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

Multi-Channel Surface EMG Spatio-Temporal Image Enhancement Using Multi-Scale Hessian-Based Filters

Surface electromyography (sEMG) signals acquired with linear electrode array are useful in analyzing muscle anatomy and physiology. Most algorithms for signal processing, detection, and estimation require adequate quality of the input signals, however, multi-channel sEMG signals are commonly contaminated due to several noise sources. The sEMG signal needs to be enhanced prior to the digital signal and image processing to achieve the best results. This study is using spatio-temporal images to represent surface EMG signals. The motor unit action potential (MUAP) in these images looks like a linear structure, making certain angles with the x-axis, depending on the conduction velocity of the MU. A multi-scale Hessian-based filter is used to enhance the linear structure, i.e., the MUAP region, and to suppress the background noise. The proposed framework is compared with some of the existing algorithms using synthetic, simulated, and experimental sEMG signals. Results show improved detection accuracy of the motor unit action potential after the proposed enhancement as a preprocessing step.

Deep Learning and Adaptive Graph-Based Growing Contours for Agricultural Field Extraction

Field mapping and information on agricultural landscapes is of increasing importance for many applications. Monitoring schemes and national cadasters provide a rich source of information but their maintenance and regular updating is costly and labor-intensive. Automatized mapping of fields based on remote sensing imagery may aid in this task and allow for a faster and more regular observation. Although remote sensing has seen extensive use in agricultural research topics, such as plant health monitoring, crop type classification, yield prediction, and irrigation, field delineation and extraction has seen comparatively little research interest. In this study, we present a field boundary detection technique based on deep learning and a variety of image features, and combine it with the graph-based growing contours (GGC) method to extract agricultural fields in a study area in northern Germany. The boundary detection step only requires red, green, and blue (RGB) data and is therefore largely independent of the sensor used. We compare different image features based on color and luminosity information and evaluate their usefulness for the task of field boundary detection. A model based on texture metrics, gradient information, Hessian matrix eigenvalues, and local statistics showed good results with accuracies up to 88.2%, an area under the ROC curve (AUC) of up to 0.94, and F1 score of up to 0.88. The exclusive use of these universal image features may also facilitate transferability to other regions. We further present modifications to the GGC method intended to aid in upscaling of the method through process acceleration with a minimal effect on results. We combined the boundary detection results with the GGC method for field polygon extraction. Results were promising, with the new GGC version performing similarly or better than the original version while experiencing an acceleration of 1.3× to 2.3× on different subsets and input complexities. Further research may explore other applications of the GGC method outside agricultural remote sensing and field extraction.

A Fully Automated Method for Segmenting Arteries and Quantifying Vessel Radii on Magnetic Resonance Angiography Images of Varying Projection Thickness

PURPOSE

Precise quantification of cerebral arteries can help with differentiation and prognostication of cerebrovascular disease. Existing image processing and segmentation algorithms for magnetic resonance angiography (MRA) are limited to the analysis of either 2D maximum intensity projection images or the entire 3D volume. The goal of this study was to develop a fully automated, hybrid 2D-3D method for robust segmentation of arteries and accurate quantification of vessel radii using MRA at varying projection thicknesses.

METHODS

A novel algorithm that employs an adaptive Frangi filter for segmentation of vessels followed by estimation of vessel radii is presented. The method was evaluated on MRA datasets and corresponding manual segmentations from three healthy subjects for various projection thicknesses. In addition, the vessel metrics were computed in four additional subjects. Three synthetically generated angiographic datasets resembling brain vasculature were also evaluated under different noise levels. Dice similarity coefficient, Jaccard Index, F-score, and concordance correlation coefficient were used to measure the segmentation accuracy of manual versus automatic segmentation.

RESULTS

Our new adaptive filter rendered accurate representations of vessels, maintained accurate vessel radii, and corresponded better to manual segmentation at different projection thicknesses than prior methods. Validation with synthetic datasets under low contrast and noisy conditions revealed accurate quantification of vessels without distortions.

CONCLUSION

We have demonstrated a method for automatic segmentation of vascular trees and the subsequent generation of a vessel radii map. This novel technique can be applied to analyze arterial structures in healthy and diseased populations and improve the characterization of vascular integrity.

Melanoma Skin Cancer Detection Method Based on Adaptive Principal Curvature, Colour Normalisation and Feature Extraction with the ABCD Rule

According to statistics of the American Cancer Society, in 2015, there are about 91,270 American adults diagnosed with melanoma of the skin. For the European Union, there are over 90,000 new cases of melanoma annually. Although melanoma only accounts for about 1% of all skin cancers, it causes most of the skin cancer deaths. Melanoma is considered one of the fastest-growing forms of skin cancer, and hence the early detection is crucial, as early detection is helpful and can provide strong recommendations for specific and suitable treatment regimens. In this work, we propose a method to detect melanoma skin cancer with automatic image processing techniques. Our method includes three stages: pre-process images of skin lesions by adaptive principal curvature, segment skin lesions by the colour normalisation and extract features by the ABCD rule. We provide experimental results of the proposed method on the publicly available International Skin Imaging Collaboration (ISIC) skin lesions dataset. The acquired results on melanoma skin cancer detection indicates that the proposed method has high accuracy, and overall, a good performance: for the segmentation stage, the accuracy, Dice, Jaccard scores are 96.6%, 93.9% and 88.7%, respectively; and for the melanoma detection stage, the accuracy is up to 100% for a selected subset of the ISIC dataset.

Arp2/3 and Mena/VASP Require Profilin 1 for Actin Network Assembly at the Leading Edge

Cells have many types of actin structures, which must assemble from a common monomer pool. Yet, it remains poorly understood how monomers are distributed to and shared between different filament networks. Simplified model systems suggest that monomers are limited and heterogeneous, which alters actin network assembly through biased polymerization and internetwork competition. However, less is known about how monomers influence complex actin structures, where different networks competing for monomers overlap and are functionally interdependent. One example is the leading edge of migrating cells, which contains filament networks generated by multiple assembly factors. The leading edge dynamically switches between the formation of different actin structures, such as lamellipodia or filopodia, by altering the balance of these assembly factors' activities. Here, we sought to determine how the monomer-binding protein profilin 1 (PFN1) controls the assembly and organization of actin in mammalian cells. Actin polymerization in PFN1 knockout cells was severely disrupted, particularly at the leading edge, where both Arp2/3 and Mena/VASP-based filament assembly was inhibited. Further studies showed that in the absence of PFN1, Arp2/3 no longer localizes to the leading edge and Mena/VASP is non-functional. Additionally, we discovered that discrete stages of internetwork competition and collaboration between Arp2/3 and Mena/VASP networks exist at different PFN1 concentrations. Low levels of PFN1 caused filopodia to form exclusively at the leading edge, while higher concentrations inhibited filopodia and favored lamellipodia and pre-filopodia bundles. These results demonstrate that dramatic changes to actin architecture can be made simply by modifying PFN1 availability.

Longitudinal optical coherence tomography imaging of tissue repair and microvasculature regeneration and function after targeted cerebral ischemia

SIGNIFICANCE

Understanding how the brain recovers from cerebral tissue and vascular damage after an ischemic event can help develop new therapeutic strategies for the treatment of stroke.

AIM

We investigated cerebral tissue repair and microvasculature regeneration and function after a targeted ischemic stroke.

APPROACH

Following photothrombosis occlusion of microvasculature, chronic optical coherence tomography (OCT)-based angiography was used to track ischemic tissue repair and microvasculature regeneration at three different cortical depths and up to 28 days in awake animals. Capillary network orientation analysis was performed to study the structural pattern of newly formed microvasculature. Based on the time-resolved OCT-angiography, we also investigated capillary stalling, which is likely related to ischemic stroke-induced inflammation.

RESULTS

Deeper cerebral tissue was found to have a larger ischemic area than shallower regions at any time point during the course of poststroke recovery, which suggests that cerebral tissue located deep in the cortex is more vulnerable. Regenerated microvasculature had a highly organized pattern at all cortical depths with a higher degree of structural reorganization in deeper regions. Additionally, capillary stalling event analysis revealed that cerebral ischemia augmented stalling events considerably.

CONCLUSION

Longitudinal OCT angiography reveals that regenerated capillary network has a highly directional pattern and an increased density and incidence of capillary stalling event.

Machine learning analysis of whole mouse brain vasculature

Tissue clearing methods enable the imaging of biological specimens without sectioning. However, reliable and scalable analysis of large imaging datasets in three dimensions remains a challenge. Here we developed a deep learning-based framework to quantify and analyze brain vasculature, named Vessel Segmentation & Analysis Pipeline (VesSAP). Our pipeline uses a convolutional neural network (CNN) with a transfer learning approach for segmentation and achieves human-level accuracy. By using VesSAP, we analyzed the vascular features of whole C57BL/6J, CD1 and BALB/c mouse brains at the micrometer scale after registering them to the Allen mouse brain atlas. We report evidence of secondary intracranial collateral vascularization in CD1 mice and find reduced vascularization of the brainstem in comparison to the cerebrum. Thus, VesSAP enables unbiased and scalable quantifications of the angioarchitecture of cleared mouse brains and yields biological insights into the vascular function of the brain.

Assessment of the human bone lacuno-canalicular network at the nanoscale and impact of spatial resolution

Recently, increasing attention has been given to the study of osteocytes, the cells that are thought to play an important role in bone remodeling and in the mechanisms of bone fragility. The interconnected osteocyte system is deeply embedded inside the mineralized bone matrix and lies within a closely fitted porosity known as the lacuno-canalicular network. However, quantitative data on human samples remain scarce, mostly measured in 2D, and there are gaps to be filled in terms of spatial resolution. In this work, we present data on femoral samples from female donors imaged with isotropic 3D spatial resolution by magnified X-ray phase nano computerized-tomography. We report quantitative results on the 3D structure of canaliculi in human femoral bone imaged with a voxel size of 30 nm. We found that the lacuno-canalicular porosity occupies on average 1.45% of the total tissue volume, the ratio of the canalicular versus lacunar porosity is about 37.7%, and the primary number of canaliculi stemming from each lacuna is 79 on average. The examination of this number at different distances from the surface of the lacunae demonstrates branching in the canaliculi network. We analyzed the impact of spatial resolution on quantification by comparing parameters extracted from the same samples imaged with 120 nm and 30 nm voxel sizes. To avoid any bias related to the analysis region, the volumes at 120 nm and 30 nm were registered and cropped to the same field of view. Our results show that the measurements at 120 and 30 nm are strongly correlated in our data set but that the highest spatial resolution provides more accurate information on the canaliculi network and its branching properties.

Vasculature-Driven Biomechanical Deformable Image Registration of Longitudinal Liver Cholangiocarcinoma Computed Tomographic Scans

PURPOSE

Deformable image registration (DIR) of longitudinal liver cancer computed tomographic (CT) images can be challenging owing to anatomic changes caused by radiation therapy (RT) or disease progression. We propose a workflow for the DIR of longitudinal contrast-enhanced CT scans of liver cancer based on a biomechanical model of the liver driven by boundary conditions on the liver surface and centerline of an autosegmentation of the vasculature.

METHODS AND MATERIALS

Pre- and post-RT CT scans acquired with a median gap of 112 (32-217) days for 28 patients who underwent RT for intrahepatic cholangiocarcinoma were retrospectively analyzed. For each patient, 5 corresponding anatomic landmarks in pre- and post-RT scans were identified in the liver by a clinical expert for evaluation of the accuracy of different DIR strategies. The first strategy corresponded to the use of a biomechanical model-based DIR method with boundary conditions specified on the liver surface (BM_DIR). The second strategy corresponded to the use of an expansion of BM_DIR consisting of the auto-segmentation of the liver vasculature to determine additional boundary conditions in the biomechanical model (BM_DIR_VBC). The 2 strategies were also compared with an intensity-based DIR strategy using a Demons algorithms.

RESULTS

The group mean target registration errors were 12.4 ± 7.5, 7.7 ± 3.7 and 4.4 ± 2.5 mm, for the Demons, BM_DIR and BM_DIR_VBC, respectively.

CONCLUSIONS

In regard to the large and complex deformation observed in this study and the achieved accuracy of 4.4 mm, the proposed BM_DIR_VBC method might reveal itself as a valuable tool in future studies on the relationship between delivered dose and treatment outcome.

Autophagy compensates for defects in mitochondrial dynamics

Compromising mitochondrial fusion or fission disrupts cellular homeostasis; however, the underlying mechanism(s) are not fully understood. The loss of C. elegans fzo-1MFN results in mitochondrial fragmentation, decreased mitochondrial membrane potential and the induction of the mitochondrial unfolded protein response (UPRmt). We performed a genome-wide RNAi screen for genes that when knocked-down suppress fzo-1MFN(lf)-induced UPRmt. Of the 299 genes identified, 143 encode negative regulators of autophagy, many of which have previously not been implicated in this cellular quality control mechanism. We present evidence that increased autophagic flux suppresses fzo-1MFN(lf)-induced UPRmt by increasing mitochondrial membrane potential rather than restoring mitochondrial morphology. Furthermore, we demonstrate that increased autophagic flux also suppresses UPRmt induction in response to a block in mitochondrial fission, but not in response to the loss of spg-7AFG3L2, which encodes a mitochondrial metalloprotease. Finally, we found that blocking mitochondrial fusion or fission leads to increased levels of certain types of triacylglycerols and that this is at least partially reverted by the induction of autophagy. We propose that the breakdown of these triacylglycerols through autophagy leads to elevated metabolic activity, thereby increasing mitochondrial membrane potential and restoring mitochondrial and cellular homeostasis.

Automatic tongue surface extraction from three-dimensional ultrasound vocal tract images

Three-dimensional (3D/4D) ultrasound (US) imaging of the tongue has emerged as a useful instrument for articulatory studies. However, extracting quantitative measurements of the shape of the tongue surface remains challenging and time-consuming. In response to these challenges, this paper documents and evaluates the first automated method for extracting tongue surfaces from 3D/4D US data. The method draws on established methods in computer vision, and combines image phase symmetry measurements, eigen-analysis of the image Hessian matrix, and a fast marching method for surface evolution towards the automatic detection of the sheet-like surface of the tongue amidst noisy US data. The method was tested on US recordings from eight speakers and the resulting automatically extracted tongue surfaces were generally found to lie within 1 to 2 mm from their corresponding manually delineated surfaces in terms of mean-sum-of-distances error. Further experiments demonstrate that the accuracy of 2D midsagittal tongue contour extraction is also improved using 3D data and methods. This is likely because the additional information afforded by 3D US compared to 2D US images strongly constrains the possible location of the midsagittal contour. Thus, the proposed method seems appropriate for immediate practical use in the analysis of 3D/4D US recordings of the tongue.

Super resolution measurement of collagen fibers in biological samples: Validation of a commercial solution for multiphoton microscopy

Multiphoton microscopy is a powerful, non-invasive technique to image biological specimens. One current limitation of multiphoton microscopy is resolution as many of the biological molecules and structures investigated by research groups are similar in size or smaller than the diffraction limit. To date, the combination of multiphoton and super-resolution imaging has proved technically challenging for biology focused laboratories to implement. Here we validate that the commercial super-resolution Airyscan detector from ZEISS, which is based on image scanning microscopy, can be integrated under warranty with a pulsed multi-photon laser to enable multiphoton microscopy with super-resolution. We demonstrate its biological application in two different imaging modalities, second harmonic generation (SHG) and two-photon excited fluorescence (TPEF), to measure the fibre thicknesses of collagen and elastin molecules surpassing the diffraction limit by a factor of 1.7±0.3x and 1.4±0.3x respectively, in human heart and lung tissues, and 3-dimensional in vitro models. We show that enhanced resolution and signal-to-noise of SHG using the Airyscan compared to traditional GaAs detectors allows for automated and precise measurement of collagen fibres using texture analysis in biological tissues.

Mapping the Fine-Scale Organization and Plasticity of the Brain Vasculature

The cerebral vasculature is a dense network of arteries, capillaries, and veins. Quantifying variations of the vascular organization across individuals, brain regions, or disease models is challenging. We used immunolabeling and tissue clearing to image the vascular network of adult mouse brains and developed a pipeline to segment terabyte-sized multichannel images from light sheet microscopy, enabling the construction, analysis, and visualization of vascular graphs composed of over 100 million vessel segments. We generated datasets from over 20 mouse brains, with labeled arteries, veins, and capillaries according to their anatomical regions. We characterized the organization of the vascular network across brain regions, highlighting local adaptations and functional correlates. We propose a classification of cortical regions based on the vascular topology. Finally, we analysed brain-wide rearrangements of the vasculature in animal models of congenital deafness and ischemic stroke, revealing that vascular plasticity and remodeling adopt diverging rules in different models.

A workflow for automated segmentation of the liver surface, hepatic vasculature and biliary tree anatomy from multiphase MR images

Accurate assessment of 3D models of patient-specific anatomy of the liver, including underlying hepatic and biliary tree, is critical for preparation and safe execution of complex liver resections, especially due to high variability of biliary and hepatic artery anatomies. Dynamic MRI with hepatospecific contrast agents is currently the only type of diagnostic imaging that provides all anatomical information required for generation of such a model, yet there is no information in the literature on how the complete 3D model can be generated automatically. In this work, a new automated segmentation workflow for extraction of patient-specific 3D model of the liver, hepatovascular and biliary anatomy from a single multiphase MRI acquisition is developed and quantitatively evaluated. The workflow incorporates course 4D k-means clustering estimation and geodesic active contour refinement of the liver boundary, based on organ's characteristic uptake of gadolinium contrast agents overtime. Subsequently, hepatic vasculature and biliary ducts segmentations are performed using multiscale vesselness filters. The algorithm was evaluated using 15 test datasets of patients with liver malignancies of various histopathological types. It showed good correlation with expert manual segmentation, resulting in an average of 1.76 ± 2.44 mm Hausdorff distance for the liver boundary, and 0.58 ± 0.72 and 1.16 ± 1.98 mm between centrelines of biliary ducts and liver veins, respectively. A workflow for automatic segmentation of the liver, hepatic vasculature and biliary anatomy from a single diagnostic MRI acquisition was developed. This enables automated extraction of 3D models of patient-specific liver anatomy, and may facilitating better perception of organ's anatomy during preparation and execution of liver surgeries. Additionally, it may help to reduce the incidence of intraoperative biliary duct damage due to an unanticipated variation in the anatomy.

Intravital Vascular Phototheranostics and Real-Time Circulation Dynamics of Micro- and Nanosized Erythrocyte-Derived Carriers

Erythrocyte-based carriers can serve as theranostic platforms for delivery of imaging and therapeutic payloads. Engineering these carriers at micro- or nanoscales makes them potentially useful for broad clinical applications ranging from vascular diseases to tumor theranostics. Longevity of these carriers in circulation is important in delivering a sufficient amount of their payloads to the target. We have investigated the circulation dynamics of micro (∼4.95 μm diameter) and nano (∼91 nm diameter) erythrocyte-derived carriers in real time using near-infrared fluorescence imaging, and evaluated the effectiveness of such carrier systems in mediating photothermolysis of cutaneous vasculature in mice. Fluorescence emission half-lives of micro- and nanosized carriers in response to a single intravenous injection were ∼49 and ∼15 min, respectively. A single injection of microsized carriers resulted in a 3-fold increase in signal-to-noise ratio that remained nearly persistent over 1 h of imaging time. Our results also suggest that a second injection of the carriers 7 days later can induce a transient inflammatory response, as manifested by the apparent leakage of the carriers into the perivascular tissue. The administration of the carriers into the mice vasculature reduced the threshold laser fluence to induce photothermolysis of blood vessels from >65 to 20 J/cm2. We discuss the importance of membrane physicochemical and mechanical characteristics in engineering erythrocyte-derived carriers and considerations for their clinical translation.

High-throughput three-dimensional visualization of root system architecture of rice using X-ray computed tomography

BACKGROUND

X-ray computed tomography (CT) allows us to visualize root system architecture (RSA) beneath the soil, non-destructively and in a three-dimensional (3-D) form. However, CT scanning, reconstruction processes, and root isolation from X-ray CT volumes, take considerable time. For genetic analyses, such as quantitative trait locus mapping, which require a large population size, a high-throughput RSA visualization method is required.

RESULTS

We have developed a high-throughput process flow for the 3-D visualization of rice (Oryza sativa) RSA (consisting of radicle and crown roots), using X-ray CT. The process flow includes use of a uniform particle size, calcined clay to reduce the possibility of visualizing non-root segments, use of a higher tube voltage and current in the X-ray CT scanning to increase root-to-soil contrast, and use of a 3-D median filter and edge detection algorithm to isolate root segments. Using high-performance computing technology, this analysis flow requires only 10 min (33 s, if a rough image is acceptable) for CT scanning and reconstruction, and 2 min for image processing, to visualize rice RSA. This reduced time allowed us to conduct the genetic analysis associated with 3-D RSA phenotyping. In 2-week-old seedlings, 85% and 100% of radicle and crown roots were detected, when 16 cm and 20 cm diameter pots were used, respectively. The X-ray dose per scan was estimated at < 0.09 Gy, which did not impede rice growth. Using the developed process flow, we were able to follow daily RSA development, i.e., 4-D RSA development, of an upland rice variety, over 3 weeks.

CONCLUSIONS

We developed a high-throughput process flow for 3-D rice RSA visualization by X-ray CT. The X-ray dose assay on plant growth has shown that this methodology could be applicable for 4-D RSA phenotyping. We named the RSA visualization method 'RSAvis3D' and are confident that it represents a potentially efficient application for 3-D RSA phenotyping of various plant species.

A Comparison of Peripheral Imaging Technologies for Bone and Muscle Quantification: A Review of Segmentation Techniques

Musculoskeletal science has developed many overlapping branches, necessitating specialists from 1 area of focus to often require the expertise in others. In terms of imaging, this means obtaining a comprehensive illustration of bone, muscle, and fat tissues. There is currently a lack of a reliable resource for end users to learn about these tissues' imaging and quantification techniques together. An improved understanding of these tissues has been an important progression toward better prediction of disease outcomes and better elucidation of their interaction with frailty, aging, and metabolic disorders. Over the last decade, there have been major advances into the image acquisition and segmentation of bone, muscle, and fat features using computed tomography (CT), magnetic resonance imaging (MRI), and peripheral modules of these systems. Dedicated peripheral quantitative musculoskeletal imaging systems have paved the way for mobile research units, lower cost clinical research facilities, and improved resolution per unit cost paid. The purpose of this review was to detail the segmentation techniques available for each of these peripheral CT and MRI modalities and to describe advances in segmentation methods as applied to study longitudinal changes and treatment-related dynamics. Although the peripheral CT units described herein have established feasible standardized protocols that users have adopted globally, there remain challenges in standardizing MRI protocols for bone and muscle imaging.

Investigating the effects of dexamethasone on blood-brain barrier permeability and inflammatory response following focused ultrasound and microbubble exposure

Rationale: Clinical trials are currently underway to test the safety and efficacy of delivering therapeutic agents across the blood-brain barrier (BBB) using focused ultrasound and microbubbles (FUS+MBs). While acoustic feedback control strategies have largely minimized the risk of overt tissue damage, transient induction of inflammatory processes have been observed following sonication in preclinical studies. The goal of this work was to explore the potential of post-sonication dexamethasone (DEX) administration as a means to mitigate treatment risk. Vascular permeability, inflammatory protein expression, blood vessel growth, and astrocyte activation were assessed. Methods: A single-element focused transducer (transmit frequency = 580 kHz) and Definity^TM microbubbles were used to increase BBB permeability unilaterally in the dorsal hippocampi of adult male rats. Sonicating pressure was calibrated based on ultraharmonic emissions. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was used to quantitatively assess BBB permeability at 15 min (baseline) and 2 hrs following sonication. DEX was administered following baseline imaging and at 24 hrs post-FUS+MB exposure. Expression of key inflammatory proteins were assessed at 2 days, and astrocyte activation and blood vessel growth were assessed at 10 days post-FUS+MB exposure. Results: Compared to saline-treated control animals, DEX administration expedited the restoration of BBB integrity at 2 hrs, and significantly limited the production of key inflammation-related proteins at 2 days, following sonication. Indications of FUS+MB-induced astrocyte activation and vascular growth were diminished at 10 days in DEX-treated animals, compared to controls. Conclusions: These results suggest that DEX provides a means of modulating the duration of BBB permeability enhancement and may reduce the risk of inflammation-induced tissue damage, increasing the safety profile of this drug-delivery strategy. This effect may be especially relevant in scenarios for which the goal of treatment is to restore or preserve neural function and multiple sonications are required.

Neuron-based high-content assay and screen for CNS active mitotherapeutics

Impaired mitochondrial dynamics and function are hallmarks of many neurological and psychiatric disorders, but direct screens for mitotherapeutics using neurons have not been reported. We developed a multiplexed and high-content screening assay using primary neurons and identified 67 small-molecule modulators of neuronal mitostasis (MnMs). Most MnMs that increased mitochondrial content, length, and/or health also increased mitochondrial function without altering neurite outgrowth. A subset of MnMs protected mitochondria in primary neurons from Aβ(1-42) toxicity, glutamate toxicity, and increased oxidative stress. Some MnMs were shown to directly target mitochondria. The top MnM also increased the synaptic activity of hippocampal neurons and proved to be potent in vivo, increasing the respiration rate of brain mitochondria after administering the compound to mice. Our results offer a platform that directly queries mitostasis processes in neurons, a collection of small-molecule modulators of mitochondrial dynamics and function, and candidate molecules for mitotherapeutics.

Evaluation of three automatic brain vessel segmentation methods for stereotactical trajectory planning

BACKGROUND AND OBJECTIVE

Stereotactical procedures require exact trajectory planning to avoid blood vessels in the trajectory path. Innovation in imaging and image recognition techniques have facilitated the automatic detection of blood vessels during the planning process and may improve patient safety in the future. To assess the feasibility of a vessel detection and warning system using currently available imaging and vessel segmentation techniques.

METHODS

Image data were acquired from post-contrast, isovolumetric T1-weighted sequences (T1CE) and time.-of-flight MR angiography at 3T or 7T from a total of nine subjects. Vessel segmentation by a combination of a vessel-enhancement filter with subsequent level-set segmentation was evaluated using three different methods (Vesselness, FastMarching and LevelSet) in 45 stereotactic trajectories. Segmentation results were compared to a gold-standard of manual segmentation performed jointly by two human experts.

RESULTS

The LevelSet method performed best with a mean interclass correlation coefficient (ICC) of 0.76 [0.73, 0.81] compared to the FastMarching method with ICC 0.70 [0.67, 0.73] respectively. The Vesselness algorithm achieved clearly inferior overall performance with a mean ICC of 0.56 [0.53, 0.59]. The differences in mean ICC between all segmentation methods were statistically significant (p < 0.001 with post-hoc p < 0.026). The LevelSet method performed likewise good in MPRAGE and 3T-TOF images and excellent in 7T-TOF image data. The negative predictive value (NPV) was very high (>97%) for all methods and modalities. Positive predictive values (PPV) were found in the overall range of 65-90% likewise depending on algorithm and modality. This pattern reflects the disposition of all segmentation methods - in case of misclassification - to produce preferentially false-positive than false-negative results. In a clinical setting, two to three potential collision warnings would be given per trajectory on average with a PPV of around 50%.

CONCLUSIONS

It is feasible to integrate a clinically meaningful vessel detection and collision warning system into stereotactical planning software. Both, T1CE and MRA sequences are suitable as image data for such an application.

Robust Inhibition-Augmented Operator for Delineation of Curvilinear Structures

Delineation of curvilinear structures in images is an important basic step of several image processing applications, such as segmentation of roads or rivers in aerial images, vessels or staining membranes in medical images, and cracks in pavements and roads, among others. Existing methods suffer from insufficient robustness to noise. In this paper, we propose a novel operator for the detection of curvilinear structures in images, which we demonstrate to be robust to various types of noise and effective in several applications. We call it RUSTICO, which stands for RobUST Inhibition-augmented Curvilinear Operator. It is inspired by the push-pull inhibition in visual cortex and takes as input the responses of two trainable B-COSFIRE filters of opposite polarity. The output of RUSTICO consists of a magnitude map and an orientation map. We carried out experiments on a data set of synthetic stimuli with noise drawn from different distributions, as well as on several benchmark data sets of retinal fundus images, crack pavements, and aerial images and a new data set of rose bushes used for automatic gardening. We evaluated the performance of RUSTICO by a metric that considers the structural properties of line networks (connectivity, area, and length) and demonstrated that RUSTICO outperforms many existing methods with high statistical significance. RUSTICO exhibits high robustness to noise and texture.

Noninvasive intravital high-resolution imaging of pancreatic neuroendocrine tumours

Preclinical trials of cancer drugs in animal models are important for drug development. The Rip1Tag2 (RT2) transgenic mouse, a model of pancreatic neuroendocrine tumours (PNET), has provided immense knowledge about PNET biology, although tumour progression occurs in a location inaccessible for real-time monitoring. To overcome this hurdle we have developed a novel platform for intravital 3D imaging of RT2 tumours to facilitate real-time studies of cancer progression. Pre-oncogenic islets retrieved from RT2 mice were implanted into the anterior chamber of the eye (ACE) of host mice, where they engrafted on the iris, recruited blood vessels and showed continuous growth. Noninvasive confocal and two-photon laser-scanning microscopy through the transparent cornea facilitated high-resolution imaging of tumour growth and angiogenesis. RT2 tumours in the ACE expanded up to 8-fold in size and shared hallmarks with tumours developing in situ in the pancreas. Genetically encoded fluorescent reporters enabled high-resolution imaging of stromal cells and tumour cell migration. Sunitinib treatment impaired RT2 tumour angiogenesis and growth, while overexpression of the vascular endothelial growth factor (VEGF)-B increased tumour angiogenesis though tumour growth was impaired. In conclusion, we present a novel platform for intravital high-resolution and 3D imaging of PNET biology and cancer drug assessment.

Three-dimensional segmentation and symbolic representation of cerebral vessels on 3DRA images of arteriovenous malformations

BACKGROUND

Endovascular embolization is a minimally invasive interventional method for the treatment of neurovascular pathologies such as aneurysms, arterial stenosis or arteriovenous malformations (AVMs). In this context, neuroradiologists need efficient tools for interventional planning and microcatheter embolization procedures optimization. Thus, the development of helpful methods is necessary to solve this challenging issue.

METHODS

A complete pipeline aiming to assist neuroradiologists in the visualization, interpretation and exploitation of three-dimensional rotational angiographic (3DRA) images for interventions planning in case of AVM is proposed. The developed method consists of two steps. First, an automated 3D region-based segmentation of the cerebral vessels which feed and drain the AVM is performed. From this, a graph-like tree representation of these connected vessels is then built. This symbolic representation provides a vascular network modelization with hierarchical and geometrical features that helps in the understanding of the complex angioarchitecture of the AVM.

RESULTS

The developed workflow achieves the segmentation of the vessels and of the malformation. It improves the 3D visualization of this complex network and highlights its three main components that are the arteries, the veins and the nidus. The symbolic representation then brings a better comprehension of the vessels angioarchitecture. It provides decomposition into topologically related vessels, offering the possibility to reduce the complexity due to the malformed vessels and also determine the optimal paths for AVM embolization during interventions planning.

CONCLUSIONS

A relevant vascular network modelization has been developed that constitutes a breakthrough in the assistance of neuroradiologists for AVM endovascular embolization planning.

Volume orientation: a practical solution to analyse the orientation of fibres in composite materials

Mechanical properties of fibres reinforced composite materials depend on the type of fibres used, their percentage as well as their arrangement and orientation. As computer technology continues to improve, high-resolution computed tomography has proven to be an ideal instrument to analyse the structure of this kind of materials. In this context, various approaches have been proposed to detect the fibre orientation distribution and the relative degree of anisotropy of these composite materials. Some of these approaches are based on 'individual' measurements that isolate and reconstruct each single fibre and measure its properties. On the other hand, other approaches capture the characteristics of the fibre distribution by means of 'global' measurements computed on the entire set of tomographic data. The first methods are more precise but also more complex because they demand a procedure able to segment and separate each single fibre in the polymer, whereas the latter are easier to implement and can be applied even if fibre segmentation and separation is not effective or practicable. In this paper, a global method based on the technique called volume orientation - originally proposed several years ago to study the anisotropy of bone structures - is applied to fibre reinforced composite materials. This new approach does not require data acquired at very high resolution nor very complex procedures for individual segmentation of the fibres, but only binarised data through common thresholding procedures. The effectiveness of the proposed new approach is demonstrated by comparing it to the results obtained from a method based on individual measurements: when resolution and images quality are good enough, the volume orientation method gives results quite similar to the other approach. The analysis of three different case studies demonstrates its flexibility and its validity as an alternative to methods based on the separation of individual fibres, which are not always usable. The samples have been carefully selected in order to range between different attenuation contrast levels and also include a specimen subjected to mechanical testing which can be of great practical interest. LAY DESCRIPTION: Mechanical properties of fibres reinforced composite materials depend on the type of fibres used, their percentage as well as their arrangement and orientation. Today, both destructive and nondestructive techniques can be used in order to assess the fibre orientation. As computer technology continues to improve, high-resolution computed tomography has proven to be an ideal instrument to analyse the structure of this kind of materials, and then the fibre orientation distribution inside the material. In this context, various strategies have been proposed. Some of them require measurements that isolate and reconstruct each single fibre and measure its properties. On the other hand, other approaches capture the characteristics of the fibre distribution by means of 'global' measurements computed on the entire set of tomographic data. The first methods are more precise but also more complex because they demand a procedure able to detect and separate each single fibre in the polymer, whereas the latter are easier to implement and can be applied even if fibre segmentation and separation is not effective or practicable. In this paper, a global method based on the technique called volume orientation - originally proposed several years ago to study the microstructure of bone tissues - is applied to fibre reinforced composite materials. The aim of this work is to demonstrate that this new approach is easier to use. As a matter of fact, it does not require data acquired at very high resolution nor very complex procedures for individual segmentation of the fibres, but only binarised data through common thresholding procedures. The effectiveness of the proposed new approach is shown by comparing it to the results obtained from a method based on individual measurements: when spatial resolution and images quality are good enough, the volume orientation method gives results quite similar to the other already used approach. The analysis of three different case studies demonstrates its flexibility and its validity as an alternative to methods based on the separation of individual fibres, which are not always usable. The samples have been carefully selected in order to range between different attenuation contrast levels and different nature of the fibres (mineral, vegetable or synthetic). A specimen subjected to mechanical testing is also included, because of its great practical interest.

Precise estimation of renal vascular dominant regions using spatially aware fully convolutional networks, tensor-cut and Voronoi diagrams

This paper presents a new approach for precisely estimating the renal vascular dominant region using a Voronoi diagram. To provide computer-assisted diagnostics for the pre-surgical simulation of partial nephrectomy surgery, we must obtain information on the renal arteries and the renal vascular dominant regions. We propose a fully automatic segmentation method that combines a neural network and tensor-based graph-cut methods to precisely extract the kidney and renal arteries. First, we use a convolutional neural network to localize the kidney regions and extract tiny renal arteries with a tensor-based graph-cut method. Then we generate a Voronoi diagram to estimate the renal vascular dominant regions based on the segmented kidney and renal arteries. The accuracy of kidney segmentation in 27 cases with 8-fold cross validation reached a Dice score of 95%. The accuracy of renal artery segmentation in 8 cases obtained a centerline overlap ratio of 80%. Each partition region corresponds to a renal vascular dominant region. The final dominant-region estimation accuracy achieved a Dice coefficient of 80%. A clinical application showed the potential of our proposed estimation approach in a real clinical surgical environment. Further validation using large-scale database is our future work.

Automatic quantitative analysis of pulmonary vascular morphology in CT images

PURPOSE

Vascular remodeling is a significant pathological feature of various pulmonary diseases, which may be assessed by quantitative computed tomography (CT) imaging. The purpose of this study was therefore to develop and validate an automatic method for quantifying pulmonary vascular morphology in CT images.

METHODS

The proposed method consists of pulmonary vessel extraction and quantification. For extracting pulmonary vessels, a graph-cuts-based method is proposed which considers appearance (CT intensity) and shape (vesselness from a Hessian-based filter) features, and incorporates distance to the airways into the cost function to prevent false detection of airway walls. For quantifying the extracted pulmonary vessels, a radius histogram is generated by counting the occurrence of vessel radii, calculated from a distance transform-based method. Subsequently, two biomarkers, slope α and intercept β, are calculated by linear regression on the radius histogram. A public data set from the VESSEL12 challenge was used to independently evaluate the vessel extraction. The quantitative analysis method was validated using images of a three-dimensional (3D) printed vessel phantom, scanned by a clinical CT scanner and a micro-CT scanner (to obtain a gold standard). To confirm the association between imaging biomarkers and pulmonary function, 77 scleroderma patients were investigated with the proposed method.

RESULTS

In the independent evaluation with the public data set, our vessel segmentation method obtained an area under the receiver operating characteristic (ROC) curve of 0.976. The median radius difference between clinical and micro-CT scans of a 3D printed vessel phantom was 0.062 ± 0.020 mm, with interquartile range of 0.199 ± 0.050 mm. In the studied patient group, a significant correlation between diffusion capacity for carbon monoxide and the biomarkers, α (R = -0.27, P = 0.018) and β (R = 0.321, P = 0.004), was obtained.

CONCLUSION

In conclusion, the proposed method was validated independently using a public data set resulting in an area under the ROC curve of 0.976 and using a 3D printed vessel phantom data set, showing a vessel sizing error of 0.062 mm (0.16 in-plane pixel units). The correlation between imaging biomarkers and diffusion capacity in a clinical data set confirmed an association between lung structure and function. This quantification of pulmonary vascular morphology may be helpful in understanding the pathophysiology of pulmonary vascular diseases.

More homogeneous capillary flow and oxygenation in deeper cortical layers correlate with increased oxygen extraction

Our understanding of how capillary blood flow and oxygen distribute across cortical layers to meet the local metabolic demand is incomplete. We addressed this question by using two-photon imaging of resting-state microvascular oxygen partial pressure (PO₂) and flow in the whisker barrel cortex in awake mice. Our measurements in layers I-V show that the capillary red-blood-cell flux and oxygenation heterogeneity, and the intracapillary resistance to oxygen delivery, all decrease with depth, reaching a minimum around layer IV, while the depth-dependent oxygen extraction fraction is increased in layer IV, where oxygen demand is presumably the highest. Our findings suggest that more homogeneous distribution of the physiological observables relevant to oxygen transport to tissue is an important part of the microvascular network adaptation to local brain metabolism. These results will inform the biophysical models of layer-specific cerebral oxygen delivery and consumption and improve our understanding of the diseases that affect cerebral microcirculation.

A statistical atlas of cerebral arteries generated using multi-center MRA datasets from healthy subjects

Magnetic resonance angiography (MRA) can capture the variation of cerebral arteries with high spatial resolution. These measurements include valuable information about the morphology, geometry, and density of brain arteries, which may be useful to identify risk factors for cerebrovascular and neurological diseases at an early time point. However, this requires knowledge about the distribution and morphology of vessels in healthy subjects. The statistical arterial brain atlas described in this work is a free and public neuroimaging resource that can be used to identify vascular morphological changes. The atlas was generated based on 544 freely available multi-center MRA and T1-weighted MRI datasets. The arteries were automatically segmented in each MRA dataset and used for vessel radius quantification. The binary segmentation and vessel size information were non-linearly registered to the MNI brain atlas using the T1-weighted MRI datasets to construct atlases of artery occurrence probability, mean artery radius, and artery radius standard deviation. This public neuroimaging resource improves the understanding of the distribution and size of arteries in the healthy human brain.

Robust Tracing and Visualization of Heterogeneous Microvascular Networks

Advances in high-throughput imaging allow researchers to collect three-dimensional images of whole organ microvascular networks. These extremely large images contain networks that are highly complex, time consuming to segment, and difficult to visualize. In this paper, we present a framework for segmenting and visualizing vascular networks from terabyte-sized three-dimensional images collected using high-throughput microscopy. While these images require terabytes of storage, the volume devoted to the fiber network is ≈ 4 percent of the total volume size. While the networks themselves are sparse, they are tremendously complex, interconnected, and vary widely in diameter. We describe a parallel GPU-based predictor-corrector method for tracing filaments that is robust to noise and sampling errors common in these data sets. We also propose a number of visualization techniques designed to convey the complex statistical descriptions of fibers across large tissue sections-including commonly studied microvascular characteristics, such as orientation and volume.

Line-Enhanced Deformable Registration of Pulmonary Computed Tomography Images Before and After Radiation Therapy With Radiation-Induced Fibrosis

Purpose:

The deformable registration of pulmonary computed tomography images before and after radiation therapy is challenging due to anatomic changes from radiation fibrosis. We hypothesize that a line-enhanced registration algorithm can reduce landmark error over the entire lung, including the irradiated regions, when compared to an intensity-based deformable registration algorithm.

Materials:

Two intensity-based B-spline deformable registration algorithms of pre-radiation therapy and post-radiation therapy images were compared. The first was a control intensity–based algorithm that utilized computed tomography images without modification. The second was a line enhancement algorithm that incorporated a Hessian-based line enhancement filter prior to deformable image registration. Registrations were evaluated based on the landmark error between user-identified landmark pairs and the overlap ratio.

Results:

Twenty-one patients with pre-radiation therapy and post-radiation therapy scans were included. The median time interval between scans was 1.2 years (range: 0.3-3.3 years). Median landmark errors for the line enhancement algorithm were significantly lower than those for the control algorithm over the entire lung (1.67 vs 1.83 mm; P < .01), as well as within the 0 to 5 Gy (1.40 vs 1.57; P < .01) and >5 Gy (2.25 vs 3.31; P < .01) dose intervals. The median lung mask overlap ratio for the line enhancement algorithm (96.2%) was greater than that for the control algorithm (95.8%; P < .01). Landmark error within the >5 Gy dose interval demonstrated a significant inverse relationship with post-radiation therapy fibrosis enhancement after line enhancement filtration (Pearson correlation coefficient = −0.48; P = .03).

Conclusion:

The line enhancement registration algorithm is a promising method for registering images before and after radiation therapy.

Multi-sample SPIM image acquisition, processing and analysis of vascular growth in zebrafish

To quantitatively understand biological processes that occur over many hours or days, it is desirable to image multiple samples simultaneously, and automatically process and analyse the resulting datasets. Here, we present a complete multi-sample preparation, imaging, processing and analysis workflow to determine the development of the vascular volume in zebrafish. Up to five live embryos were mounted and imaged simultaneously over several days using selective plane illumination microscopy (SPIM). The resulting large imagery dataset of several terabytes was processed in an automated manner on a high-performance computer cluster and segmented using a novel segmentation approach that uses images of red blood cells as training data. This analysis yielded a precise quantification of growth characteristics of the whole vascular network, head vasculature and tail vasculature over development. Our multi-sample platform demonstrates effective upgrades to conventional single-sample imaging platforms and paves the way for diverse quantitative long-term imaging studies.

Phenotyping the Microvasculature in Critical-Sized Calvarial Defects via Multimodal Optical Imaging

Tissue-engineered scaffolds are a powerful means of healing craniofacial bone defects arising from trauma or disease. Murine models of critical-sized bone defects are especially useful in understanding the role of microenvironmental factors such as vascularization on bone regeneration. Here, we demonstrate the capability of a novel multimodality imaging platform capable of acquiring in vivo images of microvascular architecture, microvascular blood flow, and tracer/cell tracking via intrinsic optical signaling (IOS), laser speckle contrast (LSC), and fluorescence (FL) imaging, respectively, in a critical-sized calvarial defect model. Defects that were 4 mm in diameter were made in the calvarial regions of mice followed by the implantation of osteoconductive scaffolds loaded with human adipose-derived stem cells embedded in fibrin gel. Using IOS imaging, we were able to visualize microvascular angiogenesis at the graft site and extracted morphological information such as vessel radius, length, and tortuosity two weeks after scaffold implantation. FL imaging allowed us to assess functional characteristics of the angiogenic vessel bed, such as time-to-peak of a fluorescent tracer, and also allowed us to track the distribution of fluorescently tagged human umbilical vein endothelial cells. Finally, we used LSC to characterize the in vivo hemodynamic response and maturity of the remodeled microvessels in the scaffold microenvironment. In this study, we provide a methodical framework for imaging tissue-engineered scaffolds, processing the images to extract key microenvironmental parameters, and visualizing these data in a manner that enables the characterization of the vascular phenotype and its effect on bone regeneration. Such multimodality imaging platforms can inform optimization and design of tissue-engineered scaffolds and elucidate the factors that promote enhanced vascularization and bone formation.

Accurate vessel extraction via tensor completion of background layer in X-ray coronary angiograms

This paper proposes an effective method for accurately recovering vessel structures and intensity information from the X-ray coronary angiography (XCA) images of moving organs or tissues. Specifically, a global logarithm transformation of XCA images is implemented to fit the X-ray attenuation sum model of vessel/background layers into a low-rank, sparse decomposition model for vessel/background separation. The contrast-filled vessel structures are extracted by distinguishing the vessels from the low-rank backgrounds by using a robust principal component analysis and by constructing a vessel mask via Radon-like feature filtering plus spatially adaptive thresholding. Subsequently, the low-rankness and inter-frame spatio-temporal connectivity in the complex and noisy backgrounds are used to recover the vessel-masked background regions using tensor completion of all other background regions, while the twist tensor nuclear norm is minimized to complete the background layers. Finally, the method is able to accurately extract vessels' intensities from the noisy XCA data by subtracting the completed background layers from the overall XCA images. We evaluated the vessel visibility of resulting images on real X-ray angiography data and evaluated the accuracy of vessel intensity recovery on synthetic data. Experiment results show the superiority of the proposed method over the state-of-the-art methods.

Minimal Paths for Tubular Structure Segmentation With Coherence Penalty and Adaptive Anisotropy

The minimal path method has proven to be particularly useful and efficient in tubular structure segmentation applications. In this paper, we propose a new minimal path model associated with a dynamic Riemannian metric embedded with an appearance feature coherence penalty and an adaptive anisotropy enhancement term. The features that characterize the appearance and anisotropy properties of a tubular structure are extracted through the associated orientation score. The proposed the dynamic Riemannian metric is updated in the course of the geodesic distance computation carried out by the efficient single-pass fast marching method. Compared to the state-of-the-art minimal path models, the proposed minimal path model is able to extract the desired tubular structures from a complicated vessel tree structure. In addition, we propose an efficient prior path-based method to search for vessel radius value at each centerline position of the target. Finally, we perform the numerical experiments on both synthetic and real images. The quantitive validation is carried out on retinal vessel images. The results indicate that the proposed model indeed achieves a promising performance.

Dense U-net Based on Patch-Based Learning for Retinal Vessel Segmentation

Various retinal vessel segmentation methods based on convolutional neural networks were proposed recently, and Dense U-net as a new semantic segmentation network was successfully applied to scene segmentation. Retinal vessel is tiny, and the features of retinal vessel can be learned effectively by the patch-based learning strategy. In this study, we proposed a new retinal vessel segmentation framework based on Dense U-net and the patch-based learning strategy. In the process of training, training patches were obtained by random extraction strategy, Dense U-net was adopted as a training network, and random transformation was used as a data augmentation strategy. In the process of testing, test images were divided into image patches, test patches were predicted by training model, and the segmentation result can be reconstructed by overlapping-patches sequential reconstruction strategy. This proposed method was applied to public datasets DRIVE and STARE, and retinal vessel segmentation was performed. Sensitivity (Se), specificity (Sp), accuracy (Acc), and area under each curve (AUC) were adopted as evaluation metrics to verify the effectiveness of proposed method. Compared with state-of-the-art methods including the unsupervised, supervised, and convolutional neural network (CNN) methods, the result demonstrated that our approach is competitive in these evaluation metrics. This method can obtain a better segmentation result than specialists, and has clinical application value.

Quality improvement of OCT angiograms with elliptical directional filtering

We present a method of OCT angiography (OCTA) data filtering for noise suppression and improved visualization of the retinal vascular networks in en face projection images. In our approach, we use a set of filters applied in three orthogonal axes in the three-dimensional (3-D) data sets. Minimization of artifacts generated in B-scan-wise data processing is accomplished by filtering the cross-sections along the slow scanning axis. A-scans are de-noised by axial filtering. The core of the method is the application of directional filtering to the C-scans, i.e. one-pixel thick sections of the 3-D data set, perpendicular to the direction of the scanning OCT beam. The method uses a concept of structuring, directional kernels of shapes matching the geometry of the image features. We use rotating ellipses to find the most likely local orientation of the vessels and use the best matching ellipses for median filtering of the C-scans. We demonstrate our approach in the imaging of a normal human eye with laboratory-grade spectral-domain OCT setup. The "field performance" is demonstrated in imaging of diabetic retinopathy cases with a commercial OCT device. The absolute complex differences method is used for the generation of OCTA images from the data collected in the most noise-wise unfavorable OCTA scanning regime-two frame scanning.

Non-destructive 3D Microtomography of Cerebral Angioarchitecture Changes Following Ischemic Stroke in Rats Using Synchrotron Radiation

A better understanding of functional changes in the cerebral microvasculature following ischemic injury is essential to elucidate the pathogenesis of stroke. Up to now, the simultaneous depiction and stereological analysis of 3D micro-architectural changes of brain vasculature with network disorders remains a technical challenge. We aimed to explore the three dimensional (3D) microstructural changes of microvasculature in the rat brain on 4, 6 hours, 3 and 18 days post-ischemia using synchrotron radiation micro-computed tomography (SRμCT) with a per pixel size of 5.2 μm. The plasticity of angioarchitecture was distinctly visualized. Quantitative assessments of time-related trends after focal ischemia, including number of branches, number of nodes, and frequency distribution of vessel diameter, reached a peak at 6 h and significantly decreased at 3 days and initiated to form cavities. The detected pathological changes were also proven by histological tests. We depicted a novel methodology for the 3D analysis of vascular repair in ischemic injury, both qualitatively and quantitatively. Cerebral angioarchitecture sustained 3D remodeling and modification during the healing process. The results might provide a deeper insight into the compensatory mechanisms of microvasculature after injury, suggesting that SRμCT is able to provide a potential new platform for deepening imaging pathological changes in complicated angioarchitecture and evaluating potential therapeutic targets for stroke.

Fractal dimension and directional analysis of elastic and collagen fiber arrangement in unsectioned arterial tissues affected by atherosclerosis and aging

Structural proteins like collagen and elastin are major constituents of the extracellular matrix (ECM). ECM degradation and remodeling in diseases significantly impact the microorganization of these structural proteins. Therefore, tracking the changes of collagen and elastin fiber morphological features within ECM impacted by disease progression could provide valuable insight into pathological processes such as tissue fibrosis and atherosclerosis. Benefiting from its intrinsic high-resolution imaging power and superior biochemical specificity, nonlinear optical microscopy (NLOM) is capable of providing information critical to the understanding of ECM remodeling. In this study, alterations of structural fibrillar proteins such as collagen and elastin in arteries excised from atherosclerotic rabbits were assessed by the combination of NLOM images and textural analysis methods such as fractal dimension (FD) and directional analysis (DA). FD and DA were tested for their performance in tracking the changes of extracellular elastin and fibrillar collagen remodeling resulting from atherosclerosis progression/aging. Although other methods of image analysis to study the organization of elastin and collagen structures have been reported, the simplified calculations of FD and DA presented in this work prove that they are viable strategies for extracting and analyzing fiber-related morphology from disease-impacted tissues. Furthermore, this study also demonstrates the potential utility of FD and DA in studying ECM remodeling caused by other pathological processes such as respiratory diseases, several skin conditions, or even cancer. NEW & NOTEWORTHY Textural analyses such as fractal dimension (FD) and directional analysis (DA) are straightforward and computationally viable strategies to extract fiber-related morphological data from optical images. Therefore, objective, quantitative, and automated characterization of protein fiber morphology in extracellular matrix can be realized by using these methods in combination with digital imaging techniques such as nonlinear optical microscopy (NLOM), a highly effective visualization tool for fibrillar collagen and elastic network. Combining FD and DA with NLOM is an innovative approach to track alterations of structural fibrillar proteins. The results illustrated in this study not only prove the effectiveness of FD and DA methods in extracellular protein characterization but also demonstrate their potential value in clinical and basic biomedical research where protein microstructure characterization is critical.

Segmentation of pulmonary vascular tree by incorporating vessel enhancement filter and variational region-growing

BACKGROUND

Automatic segmentation of pulmonary vascular tree in the thoracic computed tomography (CT) image is a promising but challenging task with great clinical potential values. It is difficult to segment the whole vascular tree in reasonable time and acceptable accuracy.

OBJECTIVE

To develop a novel pulmonary vessel segmentation approach by incorporating vessel enhancement filters and the anisotropic diffusion filter with the variational region growing.

METHODS

First, the airway wall from the lung lobes is eliminated from CT images by using multi-scale morphological operations. Second, a Hessian-based multi-scale vesselness filter and medialness filter are applied to detect and enhance the potential vessel. Third, an anisotropic diffusion filter is used to remove noise and enhance the tube-like structures in CT images. Last, the vascular tree is segmented by applying variational region growing algorithm.

RESULTS

Applying to the CT images collected from the entire dataset of VESSEL12 challenge, we achieved an average sensitivity of 92.9%, specificity of 91.6% and the area under the ROC curve of AUC = 0.972.

CONCLUSIONS

This study demonstrated feasibility of segmenting the pulmonary vessel effectively by incorporating vessel enhancement filters and the anisotropic diffusion filter with the variational region growing algorithm. Our method cannot only segment both large and peripheral vessels, but also distinguish the vessels from the adjacent tissues, especially the airway walls.

FissureNet: A Deep Learning Approach For Pulmonary Fissure Detection in CT Images

Pulmonary fissure detection in computed tomography (CT) is a critical component for automatic lobar segmentation. The majority of fissure detection methods use feature descriptors that are hand-crafted, low-level, and have local spatial extent. The design of such feature detectors is typically targeted toward normal fissure anatomy, yielding low sensitivity to weak, and abnormal fissures that are common in clinical data sets. Furthermore, local features commonly suffer from low specificity, as the complex textures in the lung can be indistinguishable from the fissure when the global context is not considered. We propose a supervised discriminative learning framework for simultaneous feature extraction and classification. The proposed framework, called FissureNet, is a coarse-to-fine cascade of two convolutional neural networks. The coarse-to-fine strategy alleviates the challenges associated with training a network to segment a thin structure that represents a small fraction of the image voxels. FissureNet was evaluated on a cohort of 3706 subjects with inspiration and expiration 3DCT scans from the COPDGene clinical trial and a cohort of 20 subjects with 4DCT scans from a lung cancer clinical trial. On both data sets, FissureNet showed superior performance compared with a deep learning approach using the U-Net architecture and a Hessian-based fissure detection method in terms of area under the precision-recall curve (PR-AUC). The overall PR-AUC for FissureNet, U-Net, and Hessian on the COPDGene (lung cancer) data set was 0.980 (0.966), 0.963 (0.937), and 0.158 (0.182), respectively. On a subset of 30 COPDGene scans, FissureNet was compared with a recently proposed advanced fissure detection method called derivative of sticks (DoS) and showed superior performance with a PR-AUC of 0.991 compared with 0.668 for DoS.

A Feasibility Study of Single-inhalation, Single-energy Xenon-enhanced CT for High-resolution Imaging of Regional Lung Ventilation in Humans

RATIONALE AND OBJECTIVES

The objective of this study was to assess the feasibility of single-inhalation xenon-enhanced computed tomography (XeCT) to provide clinically practical, high-resolution pulmonary ventilation imaging to clinics with access to only a single-energy computed tomography scanner, and to reduce the subject's overall exposure to xenon by utilizing a higher (70%) concentration for a much shorter time than has been employed in prior studies.

MATERIALS AND METHODS

We conducted an institutional review board-approved prospective feasibility study of XeCT for 15 patients undergoing thoracic radiotherapy. For XeCT, we acquired two breath-hold single-energy computed tomography images of the entire lung with a single inhalation each of 100% oxygen and a mixture of 70% xenon and 30% oxygen, respectively. A video biofeedback system for coached patient breathing was used to achieve reproducible breath holds. We assessed the technical success of XeCT acquisition and side effects. We then used deformable image registration to align the breath-hold images with each other to accurately subtract them, producing a map of lung xenon distribution. Additionally, we acquired ventilation single-photon emission computed tomography-computed tomography (V-SPECT-CT) images for 11 of the 15 patients. For a comparative analysis, we partitioned each lung into 12 sectors, calculated the xenon concentration from the Hounsfield unit enhancement in each sector, and then correlated this with the corresponding V-SPECT-CT counts.

RESULTS

XeCT scans were tolerated well overall, with a mild (grade 1) dizziness as the only side effect in 5 of the 15 patients. Technical failures in five patients occurred because of inaccurate breathing synchronization with xenon gas delivery, leaving seven patients analyzable for XeCT and single-photon emission computed tomography correlation. Sector-wise correlations were strong (Spearman coefficient >0.75, Pearson coefficient >0.65, P value <.002) for two patients for whom ventilation deficits were visibly pronounced in both scans. Correlations were nonsignificant for the remaining five who had more homogeneous XeCT ventilation maps, as well as strong V-SPECT-CT imaging artifacts attributable to airway deposition of the aerosolized imaging agent. Qualitatively, XeCT demonstrated higher resolution and no central airway deposition artifacts compared to V-SPECT-CT.

CONCLUSIONS

In this pilot study, single-breath XeCT ventilation imaging was generally feasible for patients undergoing thoracic radiotherapy, using an imaging protocol that is clinically practical and potentially widely available. In the future, the xenon delivery failures can be addressed by straightforward technical improvements to the patient biofeedback coaching system.