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

Inference of Cerebrovascular Topology With Geodesic Minimum Spanning Trees

A vectorial representation of the vascular network that embodies quantitative features-location, direction, scale, and bifurcations-has many potential cardio- and neuro-vascular applications. We present VTrails, an end-to-end approach to extract geodesic vascular minimum spanning trees from angiographic data by solving a connectivity-optimized anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Evaluating real and synthetic vascular images, we compare VTrails against the state-of-the-art ridge detectors for tubular structures by assessing the connectedness of the vesselness map and inspecting the synthesized tensor field. The inferred geodesic trees are then quantitatively evaluated within a topologically aware framework, by comparing the proposed method against popular vascular segmentation tool kits on clinical angiographies. VTrails potentials are discussed towards integrating groupwise vascular image analyses. The performance of VTrails demonstrates its versatility and usefulness also for patient-specific applications in interventional neuroradiology and vascular surgery.

Multiple Hidden Markov Model for Pathological Vessel Segmentation

One of the obstacles that prevent the accurate delineation of vessel boundaries is the presence of pathologies, which results in obscure boundaries and vessel-like structures. Targeting this limitation, we present a novel segmentation method based on multiple Hidden Markov Models. This method works with a vessel axis + cross-section model, which constrains the classifier around the vessel. The vessel axis constraint gives our method the potential to be both physiologically accurate and computationally effective. Focusing on pathological vessels, we reap the benefits of the redundant information embedded in multiple vessel-specific features and the good statistical properties coming with Hidden Markov Model, to cover the widest possible spectrum of complex situations. The performance of our method is evaluated on synthetic complex-structured datasets, where we achieve a 91% high overlap ratio. We also validate the proposed method on a real challenging case, segmentation of pathological abdominal arteries. The performance of our method is promising, since our method yields better results than two state-of-the-art methods on both synthetic datasets and real clinical datasets.

Morphometrical evaluation of the choriocapillaris imaged by swept-source optical coherence tomography angiography

Purpose

To assess the reproducibility of quantitative morphometrical evaluation of the choriocapillaris imaged with swept-source optical coherence tomography angiography (SS-OCTA).

Subjects and methods

This observational, cross-sectional case series included 35 eyes of healthy individuals and 32 eyes of 32 patients. Two images of the fovea were taken using SS-OCTA with 3×3 mm squares. Images of the choriocapillaris within 800×800 pixel squares centered at the fovea were analyzed morphometrically using open-source software "AngioTool" that applies a Gaussian recursive filter and multiscale Hessian enhancement. This program's vessel thickness and intensity parameters can be changed to aid vessel detection. We measured the pairs of images per eye with different parameter sets and calculated the intraclass correlation (ICC) for the morphometrical results. After determining the parameters that produced high reproducibility, we evaluated regional variations in 800×800 pixel mm squares within the fovea.

Results

The ICCs for vessel area, total vessel length, vessel diameter index, and mean lacunarity were over 0.9 using the parameters of "vessel thickness" 3-4 and intensity 15 in the group including all subjects. When measurements were performed using these same parameter values, the vessel density and mean vessel diameter index were 60.5% and 19.1±0.389, respectively. Vessel density, vessel length, vessel diameter index, and mean lacunarity did not change significantly within an 800×800 pixel square centered at the fovea except for the 200×200 pixel square at the foveal center.

Conclusion

SS-OCTA images of the choriocapillaris can be measured with high reproducibility by morphometrical evaluation using open-source software with multiscale Hessian enhancement. Such automated morphometric analysis can provide an objective evaluation of the choriocapillaris.

Appearance of aging signs in differently pigmented facial skin by a novel imaging system

Background

Facial wrinkles, pores, and uneven skin tone are major beauty concerns. There is differential manifestation of aging signs in different ethnic groups. In this regard, studies on Black Africans from the African continent are scarce.

Objective

To investigate facial wrinkles, pores, and skin tone in Black African women from Mauritius Island and elucidate the differences to Caucasian women from France.

Methods

Facial images were taken using the imaging system ColorFace^®. Wrinkles and pores were measured by their length, depth, surface, volume, and number; for skin tone, we measured L*a*b* and calculated ITA, IWA_Newtone, and color homogeneity.

Results

We found good correlations of wrinkle and pore scores with expert ranking done on ColorFace^® images for Caucasians (Spearman's rho = 0.78 and 0.72) and Black Africans (Spearman's rho = 0.86 and 0.65). Caucasians showed more advanced facial signs of aging than Black Africans. Exceptions were vertical lines on upper lip and the depth of pores which were greatest for the Black African subjects. Black Africans had higher heterogeneity scores indicative for uneven skin tone. Luminance (L*) was significantly higher in Caucasians but a* and b* values were significantly higher in the Black African subjects. ITA and IWA_Newtone were significantly higher for Caucasians.

Conclusions

The high correlation between expert ranking and wrinkle and pore measurements prove ColorFace^® a valid imaging system to study skin aging. Our results show that Africans from the African continent show delayed signs of aging compared to Caucasians. Some exceptions suggest that ethnic differences in facial aging are a complex phenomenon.

An Improved Fuzzy Connectedness Method for Automatic Three-Dimensional Liver Vessel Segmentation in CT Images

In this paper, an improved fuzzy connectedness (FC) method was proposed for automatic three-dimensional (3D) liver vessel segmentation in computed tomography (CT) images. The vessel-enhanced image (i.e., vesselness image) was incorporated into the fuzzy affinity function of FC, rather than the intensity image used by traditional FC. An improved vesselness filter was proposed by incorporating adaptive sigmoid filtering and a background-suppressing item. The fuzzy scene of FC was automatically initialized by using the Otsu segmentation algorithm and one single seed generated adaptively, while traditional FC required multiple seeds. The improved FC method was evaluated on 40 cases of clinical CT volumetric images from the 3Dircadb (n=20) and Sliver07 (n=20) datasets. Experimental results showed that the proposed liver vessel segmentation strategy could achieve better segmentation performance than traditional FC, region growing, and threshold level set. Average accuracy, sensitivity, specificity, and Dice coefficient of the improved FC method were, respectively, (96.4 ± 1.1)%, (73.7 ± 7.6)%, (97.4 ± 1.3)%, and (67.3 ± 5.7)% for the 3Dircadb dataset and (96.8 ± 0.6)%, (89.1 ± 6.8)%, (97.6 ± 1.1)%, and (71.4 ± 7.6)% for the Sliver07 dataset. It was concluded that the improved FC may be used as a new method for automatic 3D segmentation of liver vessel from CT images.

Automated Curved and Multiplanar Reformation for Screening of the Proximal Coronary Arteries in MR Angiography

Congenital anomalies of the coronary ostia can lead to sudden death. A screening solution would be useful to prevent adverse outcomes for the affected individuals. To be considered for integration into clinical routine, such a procedure must meet strict constraints in terms of invasiveness, time and user interaction. Imaging must be fast and seamlessly integrable into the clinical process. Non-contrast enhanced coronary magnetic resonance angiography (MRA) is well suited for this. Furthermore, planar reformations proved effective to reduce the acquired volumetric datasets to 2D images. These usually require time consuming user interaction, though. To fulfill the aforementioned challenges, we present a fully automated solution for imaging and reformatting of the proximal coronary arteries which enables rapid screening of these. The proposed pipeline consists of: (I) highly accelerated single breath-hold MRA data acquisition, (II) coronary ostia detection and vessel centerline extraction, and (III) curved planar reformation of the proximal coronary arteries, as well as multiplanar reformation of the coronary ostia. The procedure proved robust and effective in ten volunteer data sets. Imaging of the proximal coronary arteries took 24 ± 5 s and was successful within one breath-hold for all patients. The extracted centerlines achieve an overlap of 0.76 ± 0.18 compared to the reference standard and the average distance of the centerline points from the spherical surface for reformation was 1.1 ± 0.51 mm. The promising results encourage further experiments on patient data, particularly in coronary ostia anomaly screening.

3D Histopathology-a Lung Tissue Segmentation Workflow for Microfocus X-ray-Computed Tomography Scans

Lung histopathology is currently based on the analysis of 2D sections of tissue samples. The use of microfocus X-ray-computed tomography imaging of unstained soft tissue can provide high-resolution 3D image datasets in the range of 2-10 μm without affecting the current diagnostic workflow. Important details of structural features such as the tubular networks of airways and blood vessels are contained in these datasets but are difficult and time-consuming to identify by manual image segmentation. Providing 3D structures permits a better understanding of tissue functions and structural interrelationships. It also provides a more complete picture of heterogeneous samples. In addition, 3D analysis of tissue structure provides the potential for an entirely new level of quantitative measurements of this structure that have previously been based only on extrapolation from 2D sections. In this paper, a workflow for segmenting such 3D images semi-automatically has been created using and extending the ImageJ open-source software and key steps of the workflow have been integrated into a new ImageJ plug-in called LungJ. Results indicate an improved workflow with a modular organization of steps facilitating the optimization for different sample and scan properties with expert input as required. This allows for incremental and independent optimization of algorithms leading to faster segmentation. Representation of the tubular networks in samples of human lung, building on those segmentations, has been demonstrated using this approach.

Whole brain vascular imaging in a mouse model of Alzheimer's disease with two-photon microscopy

Given known correlations between vascular health and cognitive impairment, the development of tools to image microvasculature in the whole brain could help investigate these correlations. We explore the feasibility of using an automated serial two-photon microscope to image fluorescent gelatin-filled whole rodent brains in three-dimensions (3-D) with the goal of carrying group studies. Vascular density (VD) was computed using automatic segmentation combined with coregistration techniques to build a group-level vascular metric in the whole brain. Focusing on the medial prefrontal cortex, cerebral cortex, the olfactory bulb, and the hippocampal formation, we compared the VD of three age groups (2-, 4.5-, and 8-months-old), for both wild type mice and a transgenic model (APP/PS1) with pathology resembling Alzheimer's disease (AD). We report a general loss of VD caused by the aging process with a small VD increase in the diseased animals in the somatomotor and somatosensory cortical regions and the olfactory bulb, partly supported by MRI perfusion data. This study supports previous observations that AD transgenic mice show a higher VD in specific regions compared with WT mice during the early and late stages of the disease (4.5 to 8 months), extending results to whole brain mapping.

DETECTION AND TRACKING OF MIGRATING OLIGODENDROCYTE PROGENITOR CELLS FROM IN VIVO FLUORESCENCE TIME-LAPSE IMAGING DATA

In this work, we develop a fully automatic algorithm named "MCDT" (Migrating Cell Detector and Tracker) for the integrated task of migrating cell detection, segmentation and tracking from in vivo fluorescence time-lapse microscopy imaging data. The interest of detecting and tracking migrating cells arouses from the scientific question in understanding the impact of oligodendrocyte progenitor cells (OPCs) migration in vivo, using advanced microscopy imaging techniques. Current practice of OPC mobility analysis relies on manual labeling, suffering from massive human labor, subjective biases, and weak reproducibility. Existing cell tracking methods have difficulties in analyzing such challenging data due to the extra complexity of in vivo data. Designed for in vivo data, MCDT circumvents the common strong assumption of separable feature distributions between foreground and background. Besides, by focusing on migrating cells (OPCs) only, MCDT relieves the burden of tracking all irrelevant cells correctly, not only accelerating the analysis but also achieving better accuracy in OPCs. Seed based segmentation and tracking by topology-preserved motion estimation endows MCDT with robustness to complex surroundings of the cell under tracking and to occasional inaccurate segmentation in some frames. We tested MCDT on imaging data of transgenic zebrafish larval spinal cord and MCDT showed very promising performance.

Myofibrils in Cardiomyocytes Tend to Assemble Along the Maximal Principle Stress Directions

The mechanisms underlying the spatial organization of self-assembled myofibrils in cardiac tissues remain incompletely understood. By modeling cells as elastic solids under active cytoskeletal contraction, we found a good correlation between the predicted maximal principal stress directions and the in vitro myofibril orientations in individual cardiomyocytes. This implies that actomyosin fibers tend to assemble along the maximal tensile stress (MTS) directions. By considering the dynamics of focal adhesion and myofibril formation in the model, we showed that different patterns of myofibril organizations in mature versus immature cardiomyocytes can be explained as the consequence of the different levels of force-dependent remodeling of focal adhesions. Further, we applied the mechanics model to cell pairs and showed that the myofibril organizations can be regulated by a combination of multiple factors including cell shape, cell-substrate adhesions, and cell-cell adhesions. This mechanics model can guide the rational design in cardiac tissue engineering where recapitulating in vivo myofibril organizations is crucial to the contractile function of the heart.

A Novel Technique for Visualizing and Analyzing the Cerebral Vasculature in Rodents

We introduce a novel protocol to stain, visualize, and analyze blood vessels from the rat and mouse cerebrum. This technique utilizes the fluorescent dye, DiI, to label the lumen of the vasculature followed by perfusion fixation. Following brain extraction, the labeled vasculature is then imaged using wide-field fluorescence microscopy for axial and coronal images and can be followed by regional confocal microscopy. Axial and coronal images can be analyzed using classical angiographic methods for vessel density, length, and other features. We also have developed a novel fractal analysis to assess vascular complexity. Our protocol has been optimized for adult rat, adult mouse, and neonatal mouse studies. The protocol is efficient, can be rapidly completed, stains cerebral vessels with a bright fluorescence, and provides valuable quantitative data. This method has a broad range of applications, and we demonstrate its use to study the vasculature in assorted models of acquired brain injury.

Axis-Guided Vessel Segmentation Using a Self-Constructing Cascade-AdaBoost-SVM Classifier

One major limiting factor that prevents the accurate delineation of vessel boundaries has been the presence of blurred boundaries and vessel-like structures. Overcoming this limitation is exactly what we are concerned about in this paper. We describe a very different segmentation method based on a cascade-AdaBoost-SVM classifier. This classifier works with a vessel axis + cross-section model, which constrains the classifier around the vessel. This has the potential to be both physiologically accurate and computationally effective. To further increase the segmentation accuracy, we organize the AdaBoost classifiers and the Support Vector Machine (SVM) classifiers in a cascade way. And we substitute the AdaBoost classifier with the SVM classifier under special circumstances to overcome the overfitting issue of the AdaBoost classifier. The performance of our method is evaluated on synthetic complex-structured datasets, where we obtain high overlap ratios, around 91%. We also validate the proposed method on one challenging case, segmentation of carotid arteries over real clinical datasets. The performance of our method is promising, since our method yields better results than two state-of-the-art methods on both synthetic datasets and real clinical datasets.

Corneal nerve fiber size adds utility to the diagnosis and assessment of therapeutic response in patients with small fiber neuropathy

Small fiber neuropathy (SFN) is a common feature of many inflammatory diseases, often presenting with pain and disability. SFN is diagnosed using symptoms, thermal threshold testing, and intra-epidermal nerve fiber quantification. Corneal confocal microscopy (CCM) is an ophthalmic imaging technique which non-invasively quantifies corneal nerve fiber (CNF) density, branch density and length, and has comparable diagnostic and superior ability to identify nerve regeneration compared to skin biopsy. CNF size (width and area) depends upon the number of fibers within each nerve, as well as pathology (e.g., swelling), and may provide additional sensitivity to diagnose SFN and identify nerve repair. We have compared the utility of the standard CCM variables employed to CNF size in patients with diabetic sensorimotor polyneuropathy or sarcoidosis-associated SFN, and in patients with SFN following cibinetide administration, an agent which promotes nerve repair. The results show that: 1) CNF width distribution and area depend upon neuropathy severity; 2) CNF area, density, branch density and length possess comparable discriminatory power for diagnosing neuropathy; 3) CNF area is related to length by a quadratic function which is predictive for both healthy subjects and those with SFN; 4) CNF area is a useful variable for quantifying change in CNF morphology.

Progressive Minimal Path Method for Segmentation of 2D and 3D Line Structures

We propose a novel minimal path method for the segmentation of 2D and 3D line structures. Minimal path methods perform propagation of a wavefront emanating from a start point at a speed derived from image features, followed by path extraction using backtracing. Usually, the computation of the speed and the propagation of the wave are two separate steps, and point features are used to compute a static speed. We introduce a new continuous minimal path method which steers the wave propagation progressively using dynamic speed based on path features. We present three instances of our method, using an appearance feature of the path, a geometric feature based on the curvature of the path, and a joint appearance and geometric feature based on the tangent of the wavefront. These features have not been used in previous continuous minimal path methods. We compute the features dynamically during the wave propagation, and also efficiently using a fast numerical scheme and a low-dimensional parameter space. Our method does not suffer from discretization or metrication errors. We performed qualitative and quantitative evaluations using 2D and 3D images from different application areas.

A microtubule-organizing center directing intracellular transport in the early mouse embryo

The centrosome is the primary microtubule-organizing center (MTOC) of most animal cells; however, this organelle is absent during early mammalian development. Therefore, the mechanism by which the mammalian embryo organizes its microtubules (MTs) is unclear. We visualize MT bridges connecting pairs of cells and show that the cytokinetic bridge does not undergo stereotypical abscission after cell division. Instead, it serves as scaffold for the accumulation of the MT minus-end-stabilizing protein CAMSAP3 throughout interphase, thereby transforming this structure into a noncentrosomal MTOC. Transport of the cell adhesion molecule E-cadherin to the membrane is coordinated by this MTOC and is required to form the pluripotent inner mass. Our study reveals a noncentrosomal form of MT organization that directs intracellular transport and is essential for mammalian development.

Cranial shape comparison for automated objective 3D craniosynostosis surgery planning

Virtual planning of open cranial vault reconstruction is used to simulate and define an pre-operative plan for craniosynostosis surgery. However, virtual planning techniques are subjective and dependent on the experience and preferences of the surgical team. To develop an objective automated 3D pre-operative planning technique for open cranial vault reconstructions, we used curvature maps for the shape comparison of the patient's skull with an age-specific reference skull. We created an average skull for the age-group of 11-14 months. Also, we created an artificial test object and selected a cranial CT-scan of an 11 months old trigonocephaly patient as test case. Mesh data of skulls were created using marching cubes and raycasting. Curvature maps were computed using quadric surface fitting. The shape comparison was tested for the test object and the average skull. Finally, shape comparison was performed for the trigonocephalic skull with the average skull. Similar shapes and the area on the patient's skull that maximally corresponded in shape with the reference shape were correctly identified. This study showed that curvature maps allow the comparison of craniosynostosis skulls with age-appropriate average skulls and a first step towards an objective user-independent pre-operative planning technique for open cranial vault reconstructions is made.

Desmoglein 2 promotes vasculogenic mimicry in melanoma and is associated with poor clinical outcome

Tumors can develop a blood supply not only by promoting angiogenesis but also by forming vessel-like structures directly from tumor cells, known as vasculogenic mimicry (VM). Understanding mechanisms that regulate VM is important, as these might be exploitable to inhibit tumor progression. Here, we reveal the adhesion molecule desmoglein 2 (DSG2) as a novel mediator of VM in melanoma. Analysis of patient-derived melanoma cell lines and tumor tissues, and interrogation of The Cancer Genome Atlas (TCGA) data, revealed that DSG2 is frequently overexpressed in primary and metastatic melanomas compared to normal melanocytes. Notably, this overexpression was associated with poor clinical outcome. DSG2⁺ melanoma cells self-organized into tube-like structures on Matrigel, indicative of VM activity, which was inhibited by DSG2 knockdown or treatment with a DSG2-blocking peptide. Mechanistic studies revealed that DSG2 regulates adhesion and cell-cell interactions during tube formation, but does not control melanoma cell viability, proliferation or motility. Finally, analysis of patient tumors revealed a correlation between DSG2 expression, VM network density and expression of VM-associated genes. These studies identify DSG2 as a key regulator of VM activity in human melanoma and suggest this molecule might be therapeutically targeted to reduce tumor blood supply and metastatic spread.

Automated robotic monitoring and inspection of steel structures and bridges

This paper presents visual and 3D structure inspection for steel structures and bridges using a developed climbing robot. The robot can move freely on a steel surface, carry sensors, collect data and then send to the ground station in real-time for monitoring as well as further processing. Steel surface image stitching and 3D map building are conducted to provide a current condition of the structure. Also, a computer vision-based method is implemented to detect surface defects on stitched images. The effectiveness of the climbing robot's inspection is tested in multiple circumstances to ensure strong steel adhesion and successful data collection. The detection method was also successfully evaluated on various test images, where steel cracks could be automatically identified, without the requirement of some heuristic reasoning.

Semi-automated carotid lumen segmentation in computed tomography angiography images

Carotid artery stenosis causes narrowing of carotid lumens and may lead to brain infarction. The purpose of this study was to develop a semi-automated method of segmenting vessel walls, surrounding tissues, and more importantly, the carotid artery lumen by contrast computed tomography angiography (CTA) images and to define the severity of stenosis and present a three-dimensional model of the carotid for visual inspection. In vivo contrast CTA images of 14 patients (7 normal subjects and 7 patients undergoing endarterectomy) were analyzed using a multi-step segmentation algorithm. This method uses graph cut followed by watershed and Hessian based shortest path method in order to extract lumen boundary correctly without being corrupted in the presence of surrounding tissues. Quantitative measurements of the proposed method were compared with those of manual delineation by independent board-certified radiologists. The results were quantitatively evaluated using spatial overlap surface distance indices. A slightly strong match was shown in terms of dice similarity coefficient (DSC) = 0.87±0.08; mean surface distance (Dmsd) = 0.32±0.32; root mean squared surface distance (Drmssd) = 0.49±0.54 and maximum surface distance (Dmax) = 2.14±2.08 between manual and automated segmentation of common, internal and external carotid arteries, carotid bifurcation and stenotic artery, respectively. Quantitative measurements showed that the proposed method has high potential to segment the carotid lumen and is robust to the changes of the lumen diameter and the shape of the stenosis area at the bifurcation site. The proposed method for CTA images provides a fast and reliable tool to quantify the severity of carotid artery stenosis.

Validation of the Gatortail method for accurate sizing of pulmonary vessels from 3D medical images

PURPOSE

Detailed characterization of changes in vessel size is crucial for the diagnosis and management of a variety of vascular diseases. Because clinical measurement of vessel size is typically dependent on the radiologist's subjective interpretation of the vessel borders, it is often prone to high inter- and intra-user variability. Automatic methods of vessel sizing have been developed for two-dimensional images but a fully three-dimensional (3D) method suitable for vessel sizing from volumetric X-ray computed tomography (CT) or magnetic resonance imaging has heretofore not been demonstrated and validated robustly.

METHODS

In this paper, we refined and objectively validated Gatortail, a method that creates a mathematical geometric 3D model of each branch in a vascular tree, simulates the appearance of the virtual vascular tree in a 3D CT image, and uses the similarity of the simulated image to a patient's CT scan to drive the optimization of the model parameters, including vessel size, to match that of the patient. The method was validated with a 2-dimensional virtual tree structure under deformation, and with a realistic 3D-printed vascular phantom in which the diameter of 64 branches were manually measured 3 times each. The phantom was then scanned on a conventional clinical CT imaging system and the images processed with the in-house software to automatically segment and mathematically model the vascular tree, label each branch, and perform the Gatortail optimization of branch size and trajectory. Previously proposed methods of vessel sizing using matched Gaussian filters and tubularity metrics were also tested. The Gatortail method was then demonstrated on the pulmonary arterial tree segmented from a human volunteer's CT scan.

RESULTS

The standard deviation of the difference between the manually measured and Gatortail-based radii in the 3D physical phantom was 0.074 mm (0.087 in-plane pixel units for image voxels of dimension 0.85 × 0.85 × 1.0 mm) over the 64 branches, representing vessel diameters ranging from 1.2 to 7 mm. The linear regression fit gave a slope of 1.056 and an R² value of 0.989. These three metrics reflect superior agreement of the radii estimates relative to previously published results over all sizes tested. Sizing via matched Gaussian filters resulted in size underestimates of >33% over all three test vessels, while the tubularity-metric matching exhibited a sizing uncertainty of >50%. In the human chest CT data set, the vessel voxel intensity profiles with and without branch model optimization showed excellent agreement and improvement in the objective measure of image similarity.

CONCLUSIONS

Gatortail has been demonstrated to be an automated, objective, accurate and robust method for sizing of vessels in 3D non-invasively from chest CT scans. We anticipate that Gatortail, an image-based approach to automatically compute estimates of blood vessel radii and trajectories from 3D medical images, will facilitate future quantitative evaluation of vascular response to disease and environmental insult and improve understanding of the biological mechanisms underlying vascular disease processes.

Removal of Arterial Vessel Contributions in Susceptibility-Weighted Images for Quantification of Normalized Visible Venous Volume in Children with Sickle Cell Disease

Purpose

To evaluate a new postprocessing framework that eliminates arterial vessel signal contributions in the quantification of normalized visible venous volume (NVVV, a ratio between venous and brain volume) in susceptibility-weighted imaging (SWI) exams in patients with sickle cell disease (SCD).

Materials and Methods

We conducted a retrospective study and qualitatively reviewed for hypointense arterial vessel contamination in SWI exams from 21 children with SCD. We developed a postprocessing framework using magnetic resonance angiography in combination with SWI to provide a more accurate quantification of NVVV. NVVV was calculated before and after removing arterial vessel contributions to determine the error from hypointense arterial vessels in quantifying NVVV.

Results

Hypointense arterial vessel contamination was observed in 86% SWI exams and was successfully corrected by the proposed method. The contributions of hypointense arterial vessels in the original SWI were significant and accounted for approximately 33% of the NVVV [uncorrected NVVV = 0.012 ± 0.005 versus corrected NVVV = 0.008 ± 0.003 (mean ± SD), P < 0.01].

Conclusion

Hypointense arterial vessel contamination occurred in the majority of SWI exams and led to a sizeable overestimation of the visible venous volume. A prospective longitudinal study is needed to evaluate if quantitation of NVVV was improved and to assess the role of NVVV as a biomarker of SCD severity or stroke risk.

VIPAR, a quantitative approach to 3D histopathology applied to lymphatic malformations

BACKGROUND

Lack of investigatory and diagnostic tools has been a major contributing factor to the failure to mechanistically understand lymphedema and other lymphatic disorders in order to develop effective drug and surgical therapies. One difficulty has been understanding the true changes in lymph vessel pathology from standard 2D tissue sections.

METHODS

VIPAR (volume information-based histopathological analysis by 3D reconstruction and data extraction), a light-sheet microscopy-based approach for the analysis of tissue biopsies, is based on digital reconstruction and visualization of microscopic image stacks. VIPAR allows semiautomated segmentation of the vasculature and subsequent nonbiased extraction of characteristic vessel shape and connectivity parameters. We applied VIPAR to analyze biopsies from healthy lymphedematous and lymphangiomatous skin.

RESULTS

Digital 3D reconstruction provided a directly visually interpretable, comprehensive representation of the lymphatic and blood vessels in the analyzed tissue volumes. The most conspicuous features were disrupted lymphatic vessels in lymphedematous skin and a hyperplasia (4.36-fold lymphatic vessel volume increase) in the lymphangiomatous skin. Both abnormalities were detected by the connectivity analysis based on extracted vessel shape and structure data. The quantitative evaluation of extracted data revealed a significant reduction of lymphatic segment length (51.3% and 54.2%) and straightness (89.2% and 83.7%) for lymphedematous and lymphangiomatous skin, respectively. Blood vessel length was significantly increased in the lymphangiomatous sample (239.3%).

CONCLUSION

VIPAR is a volume-based tissue reconstruction data extraction and analysis approach that successfully distinguished healthy from lymphedematous and lymphangiomatous skin. Its application is not limited to the vascular systems or skin.

FUNDING

Max Planck Society, DFG (SFB 656), and Cells-in-Motion Cluster of Excellence EXC 1003.

L. fermentum CECT 5716 prevents stress-induced intestinal barrier dysfunction in newborn rats

BACKGROUND

Intestinal epithelial barrier (IEB) dysfunction plays a critical role in various intestinal disorders affecting infants and children, including the development of food allergies and colitis. Recent studies highlighted the role of probiotics in regulating IEB functions and behavior in adults, but their effects in the newborn remain largely unknown. We therefore characterized in rat pups, the impact of Lactobacillus fermentum CECT 5716 (L. fermentum) on stress-induced IEB dysfunction, systemic immune response and exploratory behavior.

METHODS

Newborn rats received daily by gavage either L. fermentum or water. Intestinal permeability to fluorescein sulfonic acid (FSA) and horseradish peroxidase (HRP) was measured following maternal separation (MS) and water avoidance stress (WAS). Immunohistochemical, transcriptomic, and Western blot analysis of zonula occludens-1 (ZO-1) distribution and expression were performed. Anxiety-like and exploratory behavior was assessed using the elevated plus maze test. Cytokine secretion of activated splenocytes was also evaluated.

KEY RESULTS

L. fermentum prevented MS and WAS-induced IEB dysfunction in vivo. L. fermentum reduced permeability to both FSA and HRP in the small intestine but not in the colon. L. fermentum increased expression of ZO-1 and prevented WAS-induced ZO-1 disorganization in ileal epithelial cells. L. fermentum also significantly reduced stress-induced increase in plasma corticosteronemia. In activated splenocytes, L. fermentum enhanced IFNγ secretion while it prevented IL-4 secretion. Finally, L. fermentum increased exploratory behavior.

CONCLUSIONS & INFERENCES

These results suggest that L. fermentum could provide a novel tool for the prevention and/or treatment of gastrointestinal disorders associated with altered IEB functions in the newborn.

3D Gabor wavelet based vessel filtering of photoacoustic images

Filtering and segmentation of vasculature is an important issue in medical imaging. The visualization of vasculature is crucial for the early diagnosis and therapy in numerous medical applications. This paper investigates the use of Gabor wavelet to enhance the effect of vasculature while eliminating the noise due to size, sensitivity and aperture of the detector in 3D Optical Resolution Photoacoustic Microscopy (OR-PAM). A detailed multi-scale analysis of wavelet filtering and Hessian based method is analyzed for extracting vessels of different sizes since the blood vessels usually vary with in a range of radii. The proposed algorithm first enhances the vasculature in the image and then tubular structures are classified by eigenvalue decomposition of the local Hessian matrix at each voxel in the image. The algorithm is tested on non-invasive experiments, which shows appreciable results to enhance vasculature in photo-acoustic images.

An Automatic Detection System of Lung Nodule Based on Multigroup Patch-Based Deep Learning Network

High-efficiency lung nodule detection dramatically contributes to the risk assessment of lung cancer. It is a significant and challenging task to quickly locate the exact positions of lung nodules. Extensive work has been done by researchers around this domain for approximately two decades. However, previous computer-aided detection (CADe) schemes are mostly intricate and time-consuming since they may require more image processing modules, such as the computed tomography image transformation, the lung nodule segmentation, and the feature extraction, to construct a whole CADe system. It is difficult for these schemes to process and analyze enormous data when the medical images continue to increase. Besides, some state of the art deep learning schemes may be strict in the standard of database. This study proposes an effective lung nodule detection scheme based on multigroup patches cut out from the lung images, which are enhanced by the Frangi filter. Through combining two groups of images, a four-channel convolution neural networks model is designed to learn the knowledge of radiologists for detecting nodules of four levels. This CADe scheme can acquire the sensitivity of 80.06% with 4.7 false positives per scan and the sensitivity of 94% with 15.1 false positives per scan. The results demonstrate that the multigroup patch-based learning system is efficient to improve the performance of lung nodule detection and greatly reduce the false positives under a huge amount of image data.

System-wide organization of actin cytoskeleton determines organelle transport in hypocotyl plant cells

The actin cytoskeleton is an essential intracellular filamentous structure that underpins cellular transport and cytoplasmic streaming in plant cells. However, the system-level properties of actin-based cellular trafficking remain tenuous, largely due to the inability to quantify key features of the actin cytoskeleton. Here, we developed an automated image-based, network-driven framework to accurately segment and quantify actin cytoskeletal structures and Golgi transport. We show that the actin cytoskeleton in both growing and elongated hypocotyl cells has structural properties facilitating efficient transport. Our findings suggest that the erratic movement of Golgi is a stable cellular phenomenon that might optimize distribution efficiency of cell material. Moreover, we demonstrate that Golgi transport in hypocotyl cells can be accurately predicted from the actin network topology alone. Thus, our framework provides quantitative evidence for system-wide coordination of cellular transport in plant cells and can be readily applied to investigate cytoskeletal organization and transport in other organisms.

Automated neuron tracing using probability hypothesis density filtering

Motivation

The functionality of neurons and their role in neuronal networks is tightly connected to the cell morphology. A fundamental problem in many neurobiological studies aiming to unravel this connection is the digital reconstruction of neuronal cell morphology from microscopic image data. Many methods have been developed for this, but they are far from perfect, and better methods are needed.

Results

Here we present a new method for tracing neuron centerlines needed for full reconstruction. The method uses a fundamentally different approach than previous methods by considering neuron tracing as a Bayesian multi-object tracking problem. The problem is solved using probability hypothesis density filtering. Results of experiments on 2D and 3D fluorescence microscopy image datasets of real neurons indicate the proposed method performs comparably or even better than the state of the art.

Availability and Implementation

Software implementing the proposed neuron tracing method was written in the Java programming language as a plugin for the ImageJ platform. Source code is freely available for non-commercial use at https://bitbucket.org/miroslavradojevic/phd .

Contact

meijering@imagescience.org.

Supplementary information

Supplementary data are available at Bioinformatics online.

Application of the 3D slicer chest imaging platform segmentation algorithm for large lung nodule delineation

PURPOSE

Accurate segmentation of lung nodules is crucial in the development of imaging biomarkers for predicting malignancy of the nodules. Manual segmentation is time consuming and affected by inter-observer variability. We evaluated the robustness and accuracy of a publically available semiautomatic segmentation algorithm that is implemented in the 3D Slicer Chest Imaging Platform (CIP) and compared it with the performance of manual segmentation.

METHODS

CT images of 354 manually segmented nodules were downloaded from the LIDC database. Four radiologists performed the manual segmentation and assessed various nodule characteristics. The semiautomatic CIP segmentation was initialized using the centroid of the manual segmentations, thereby generating four contours for each nodule. The robustness of both segmentation methods was assessed using the region of uncertainty (δ) and Dice similarity index (DSI). The robustness of the segmentation methods was compared using the Wilcoxon-signed rank test (pWilcoxon<0.05). The Dice similarity index (DSIAgree) between the manual and CIP segmentations was computed to estimate the accuracy of the semiautomatic contours.

RESULTS

The median computational time of the CIP segmentation was 10 s. The median CIP and manually segmented volumes were 477 ml and 309 ml, respectively. CIP segmentations were significantly more robust than manual segmentations (median δCIP = 14ml, median dsiCIP = 99% vs. median δmanual = 222ml, median dsimanual = 82%) with pWilcoxon~10-16. The agreement between CIP and manual segmentations had a median DSIAgree of 60%. While 13% (47/354) of the nodules did not require any manual adjustment, minor to substantial manual adjustments were needed for 87% (305/354) of the nodules. CIP segmentations were observed to perform poorly (median DSIAgree≈50%) for non-/sub-solid nodules with subtle appearances and poorly defined boundaries.

CONCLUSION

Semi-automatic CIP segmentation can potentially reduce the physician workload for 13% of nodules owing to its computational efficiency and superior stability compared to manual segmentation. Although manual adjustment is needed for many cases, CIP segmentation provides a preliminary contour for physicians as a starting point.

Automated Anatomy-Based Tracking of Systemic Arteries in Arbitrary Field-of-View CTA Scans

We propose an automated pipeline for vessel centerline extraction in 3-D computed tomography angiography (CTA) scans with arbitrary fields of view. The principal steps of the pipeline are body part detection, candidate seed selection, segment tracking, which includes centerline extraction, and vessel tree growing. The final tree-growing step can be instantiated in either a semi- or fully automated fashion. The fully automated initialization is carried out using a vessel position regression algorithm. Both semi-and fully automated methods were evaluated on 30 CTA scans comprising neck, abdominal, and leg arteries in multiple fields of view. High detection rates and centerline accuracy values for 38 distinct vessels demonstrate the effectiveness of our approach.

Inferior vena cava segmentation with parameter propagation and graph cut

PURPOSE

The inferior vena cava (IVC) is one of the vital veins inside the human body. Accurate segmentation of the IVC from contrast-enhanced CT images is of great importance. This extraction not only helps the physician understand its quantitative features such as blood flow and volume, but also it is helpful during the hepatic preoperative planning. However, manual delineation of the IVC is time-consuming and poorly reproducible.

METHODS

In this paper, we propose a novel method to segment the IVC with minimal user interaction. The proposed method performs the segmentation block by block between user-specified beginning and end masks. At each stage, the proposed method builds the segmentation model based on information from image regional appearances, image boundaries, and a prior shape. The intensity range and the prior shape for this segmentation model are estimated based on the segmentation result from the last block, or from user- specified beginning mask if at first stage. Then, the proposed method minimizes the energy function and generates the segmentation result for current block using graph cut. Finally, a backward tracking step from the end of the IVC is performed if necessary.

RESULTS

We have tested our method on 20 clinical datasets and compared our method to three other vessel extraction approaches. The evaluation was performed using three quantitative metrics: the Dice coefficient (Dice), the mean symmetric distance (MSD), and the Hausdorff distance (MaxD). The proposed method has achieved a Dice of [Formula: see text], an MSD of [Formula: see text] mm, and a MaxD of [Formula: see text] mm, respectively, in our experiments.

CONCLUSION

The proposed approach can achieve a sound performance with a relatively low computational cost and a minimal user interaction. The proposed algorithm has high potential to be applied for the clinical applications in the future.

Evaluation of Direct Haptic 4D Volume Rendering of Partially Segmented Data for Liver Puncture Simulation

This work presents an evaluation study using a force feedback evaluation framework for a novel direct needle force volume rendering concept in the context of liver puncture simulation. PTC/PTCD puncture interventions targeting the bile ducts have been selected to illustrate this concept. The haptic algorithms of the simulator system are based on (1) partially segmented patient image data and (2) a non-linear spring model effective at organ borders. The primary aim is to quantitatively evaluate force errors caused by our patient modeling approach, in comparison to haptic force output obtained from using gold-standard, completely manually-segmented data. The evaluation of the force algorithms compared to a force output from fully manually segmented gold-standard patient models, yields a low mean of 0.12 N root mean squared force error and up to 1.6 N for systematic maximum absolute errors. Force errors were evaluated on 31,222 preplanned test paths from 10 patients. Only twelve percent of the emitted forces along these paths were affected by errors. This is the first study evaluating haptic algorithms with deformable virtual patients in silico. We prove haptic rendering plausibility on a very high number of test paths. Important errors are below just noticeable differences for the hand-arm system.

Vascular segmentation of head phase-contrast magnetic resonance angiograms using grayscale and shape features

BACKGROUND AND OBJECTIVE

In neurosurgery planning, vascular structures must be predetermined, which can guarantee the security of the operation carried out in the case of avoiding blood vessels. In this paper, an automatic algorithm of vascular segmentation, which combined the grayscale and shape features of the blood vessels, is proposed to extract 3D vascular structures from head phase-contrast magnetic resonance angiography dataset.

METHODS

First, a cost function of mis-segmentation is introduced on the basis of traditional Bayesian statistical classification, and the blood vessel of weak grayscale that tended to be misclassified into background will be preserved. Second, enhanced vesselness image is obtained according to the shape-based multiscale vascular enhancement filter. Third, a new reconstructed vascular image is established according to the fusion of vascular grayscale and shape features using Dempster-Shafer evidence theory; subsequently, the corresponding segmentation structures are obtained. Finally, according to the noise distribution characteristic of the data, segmentation ratio coefficient, which increased linearly from top to bottom, is proposed to control the segmentation result, thereby preventing over-segmentation.

RESULTS

Experiment results show that, through the proposed method, vascular structures can be detected not only when both grayscale and shape features are strong, but also when either of them is strong. Compared with traditional grayscale feature- and shape feature-based methods, it is better in the evaluation of testing in segmentation accuracy, and over-segmentation and under-segmentation ratios.

CONCLUSIONS

The proposed grayscale and shape features combined vascular segmentation is not only effective but also accurate. It may be used for diagnosis of vascular diseases and planning of neurosurgery.

Manipulating fear associations via optogenetic modulation of amygdala inputs to prefrontal cortex

Fear-related disorders are thought to reflect strong and persistent fear memories. The basolateral amygdala (BLA) and the medial prefrontal cortex (mPFC) form strong reciprocal synaptic connections that play a key role in acquisition and extinction of fear memories. While synaptic contacts of BLA cells onto mPFC neurons are likely to play a crucial role in this process, the BLA connects with several additional nuclei within the fear circuit that could relay fear-associated information to the mPFC, and the contribution of direct monosynaptic BLA-mPFC inputs is not yet clear. Here we establish an optogenetic stimulation protocol that induces synaptic depression in BLA-mPFC synapses. In behaving mice, optogenetic high-frequency stimulation of BLA inputs to mPFC interfered with retention of cued associations, attenuated previously acquired cue-associated responses in mPFC neurons and facilitated extinction. Our findings demonstrate the contribution of BLA inputs to mPFC in forming and maintaining cued fear associations.

Gap-free segmentation of vascular networks with automatic image processing pipeline

Current image processing techniques capture large vessels reliably but often fail to preserve connectivity in bifurcations and small vessels. Imaging artifacts and noise can create gaps and discontinuity of intensity that hinders segmentation of vascular trees. However, topological analysis of vascular trees require proper connectivity without gaps, loops or dangling segments. Proper tree connectivity is also important for high quality rendering of surface meshes for scientific visualization or 3D printing. We present a fully automated vessel enhancement pipeline with automated parameter settings for vessel enhancement of tree-like structures from customary imaging sources, including 3D rotational angiography, magnetic resonance angiography, magnetic resonance venography, and computed tomography angiography. The output of the filter pipeline is a vessel-enhanced image which is ideal for generating anatomical consistent network representations of the cerebral angioarchitecture for further topological or statistical analysis. The filter pipeline combined with computational modeling can potentially improve computer-aided diagnosis of cerebrovascular diseases by delivering biometrics and anatomy of the vasculature. It may serve as the first step in fully automatic epidemiological analysis of large clinical datasets. The automatic analysis would enable rigorous statistical comparison of biometrics in subject-specific vascular trees. The robust and accurate image segmentation using a validated filter pipeline would also eliminate operator dependency that has been observed in manual segmentation. Moreover, manual segmentation is time prohibitive given that vascular trees have more than thousands of segments and bifurcations so that interactive segmentation consumes excessive human resources. Subject-specific trees are a first step toward patient-specific hemodynamic simulations for assessing treatment outcomes.

Automatic recognition of subject-specific cerebrovascular trees

PURPOSE

An image filter designed for reconstructing cerebrovascular trees from MR images is described. Current imaging techniques capture major cerebral vessels reliably, but often fail to detect small vessels, whose contrast is suppressed due to limited resolution, slow blood flow rate, and distortions around bifurcations or nonvascular structures. An incomplete view of angioarchitecture limits the information available to physicians.

METHODS

A novel Hessian-based filter for contrast-enhancement in MR angiography and venography for blood vessel reconstruction without introducing dangling segments is presented. We quantify filter performance with receiver-operating-characteristic and dice-similarity-coefficient analysis. Total extracted vascular length, number-of-segments, volume, surface-to-distance, and positional error are calculated for validation.

RESULTS

Reconstruction of cerebrovascular trees from MR images of six volunteers show that the new filter renders more complete representations of subject-specific cerebrovascular networks. Validation with phantom models shows the filter correctly detects blood vessels across all length scales without failing at bifurcations or distorting diameters.

CONCLUSION

The novel filter can potentially improve the diagnosis of cerebrovascular diseases by delivering metrics and anatomy of the vasculature. It also facilitates the automated analysis of large datasets by computing biometrics free of operator subjectivity. The high quality reconstruction enables computational mesh generation for subject-specific hemodynamic simulations. Magn Reson Med 77:398-410, 2017. © 2016 Wiley Periodicals, Inc.

Detecting retinal microaneurysms and hemorrhages with robustness to the presence of blood vessels

BACKGROUND AND OBJECTIVES

Diabetic Retinopathy is the leading cause of blindness in developed countries in the age group 20-74 years. It is characterized by lesions on the retina and this paper focuses on detecting two of these lesions, Microaneurysms and Hemorrhages, which are also known as red lesions. This paper attempts to deal with two problems in detecting red lesions from retinal fundus images: (1) false detections on blood vessels; and (2) different size of red lesions.

METHODS

To deal with false detections on blood vessels, novel filters have been proposed which can distinguish between red lesions and blood vessels. This distinction is based on the fact that vessels are elongated while red lesions are usually circular blob-like structures. The second problem of the different size of lesions is dealt with by applying the proposed filters on patches of different sizes instead of filtering the full image. These patches are obtained by dividing the original image using a grid whose size determines the patch size. Different grid sizes were used and lesion detection results for these grid sizes were combined using Multiple Kernel Learning.

RESULTS

Experiments on a dataset of 143 images showed that proposed filters detected Microaneurysms and Hemorrhages successfully even when these lesions were close to blood vessels. In addition, using Multiple Kernel Learning improved the results when compared to using a grid of one size only. The areas under receiver operating characteristic curve were found to be 0.97 and 0.92 for Microaneurysms and Hemorrhages respectively which are better than the existing related works.

CONCLUSIONS

Proposed filters are robust to the presence of blood vessels and surpass related works in detecting red lesions from retinal fundus images. Improved lesion detection using the proposed approach can help in automatic detection of Diabetic Retinopathy.