep learning algorithm to use skin optical coherence tomography (OCT) angiograms to differentiate between healthy and type 2 diabetic mice. OCT angiograms were acquired with a custom‐built OCT system based on an akinetic swept laser at 1322 nm with a lateral resolution of ∼13 μm and using split‐spectrum amplitude decorrelation. Our data set consisted of 24 stitched angiograms of the full ear, with a size of approximately 8.2 × 8.2 mm, evenly distributed between healthy and diabetic mice. The deep learning classification algorithm uses the ResNet v2 convolutional neural network architecture and was trained on small patches extracted from the full ear angiograms. For individual patches, we obtained a cross‐validated accuracy of 0.925 and an area under the receiver operating characteristic curve (ROC AUC) of 0.974. Averaging over multiple patches extracted from each ear resulted in the correct classification of all 24 ears.

Droplet printing reveals the importance of micron-scale structure for bacterial ecology

Bacteria often live in diverse communities where the spatial arrangement of strains and species is considered critical for their ecology. However, a test of this hypothesis requires manipulation at the fine scales at which spatial structure naturally occurs. Here we develop a droplet-based printing method to arrange bacterial genotypes across a sub-millimetre array. We print strains of the gut bacterium Escherichia coli that naturally compete with one another using protein toxins. Our experiments reveal that toxin-producing strains largely eliminate susceptible non-producers when genotypes are well-mixed. However, printing strains side-by-side creates an ecological refuge where susceptible strains can persist in large numbers. Moving to competitions between toxin producers reveals that spatial structure can make the difference between one strain winning and mutual destruction. Finally, we print different potential barriers between competing strains to understand how ecological refuges form, which shows that cells closest to a toxin producer mop up the toxin and protect their clonemates. Our work provides a method to generate customised bacterial communities with defined spatial distributions, and reveals that micron-scale changes in these distributions can drive major shifts in ecology.

The spatial arrangement of bacterial strains and species within microbial communities is considered crucial for their ecology. Here, Krishna Kumar et al. use a droplet-based printing method to arrange different bacterial genotypes across a sub-millimetre array, and show that micron-scale changes in spatial distributions can drive major shifts in ecology.

Inspection of Transparent Objects with Varying Light Scattering Using a Frangi Filter

This paper proposes a new machine vision method to test the quality of a semi-transparent automotive illuminant component. Difference images of Frangi filtered surface images are used to enhance defect-like image structures. In order to distinguish allowed structures from defective structures, morphological features are extracted and used for a nearest-neighbor-based anomaly score. In this way, it could be demonstrated that a segmentation of occurring defects is possible on transparent illuminant parts. The method turned out to be fast and accurate and is therefore also suited for in-production testing.

Comparison of morphological changes of corneal collagen fibers treated with collagen crosslinking agents using second harmonic generation images

Corneal cross-linking (CXL) is a common surgical procedure used to modify corneal biomechanics and stabilize keratoconus progression which is still under discussion. Its side effects, which are mostly related to anatomical unpredictability and stromal exposure, are the reason for the search for new CXL agents. In this work we have quantitatively evaluated the porcine corneal stroma architecture treated with collagen crosslinking agents such as riboflavin solutions and açai extract, using second harmonic generation microscopy. Aimed at evaluating the morphological changes in the corneal stroma after collagen crosslinking under a CXL chemical agent, a tubeness filter based Hessian matrix to obtain a 3D fiber characterization of the SHG images was applied. The results showed a curling effect and shortening of the collagen fibers treated with açai as compared to the control. They also showed a higher degree of clustering of the collagen fibers with larger empty spaces when compared to the other two groups. We believe that studies such as these presented in this paper are a good direct nondestructive and free labeling evaluation technique that allows the observation of morphologic features of corneas treated with new CXL agents.

Radiomic features based on Hessian index for prediction of prognosis in head-and-neck cancer patients

This study demonstrated the usefulness of radiomic features based on the Hessian index of differential topology for the prediction of prognosis prior to treatment in head-and-neck (HN) cancer patients. The Hessian index, which can indicate tumor heterogeneity with convex, concave, and other points (saddle points), was calculated as the number of negative eigenvalues of the Hessian matrix at each voxel on computed tomography (CT) images. Three types of signatures were constructed in a training cohort (n = 126), one type each from CT conventional features, Hessian index features, and combined features from the conventional and index feature sets. The prognostic value of the signatures were evaluated using statistically significant difference (p value, log-rank test) to compare the survival curves of low- and high-risk groups. In a test cohort (n = 68), the p values of the models built with conventional, index, combined features, and clinical variables were 2.95 [Formula: see text] 10-2, 1.85 [Formula: see text] 10-2, 3.17 [Formula: see text] 10-2, and 1.87 [Formula: see text] 10-3, respectively. When the features were integrated with clinical variables, the p values of conventional, index, and combined features were 3.53 [Formula: see text] 10-3, 1.28 [Formula: see text] 10-3, and 1.45 [Formula: see text] 10-3, respectively. This result indicates that index features could provide more prognostic information than conventional features and further increase the prognostic value of clinical variables in HN cancer patients.

Deep learning toolbox for automated enhancement, segmentation, and graphing of cortical optical coherence tomography microangiograms

Optical coherence tomography angiography (OCTA) is becoming increasingly popular for neuroscientific study, but it remains challenging to objectively quantify angioarchitectural properties from 3D OCTA images. This is mainly due to projection artifacts or "tails" underneath vessels caused by multiple-scattering, as well as the relatively low signal-to-noise ratio compared to fluorescence-based imaging modalities. Here, we propose a set of deep learning approaches based on convolutional neural networks (CNNs) to automated enhancement, segmentation and gap-correction of OCTA images, especially of those obtained from the rodent cortex. Additionally, we present a strategy for skeletonizing the segmented OCTA and extracting the underlying vascular graph, which enables the quantitative assessment of various angioarchitectural properties, including individual vessel lengths and tortuosity. These tools, including the trained CNNs, are made publicly available as a user-friendly toolbox for researchers to input their OCTA images and subsequently receive the underlying vascular network graph with the associated angioarchitectural properties.

Hindbrain and Spinal Cord Contributions to the Cutaneous Sensory Innervation of the Larval Zebrafish Pectoral Fin

Vertebrate forelimbs contain arrays of sensory neuron fibers that transmit signals from the skin to the nervous system. We used the genetic toolkit and optical clarity of the larval zebrafish to conduct a live imaging study of the sensory neurons innervating the pectoral fin skin. Sensory neurons in both the hindbrain and the spinal cord innervate the fin, with most cells located in the hindbrain. The hindbrain somas are located in rhombomere seven/eight, laterally and dorsally displaced from the pectoral fin motor pool. The spinal cord somas are located in the most anterior part of the cord, aligned with myomere four. Single cell reconstructions were used to map afferent processes and compare the distributions of processes to soma locations. Reconstructions indicate that this sensory system breaks from the canonical somatotopic organization of sensory systems by lacking a clear organization with reference to fin region. Arborizations from a single cell branch widely over the skin, innervating the axial skin, lateral fin surface, and medial fin surface. The extensive branching over the fin and the surrounding axial surface suggests that these fin sensory neurons report on general conditions of the fin area rather than providing fine location specificity, as has been demonstrated in other vertebrate limbs. With neuron reconstructions that span the full primary afferent arborization from the soma to the peripheral cutaneous innervation, this neuroanatomical study describes a system of primary sensory neurons and lays the groundwork for future functional studies.

Bone and joint enhancement filtering: Application to proximal femur segmentation from uncalibrated computed tomography datasets

Methods for reliable femur segmentation enable the execution of quality retrospective studies and building of robust screening tools for bone and joint disease. An enhance-and-segment pipeline is proposed for proximal femur segmentation from computed tomography datasets. The filter is based on a scale-space model of cortical bone with properties including edge localization, invariance to density calibration, rotation invariance, and stability to noise. The filter is integrated with a graph cut segmentation technique guided through user provided sparse labels for rapid segmentation. Analysis is performed on 20 independent femurs. Rater proximal femur segmentation agreement was 0.21 mm (average surface distance), 0.98 (Dice similarity coefficient), and 2.34 mm (Hausdorff distance). Manual segmentation added considerable variability to measured failure load and volume (CV_RMS > 5%) but not density. The proposed algorithm considerably improved inter-rater reproducibility for all three outcomes (CV_RMS < 0.5%). The algorithm localized the periosteal surface accurately compared to manual segmentation but with a slight bias towards a smaller volume. Hessian-based filtering and graph cut segmentation localizes the periosteal surface of the proximal femur with comparable accuracy and improved precision compared to manual segmentation.

Quantification of the bone lacunocanalicular network from 3D X-ray phase nanotomography images

There is a growing interest in developing 3D microscopy for the exploration of thick biological tissues. Recently, 3D X-ray nanocomputerised tomography has proven to be a suitable technique for imaging the bone lacunocanalicular network. This interconnected structure is hosting the osteocytes which play a major role in maintaining bone quality through remodelling processes. 3D images have the potential to reveal the architecture of cellular networks, but their quantitative analysis remains a challenge due to the density and complexity of nanometre sized structures and the need to handle and process large datasets, for example, 2048³ voxels corresponding to 32 GB per individual image in our case. In this work, we propose an efficient image processing approach for the segmentation of the network and the extraction of characteristic parameters describing the 3D structure. These parameters include the density of lacunae, the porosity of lacunae and canaliculi, and morphological features of lacunae (volume, surface area, lengths, anisotropy etc.). We also introduce additional parameters describing the local environment of each lacuna and its canaliculi. The method is applied to analyse eight human femoral cortical bone samples imaged by magnified X-ray phase nanotomography with a voxel size of 120 nm, which was found to be a good compromise to resolve canaliculi while keeping a sufficiently large field of view of 246 μm in 3D. The analysis was performed on a total of 2077 lacunae showing an average length, width and depth of 17.1 μm × 9.2 μm × 4.4 μm, with an average number of 58.2 canaliculi per lacuna and a total lacuno-canalicular porosity of 1.12%. The reported descriptive parameters provide information on the 3D organisation of the lacuno-canalicular network in human bones.

MitoSegNet: Easy-to-use Deep Learning Segmentation for Analyzing Mitochondrial Morphology

While the analysis of mitochondrial morphology has emerged as a key tool in the study of mitochondrial function, efficient quantification of mitochondrial microscopy images presents a challenging task and bottleneck for statistically robust conclusions. Here, we present Mitochondrial Segmentation Network (MitoSegNet), a pretrained deep learning segmentation model that enables researchers to easily exploit the power of deep learning for the quantification of mitochondrial morphology. We tested the performance of MitoSegNet against three feature-based segmentation algorithms and the machine-learning segmentation tool Ilastik. MitoSegNet outperformed all other methods in both pixelwise and morphological segmentation accuracy. We successfully applied MitoSegNet to unseen fluorescence microscopy images of mitoGFP expressing mitochondria in wild-type and catp-6 ATP13A2 mutant C. elegans adults. Additionally, MitoSegNet was capable of accurately segmenting mitochondria in HeLa cells treated with fragmentation inducing reagents. We provide MitoSegNet in a toolbox for Windows and Linux operating systems that combines segmentation with morphological analysis.

Whole-brain efferent and afferent connectivity of mouse ventral tegmental area melanocortin-3 receptor neurons

The mesolimbic dopamine (DA) system is involved in the regulation of multiple behaviors, including feeding, and evidence demonstrates that the melanocortin system can act on the mesolimbic DA system to control feeding and other behaviors. The melanocortin-3 receptor (MC3R) is an important component of the melanocortin system, but its overall role is poorly understood. Because MC3Rs are highly expressed in the ventral tegmental area (VTA) and are likely to be the key interaction point between the melanocortin and mesolimbic DA systems, we set out to identify both the efferent projection patterns of VTA MC3R neurons and the location of the neurons providing afferent input to them. VTA MC3R neurons were broadly connected to neurons across the brain but were strongly connected to a discrete set of brain regions involved in the regulation of feeding, reward, and aversion. Surprisingly, experiments using monosynaptic rabies virus showed that proopiomelanocortin (POMC) and agouti-related protein (AgRP) neurons in the arcuate nucleus made few direct synapses onto VTA MC3R neurons or any of the other major neuronal subtypes in the VTA, despite being extensively labeled by general retrograde tracers injected into the VTA. These results greatly contribute to our understanding of the anatomical interactions between the melanocortin and mesolimbic systems and provide a foundation for future studies of VTA MC3R neurons and the circuits containing them in the control of feeding and other behaviors.

Compromised Mitochondrial Protein Import Acts as a Signal for UPRmt

The induction of the mitochondrial unfolded protein response (UPR^mt) results in increased transcription of the gene encoding the mitochondrial chaperone HSP70. We systematically screened the C. elegans genome and identified 171 genes that, when knocked down, induce the expression of an hsp-6 HSP70 reporter and encode mitochondrial proteins. These genes represent many, but not all, mitochondrial processes (e.g., mitochondrial calcium homeostasis and mitophagy are not represented). Knockdown of these genes leads to reduced mitochondrial membrane potential and, hence, decreased protein import into mitochondria. In addition, it induces UPR^mt in a manner that is dependent on ATFS-1 but that is not antagonized by the kinase GCN-2. We propose that compromised mitochondrial protein import signals the induction of UPR^mt and that the mitochondrial targeting sequence of ATFS-1 functions as a sensor for this signal.