3D cerebrovascular segmentation combining fuzzy vessel enhancement and level-sets with anisotropic energy weights

Contour attention network for cerebrovascular segmentation from TOF-MRA volumetric images

BACKGROUND

Cerebrovascular segmentation is a crucial step in the computer-assisted diagnosis of cerebrovascular pathologies. However, accurate extraction of cerebral vessels from time-of-flight magnetic resonance angiography (TOF-MRA) data is still challenging due to the complex topology and slender shape.

PURPOSE

The existing deep learning-based approaches pay more attention to the skeleton and ignore the contour, which limits the segmentation performance of the cerebrovascular structure. We aim to weight the contour of brain vessels in shallow features when concatenating with deep features. It helps to obtain more accurate cerebrovascular details and narrows the semantic gap between multilevel features.

METHODS

This work proposes a novel framework for priority extraction of contours in cerebrovascular structures. We first design a neighborhood-based algorithm to generate the ground truth of the cerebrovascular contour from original annotations, which can introduce useful shape information for the segmentation network. Moreover, We propose an encoder-dual decoder-based contour attention network (CA-Net), which consists of the dilated asymmetry convolution block (DACB) and the Contour Attention Module (CAM). The ancillary decoder uses the DACB to obtain cerebrovascular contour features under the supervision of contour annotations. The CAM transforms these features into a spatial attention map to increase the weight of the contour voxels in main decoder to better restored the vessel contour details.

RESULTS

The CA-Net is thoroughly validated using two publicly available datasets, and the experimental results demonstrate that our network outperforms the competitors for cerebrovascular segmentation. We achieved the average dice similarity coefficient (D S C $DSC$ ) of 68.15 and 99.92% in natural and synthetic datasets. Our method segments cerebrovascular structures with better completeness.

CONCLUSIONS

We propose a new framework containing contour annotation generation and cerebrovascular segmentation network that better captures the tiny vessels and improve vessel connectivity.

Fractal-Based Analysis of Arteriovenous Malformations (AVMs)

Arteriovenous malformations (AVMs) are cerebrovascular lesions consisting of a pathologic tangle of the vessels characterized by a core termed the nidus, which is the "nest" where the fistulous connections occur. AVMs can cause headache, stroke, and/or seizures. Their treatment can be challenging requiring surgery, endovascular embolization, and/or radiosurgery as well. AVMs' morphology varies greatly among patients, and there is still a lack of standardization of angioarchitectural parameters, which can be used as morphometric parameters as well as potential clinical biomarkers (e.g., related to prognosis).In search of new diagnostic and prognostic neuroimaging biomarkers of AVMs, computational fractal-based models have been proposed for describing and quantifying the angioarchitecture of the nidus. In fact, the fractal dimension (FD) can be used to quantify AVMs' branching pattern. Higher FD values are related to AVMs characterized by an increased number and tortuosity of the intranidal vessels or to an increasing angioarchitectural complexity as a whole. Moreover, FD has been investigated in relation to the outcome after Gamma Knife radiosurgery, and an inverse relationship between FD and AVM obliteration was found.Taken altogether, FD is able to quantify in a single and objective value what neuroradiologists describe in qualitative and/or semiquantitative way, thus confirming FD as a reliable morphometric neuroimaging biomarker of AVMs and as a potential surrogate imaging biomarker. Moreover, computational fractal-based techniques are under investigation for the automatic segmentation and extraction of the edges of the nidus in neuroimaging, which can be relevant for surgery and/or radiosurgery planning.

VCerebrovascular Segmentation in Phase-Contrast Magnetic Resonance Angiography by a Radon Projection Composition Network

Cerebrovascular segmentation based on phase-contrast magnetic resonance angiography (PC-MRA) provides patient-specific intracranial vascular structures for neurosurgery planning. However, the vascular complex topology and spatial sparsity make the task challenging. Inspired by the computed tomography reconstruction, this paper proposes a Radon Projection Composition Network (RPC-Net) for cerebrovascular segmentation in PC-MRA, aiming to enhance distribution probability of vessels and fully obtain the vascular topological information. Multi-directional Radon projections of the images are introduced and a two-stream network is used to learn the features of the 3D images and projections. The projection domain features are remapped to the 3D image domain by filtered back-projection transform to obtain the image-projection joint features for predicting vessel voxels. A four-fold cross-validation experiment was performed on a local dataset containing 128 PC-MRA scans. The average Dice similarity coefficient, precision and recall of the RPC-Net achieved 86.12%, 85.91% and 86.50%, respectively, while the average completeness and validity of the vessel structure were 85.50% and 92.38%, respectively. The proposed method outperformed the existing methods, especially with significant improvement on the extraction of small and low-intensity vessels. Moreover, the applicability of the segmentation for electrode trajectory planning was also validated. The results demonstrate that the RPC-Net realizes an accurate and complete cerebrovascular segmentation and has potential applications in assisting neurosurgery preoperative planning.

Multimodal brain age prediction fusing morphometric and imaging data and association with cardiovascular risk factors

Introduction

The difference between the chronological and biological brain age, called the brain age gap (BAG), has been identified as a promising biomarker to detect deviation from normal brain aging and to indicate the presence of neurodegenerative diseases. Moreover, the BAG has been shown to encode biological information about general health, which can be measured through cardiovascular risk factors. Current approaches for biological brain age estimation, and therefore BAG estimation, either depend on hand-crafted, morphological measurements extracted from brain magnetic resonance imaging (MRI) or on direct analysis of brain MRI images. The former can be processed with traditional machine learning models while the latter is commonly processed with convolutional neural networks (CNNs). Using a multimodal setting, this study aims to compare both approaches in terms of biological brain age prediction accuracy and biological information captured in the BAG.

Methods

T1-weighted MRI, containing brain tissue information, and magnetic resonance angiography (MRA), providing information about brain arteries, from 1,658 predominantly healthy adults were used. The volumes, surface areas, and cortical thickness of brain structures were extracted from the T1-weighted MRI data, while artery density and thickness within the major blood flow territories and thickness of the major arteries were extracted from MRA data. Independent multilayer perceptron and CNN models were trained to estimate the brain age from the hand-crafted features and image data, respectively. Next, both approaches were fused to assess the benefits of combining image data and hand-crafted features for brain age prediction.

Results

The combined model achieved a mean absolute error of 4 years between the chronological and predicted biological brain age. Among the independent models, the lowest mean absolute error was observed for the CNN using T1-weighted MRI data (4.2 years). When evaluating the BAGs obtained using the different approaches and imaging modalities, diverging associations between cardiovascular risk factors were found. For example, BAGs obtained from the CNN models showed an association with systolic blood pressure, while BAGs obtained from hand-crafted measurements showed greater associations with obesity markers.

Discussion

In conclusion, the use of more diverse sources of data can improve brain age estimation modeling and capture more diverse biological deviations from normal aging.

Intracranial Artery Morphology in Pediatric Moya Moya Disease and Moya Moya Syndrome

BACKGROUND

Moya Moya disease (MMD) and Moya Moya syndrome (MMS) are cerebrovascular disorders, which affect the internal carotid arteries (ICAs). Diagnosis and surveillance of MMD/MMS in children mostly rely on qualitative evaluation of vascular imaging, especially MR angiography (MRA).

OBJECTIVE

To quantitatively characterize arterial differences in pediatric patients with MMD/MMS compared with normal controls.

METHODS

MRA data sets from 17 presurgery MMD/MMS (10M/7F, mean age = 10.0 years) patients were retrospectively collected and compared with MRA data sets of 98 children with normal vessel morphology (49 male patients; mean age = 10.6 years). Using a level set segmentation method with anisotropic energy weights, the cerebral arteries were automatically extracted and used to compute the radius of the ICA, middle cerebral artery (MCA), anterior cerebral artery (ACA), posterior cerebral artery (PCA), and basilar artery (BA). Moreover, the density and the average radius of all arteries in the MCA, ACA, and PCA flow territories were quantified.

RESULTS

Statistical analysis revealed significant differences comparing children with MMD/MMS and those with normal vasculature (P < .001), whereas post hoc analyses identified significantly smaller radii of the ICA, MCA-M1, MCA-M2, and ACA (P < .001) in the MMD/MMS group. No significant differences were found for the radii of the PCA and BA or any artery density and average artery radius measurement in the flow territories (P > .05).

CONCLUSION

His study describes the results of an automatic approach for quantitative characterization of the cerebrovascular system in patients with MMD/MMS with promising preliminary results for quantitative surveillance in pediatric MMD/MMS management.

Multimodal biological brain age prediction using magnetic resonance imaging and angiography with the identification of predictive regions

Biological brain age predicted using machine learning models based on high-resolution imaging data has been suggested as a potential biomarker for neurological and cerebrovascular diseases. In this work, we aimed to develop deep learning models to predict the biological brain age using structural magnetic resonance imaging and angiography datasets from a large database of 2074 adults (21-81 years). Since different imaging modalities can provide complementary information, combining them might allow to identify more complex aging patterns, with angiography data, for instance, showing vascular aging effects complementary to the atrophic brain tissue changes seen in T1-weighted MRI sequences. We used saliency maps to investigate the contribution of cortical, subcortical, and arterial structures to the prediction. Our results show that combining T1-weighted and angiography MR data led to a significantly improved brain age prediction accuracy, with a mean absolute error of 3.85 years comparing the predicted and chronological age. The most predictive brain regions included the lateral sulcus, the fourth ventricle, and the amygdala, while the brain arteries contributing the most to the prediction included the basilar artery, the middle cerebral artery M2 segments, and the left posterior cerebral artery. Our study proposes a framework for brain age prediction using multimodal imaging, which gives accurate predictions and allows identifying the most predictive regions for this task, which can serve as a surrogate for the brain regions that are most affected by aging.

Automatic segmentation of peripheral arteries and veins in ferumoxytol-enhanced MR angiography

PURPOSE

To automate the segmentation of the peripheral arteries and veins in the lower extremities based on ferumoxytol-enhanced MR angiography (FE-MRA).

METHODS

Our automated pipeline has 2 sequential stages. In the first stage, we used a 3D U-Net with local attention gates, which was trained based on a combination of the Focal Tversky loss with region mutual loss under a deep supervision mechanism to segment the vasculature from the high-resolution FE-MRA datasets. In the second stage, we used time-resolved images to separate the arteries from the veins. Because the ultimate segmentation quality of the arteries and veins relies on the performance of the first stage, we thoroughly evaluated the different aspects of the segmentation network and compared its performance in blood vessel segmentation with currently accepted state-of-the-art networks, including Volumetric-Net, DeepVesselNet-FCN, and Uception.

RESULTS

We achieved a competitive F1 = 0.8087 and recall = 0.8410 for blood vessel segmentation compared with F1 = (0.7604, 0.7573, 0.7651) and recall = (0.7791, 0.7570, 0.7774) obtained with Volumetric-Net, DeepVesselNet-FCN, and Uception. For the artery and vein separation stage, we achieved F1 = (0.8274/0.7863) in the calf region, which is the most challenging region in peripheral arteries and veins segmentation.

CONCLUSION

Our pipeline is capable of fully automatic vessel segmentation based on FE-MRA without need for human interaction in <4 min. This method improves upon manual segmentation by radiologists, which routinely takes several hours.

Age-dependent Intracranial Artery Morphology in Healthy Children

PURPOSE

Evaluation of intracranial artery morphology plays an important role in diagnosing a variety of neurovascular diseases. In addition to clinical symptoms, diagnosis currently relies on qualitative rather than quantitative evaluation of vascular imaging sequences, such as magnetic resonance angiography (MRA). However, there is a paucity of literature on normal arterial morphology in the pediatric population across brain development. We aimed to quantitatively assess normal, age-related changes in artery morphology in children.

METHODS

We performed retrospective analysis of pediatric MRA data obtained from a tertiary referral center. An MRA dataset from 98 children (49 boys/49 girls) aged 0.6-20 years (median = 11.5 years) with normal intracranial vasculature was retrospectively collected between 2011 and 2018. All arteries were automatically segmented to determine the vessel radius. Using an atlas-based approach, the average radius and density of arteries were measured in the three main cerebral vascular territories and the radius of five major arteries was determined at corresponding locations.

RESULTS

The radii of the major arteries as well as the average artery radius and density in the different vascular territories in the brain remained constant throughout childhood and adolescence (|r| < 0.369 in all cases).

CONCLUSION

This study presents the first automated evaluation of intracranial vessel morphology on MRA across childhood. Our results can serve as a framework for quantitative evaluation of cerebral vessel morphology in the setting of pediatric neurovascular diseases.

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

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

Influence of cardiovascular risk-factors on morphological changes of cerebral arteries in healthy adults across the life span

Cerebral artery morphological alterations have been associated with several cerebrovascular and neurological diseases, whereas these structures are known to be highly variable among healthy individuals. To date, the knowledge about the influence of cardiovascular risk factors on the morphology of cerebral arteries is rather limited. The aim of this work was to investigate the impact of cardiovascular risk factors on the regional cerebroarterial radius and density. Time-of-Flight magnetic resonance angiography from 1722 healthy adults (21-82 years) were used to extract region-specific measurements describing the main cerebral artery morphology. Multivariate statistical analysis was conducted to quantify the impact of cardiovascular risk factors, including clinical and life behavioural factors, on each region-specific artery measurement. Increased age, blood pressure, and markers of obesity were significantly associated with decreased artery radius and density in most regions, with aging having the greatest impact. Additionally, females showed significantly higher artery density while males showed higher artery radius. Smoking and alcohol consumption did not show any significant association with the artery morphology. The results of this study improve the understanding of the impact of aging, clinical factors, and life behavioural factors on cerebrovascular morphology and can help to identify potential risk factors for cerebrovascular and neurological diseases.

Vessel segmentation from volumetric images: a multi-scale double-pathway network with class-balanced loss at the voxel level

PURPOSE

Vessel segmentation from volumetric medical images is becoming an essential pre-step in aiding the diagnosis, guiding the therapy, and patient management for vascular-related diseases. Deep learning-based methods have drawn many attentions, but most of them did not fully utilize the multi-scale spatial information of vessels. To address this shortcoming, we propose a multi-scale network similar to the well-known multi-scale DeepMedic. It also includes a double-pathway architecture and a class-balanced loss at the voxel level (MDNet-Vb) to achieve both the computation efficiency and segmentation accuracy.

METHODS

The proposed network consists two parallel pathways to learn the multi-scale vessel morphology. Specifically, the pathway with a normal resolution uses three-dimensional (3D) U-Net fed with small inputs to learn the local details with relatively small storage and time consumption. The pathway with a low-resolution employs 3D fully convolutional network (FCN) fed with downsampled large inputs to learn the overall spatial relationships between vessels and adjacent tissues, and the morphological information of large vessels. To cope with the class-imbalanced issue in vessel segmentation, we propose a class-balanced loss at the voxel level with uniform sampling strategy. The class-balanced loss at the voxel level re-balances the loss function with a coefficient that is inversely proportional to the normalized effective number at the voxel level of each class. The uniform sampling strategy extracts training data by sampling uniformly from two classes in every epoch.

RESULTS

Our MDNet-Vb outperforms several state-of-the-art methods including ResNet, DenseNet, 3D U-Net, V-Net, and DeepMedic with the highest dice coefficients of 72.91% and 69.32% on cardiac computed tomography angiography (CTA) dataset and cerebral magnetic resonance angiography (MRA) dataset, respectively. Among four different double-pathway networks, our network (3D U-Net+3D FCN) not only has the fewest training parameters and shortest training time, but also gets competitive dice coefficients on both the CTA and MRA datasets. Compared with classical losses, our class-balanced focal loss (FL-Vb) and dice coefficient loss at the voxel level (Dsc-Vb) alleviates class imbalanced issue by improving both the sensitivity and dice coefficient on the CTA and MRA datasets. Moreover, simultaneously training on two datasets shows that our method has the highest dice coefficient of 73.06% and 65.40% on CTA and MRA datasets, respectively, outperforming the commonly used methods, such as U-Net and DeepMedic, which demonstrates the generalization potential of our network for segmenting different blood vessels.

CONCLUSIONS

Our MDNet-Vb method demonstrates its superiority over other state-of-the-art methods, on both cardiac CTA and cerebral MRA datasets. For the network architecture, the MDNet-Vb combined the 3D U-Net and 3D FCN, which dramatically reduces the network parameters yet maintains the segmentation accuracy. The class-balanced loss at the voxel level further improves accuracy by properly alleviating the class-imbalanced issue between different classes. In summary, MDNet-Vb is promising for vessel segmentation from various volumetric medical images.

Cerebrovascular segmentation from TOF-MRA based on multiple-U-net with focal loss function

BACKGROUND AND OBJECTIVE

Accurate cerebrovascular segmentation plays an important role in the diagnosis of cerebrovascular diseases. Considering the complexity and uncertainty of doctors' manual segmentation of cerebral vessels, this paper proposed an automatic segmentation algorithm based on Multiple-U-net (M-U-net) to segment cerebral vessel structures from the Time-of-flight Magnetic Resonance Angiography (TOF-MRA) data.

METHODS

First, the TOF-MRA data was normalized by volume and then divided into three groups through slices of axial, coronal and sagittal directions respectively. Three single U-nets were trained by divided dataset. To solve the problem of uneven distribution of positive and negative samples, the focal loss function was adopted in training. After obtaining the prediction results of three single U-nets, the voting feature fusion and the post-processing process based on connected domain analysis would be performed. 95 volumes of TOF-MRA provided by the MIDAS platform were applied to the experiment, among which 20 volumes were treated as the training dataset, 5 volumes were used as the validation dataset and the remaining 70 volumes were divided into 10 groups to test the trained model respectively.

RESULTS

Experiments showed that the proposed M-U-net based algorithm achieved 88.60% and 87.93% Dice Similarity Coefficient (DSC) on the verification dataset and testing dataset, which performed better than any single U-net.

CONCLUSIONS

Compared with other existing algorithms, our algorithm reached the state of the art level. The feature fusion of three single U-nets could effectively complement the segmentation results.

VC-Net: Deep Volume-Composition Networks for Segmentation and Visualization of Highly Sparse and Noisy Image Data

The fundamental motivation of the proposed work is to present a new visualization-guided computing paradigm to combine direct 3D volume processing and volume rendered clues for effective 3D exploration. For example, extracting and visualizing microstructures in-vivo have been a long-standing challenging problem. However, due to the high sparseness and noisiness in cerebrovasculature data as well as highly complex geometry and topology variations of micro vessels, it is still extremely challenging to extract the complete 3D vessel structure and visualize it in 3D with high fidelity. In this paper, we present an end-to-end deep learning method, VC-Net, for robust extraction of 3D microvascular structure through embedding the image composition, generated by maximum intensity projection (MIP), into the 3D volumetric image learning process to enhance the overall performance. The core novelty is to automatically leverage the volume visualization technique (e.g., MIP - a volume rendering scheme for 3D volume images) to enhance the 3D data exploration at the deep learning level. The MIP embedding features can enhance the local vessel signal (through canceling out the noise) and adapt to the geometric variability and scalability of vessels, which is of great importance in microvascular tracking. A multi-stream convolutional neural network (CNN) framework is proposed to effectively learn the 3D volume and 2D MIP feature vectors, respectively, and then explore their inter-dependencies in a joint volume-composition embedding space by unprojecting the 2D feature vectors into the 3D volume embedding space. It is noted that the proposed framework can better capture the small/micro vessels and improve the vessel connectivity. To our knowledge, this is the first time that a deep learning framework is proposed to construct a joint convolutional embedding space, where the computed vessel probabilities from volume rendering based 2D projection and 3D volume can be explored and integrated synergistically. Experimental results are evaluated and compared with the traditional 3D vessel segmentation methods and the state-of-the-art in deep learning, by using extensive public and real patient (micro- )cerebrovascular image datasets. The application of this accurate segmentation and visualization of sparse and complicated 3D microvascular structure facilitated by our method demonstrates the potential in a powerful MR arteriogram and venogram diagnosis of vascular disease.

Multimodal Risk-Based Path Planning for Neurosurgical Interventions

Image-guided neurosurgical interventions are challenging due to the complex anatomy of the brain and the inherent risk of damaging vital structures. This paper presents a neurosurgical planning tool for safe and effective neurosurgical interventions, minimizing the risk through optimized access planning. The strengths of the proposed system are the integration of multiple risk structures combined into a holistic model for fast and intuitive user interaction, and a modular architecture. The tool is intended to support neurosurgeons to quickly determine the most appropriate surgical entry point and trajectory through the brain with minimized risk. The user interface guides a user through the decision-making process and may save planning time of neurosurgical interventions. The navigation tool has been interfaced to the Robot Operating System, which allows the integration into automated workflows and the planning of linear and nonlinear trajectories. Determined risk structures and trajectories can be visualized intuitively as a projection map on the skin or cortical surface. Two risk calculation modes (strict and joint) are offered to the neurosurgeons, depending on the intracranial procedure's type and complexity. A qualitative evaluation with clinical experts shows the practical relevance, while a quantitative performance and functionality analysis proves the robustness and effectiveness of the system.

DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes

We present DeepVesselNet, an architecture tailored to the challenges faced when extracting vessel trees and networks and corresponding features in 3-D angiographic volumes using deep learning. We discuss the problems of low execution speed and high memory requirements associated with full 3-D networks, high-class imbalance arising from the low percentage (<3%) of vessel voxels, and unavailability of accurately annotated 3-D training data-and offer solutions as the building blocks of DeepVesselNet. First, we formulate 2-D orthogonal cross-hair filters which make use of 3-D context information at a reduced computational burden. Second, we introduce a class balancing cross-entropy loss function with false-positive rate correction to handle the high-class imbalance and high false positive rate problems associated with existing loss functions. Finally, we generate a synthetic dataset using a computational angiogenesis model capable of simulating vascular tree growth under physiological constraints on local network structure and topology and use these data for transfer learning. We demonstrate the performance on a range of angiographic volumes at different spatial scales including clinical MRA data of the human brain, as well as CTA microscopy scans of the rat brain. Our results show that cross-hair filters achieve over 23% improvement in speed, lower memory footprint, lower network complexity which prevents overfitting and comparable accuracy that does not differ from full 3-D filters. Our class balancing metric is crucial for training the network, and transfer learning with synthetic data is an efficient, robust, and very generalizable approach leading to a network that excels in a variety of angiography segmentation tasks. We observe that sub-sampling and max pooling layers may lead to a drop in performance in tasks that involve voxel-sized structures. To this end, the DeepVesselNet architecture does not use any form of sub-sampling layer and works well for vessel segmentation, centerline prediction, and bifurcation detection. We make our synthetic training data publicly available, fostering future research, and serving as one of the first public datasets for brain vessel tree segmentation and analysis.

Quantitative Analysis of the Cerebral Vasculature on Magnetic Resonance Angiography

The arterial connections in the Circle of Willis are a central source of collateral blood flow and play an important role in pathologies such as stroke and mental illness. Analysis of the Circle of Willis and its variants can shed light on optimal methods of diagnosis, treatment planning, surgery, and quantification of outcomes. We developed an automated, standardized, objective, and high-throughput approach for categorizing and quantifying the Circle of Willis vascular anatomy using magnetic resonance angiography images. This automated algorithm for processing of MRA images isolates and automatically identifies key features of the cerebral vasculature such as branching of the internal intracranial internal carotid artery and the basilar artery. Subsequently, physical features of the segments of the anterior cerebral artery were acquired on a sample and intra-patient comparisons were made. We demonstrate the feasibility of using our approach to automatically classify important structures of the Circle of Willis and extract biomarkers from cerebrovasculature. Automated image analysis can provide clinically-relevant vascular features such as aplastic arteries, stenosis, aneurysms, and vessel caliper for endovascular procedures. The developed algorithm could facilitate clinical studies by supporting high-throughput automated analysis of the cerebral vasculature.

Segmentation-Based Blood Flow Parameter Refinement in Cerebrovascular Structures Using 4-D Arterial Spin Labeling MRA

OBJECTIVE

Cerebrovascular diseases are one of the main global causes of death and disability in the adult population. The preferred imaging modality for the diagnostic routine is digital subtraction angiography, an invasive modality. Time-resolved three-dimensional arterial spin labeling magnetic resonance angiography (4D ASL MRA) is an alternative non-invasive modality, which captures morphological and blood flow data of the cerebrovascular system, with high spatial and temporal resolution. This work proposes advanced medical image processing methods that extract the anatomical and hemodynamic information contained in 4D ASL MRA datasets.

METHODS

A previously published segmentation method, which uses blood flow data to improve its accuracy, is extended to estimate blood flow parameters by fitting a mathematical model to the measured vascular signal. The estimated values are then refined using regression techniques within the cerebrovascular segmentation. The proposed method was evaluated using fifteen 4D ASL MRA phantoms, with ground-truth morphological and hemodynamic data, fifteen 4D ASL MRA datasets acquired from healthy volunteers, and two 4D ASL MRA datasets from patients with a stenosis.

RESULTS

The proposed method reached an average Dice similarity coefficient of 0.957 and 0.938 in the phantom and real dataset segmentation evaluations, respectively. The estimated blood flow parameter values are more similar to the ground-truth values after the refinement step, when using phantoms. A qualitative analysis showed that the refined blood flow estimation is more realistic compared to the raw hemodynamic parameters.

CONCLUSION

The proposed method can provide accurate segmentations and blood flow parameter estimations in the cerebrovascular system using 4D ASL MRA datasets.

SIGNIFICANCE

The information obtained with the proposed method can help clinicians and researchers to study the cerebrovascular system non-invasively.

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.

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.

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.

Automatic Temporal Segmentation of Vessels of the Brain Using 4D ASL MRA Images

OBJECTIVE

Automatic vessel segmentation can be used to process the considerable amount of data generated by four-dimensional arterial spin labeling magnetic resonance angiography (4D ASL MRA) images. Previous segmentation approaches for dynamic series of images propose either reducing the series to a temporal average (tAIP) or maximum intensity projection (tMIP) prior to vessel segmentation, or a separate segmentation of each image. This paper introduces a method that combines both approaches to overcome the specific drawbacks of each technique.

METHODS

Vessels in the tAIP are enhanced by using the ranking orientation responses of path operators and multiscale vesselness enhancement filters. Then, tAIP segmentation is performed using a seed-based algorithm. In parallel, this algorithm is also used to segment each frame of the series and identify small vessels, which might have been lost in the tAIP segmentation. The results of each individual time frame segmentation are fused using an or boolean operation. Finally, small vessels found only in the fused segmentation are added to the tAIP segmentation.

RESULTS

In a quantitative analysis using ten 4D ASL MRA image series from healthy volunteers, the proposed combined approach reached an average Dice coefficient of 0.931, being more accurate than the corresponding tMIP, tAIP, and single time frame segmentation methods with statistical significance.

CONCLUSION

The novel combined vessel segmentation strategy can be used to obtain improved vessel segmentation results from 4D ASL MRA and other dynamic series of images.

SIGNIFICANCE

Improved vessel segmentation of 4D ASL MRA allows a fast and accurate assessment of cerebrovascular structures.

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.

Simulation of MR angiography imaging for validation of cerebral arteries segmentation algorithms

BACKGROUND AND OBJECTIVE

Accurate vessel segmentation of magnetic resonance angiography (MRA) images is essential for computer-aided diagnosis of cerebrovascular diseases such as stenosis or aneurysm. The ability of a segmentation algorithm to correctly reproduce the geometry of the arterial system should be expressed quantitatively and observer-independently to ensure objectivism of the evaluation.

METHODS

This paper introduces a methodology for validating vessel segmentation algorithms using a custom-designed MRA simulation framework. For this purpose, a realistic reference model of an intracranial arterial tree was developed based on a real Time-of-Flight (TOF) MRA data set. With this specific geometry blood flow was simulated and a series of TOF images was synthesized using various acquisition protocol parameters and signal-to-noise ratios. The synthesized arterial tree was then reconstructed using a level-set segmentation algorithm available in the Vascular Modeling Toolkit (VMTK). Moreover, to present versatile application of the proposed methodology, validation was also performed for two alternative techniques: a multi-scale vessel enhancement filter and the Chan-Vese variant of the level-set-based approach, as implemented in the Insight Segmentation and Registration Toolkit (ITK). The segmentation results were compared against the reference model.

RESULTS

The accuracy in determining the vessels centerline courses was very high for each tested segmentation algorithm (mean error rate = 5.6% if using VMTK). However, the estimated radii exhibited deviations from ground truth values with mean error rates ranging from 7% up to 79%, depending on the vessel size, image acquisition and segmentation method.

CONCLUSIONS

We demonstrated the practical application of the designed MRA simulator as a reliable tool for quantitative validation of MRA image processing algorithms that provides objective, reproducible results and is observer independent.

Cerebral Hemodynamics in Patients with Hemolytic Uremic Syndrome Assessed by Susceptibility Weighted Imaging and Four-Dimensional Non-Contrast MR Angiography

Background and Purpose

Conventional magnetic resonance imaging (MRI) of patients with hemolytic uremic syndrome (HUS) and neurological symptoms performed during an epidemic outbreak of Escherichia coli O104:H4 in Northern Europe has previously shown pathological changes in only approximately 50% of patients. In contrast, susceptibility-weighted imaging (SWI) revealed a loss of venous contrast in a large number of patients. We hypothesized that this observation may be due to an increase in cerebral blood flow (CBF) and aimed to identify a plausible cause.

Materials and Methods

Baseline 1.5T MRI scans of 36 patients (female, 26; male, 10; mean age, 38.2±19.3 years) were evaluated. Venous contrast was rated on standard SWI minimum intensity projections. A prototype four-dimensional (time resolved) magnetic resonance angiography (4D MRA) assessed cerebral hemodynamics by global time-to-peak (TTP), as a surrogate marker for CBF. Clinical parameters studied were hemoglobin, hematocrit, creatinine, urea levels, blood pressure, heart rate, and end-tidal CO₂.

Results

SWI venous contrast was abnormally low in 33 of 36 patients. TTP ranged from 3.7 to 10.2 frames (mean, 7.9 ± 1.4). Hemoglobin at the time of MRI (n = 35) was decreased in all patients (range, 5.0 to 12.6 g/dL; mean, 8.2 ± 1.4); hematocrit (n = 33) was abnormally low in all but a single patient (range, 14.3 to 37.2%; mean, 23.7 ± 4.2). Creatinine was abnormally high in 30 of 36 patients (83%) (range, 0.8 to 9.7; mean, 3.7 ± 2.2). SWI venous contrast correlated significantly with hemoglobin (r = 0.52, P = 0.0015), hematocrit (r = 0.65, P < 0.001), and TTP (r = 0.35, P = 0.036). No correlation of SWI with blood pressure, heart rate, end-tidal CO₂, creatinine, and urea level was observed. Findings suggest that the loss of venous contrast is related to an increase in CBF secondary to severe anemia related to HUS. SWI contrast of patients with pathological conventional MRI findings was significantly lower compared to patients with normal MRI (mean SWI score, 1.41 and 2.05, respectively; P = 0.04). In patients with abnormal conventional MRI, mean TTP (7.45), mean hemoglobin (7.65), and mean hematocrit (22.0) were lower compared to patients with normal conventional MRI scans (mean TTP = 8.28, mean hemoglobin = 8.63, mean hematocrit = 25.23).

Conclusion

In contrast to conventional MRI, almost all patients showed pathological changes in cerebral hemodynamics assessed by SWI and 4D MRA. Loss of venous contrast on SWI is most likely the result of an increase in CBF and may be related to the acute onset of anemia. Future studies will be needed to assess a possible therapeutic effect of blood transfusions in patients with HUS and neurological symptoms.

Selective arterial spin labeling in conjunction with phase-contrast acquisition for the simultaneous visualization of morphology, flow direction, and velocity of individual arteries in the cerebrovascular system

PURPOSE

In various cerebrovascular diseases the visualization of individual arteries and knowledge about their hemodynamic properties, like flow velocity and direction, can become important for an accurate diagnosis. Magnetic resonance angiography methods are intended to acquire this information, but often a single acquisition is not sufficient to retrieve all of this desired information.

METHODS

Using selective arterial spin labeling (ASL) methods, a single artery of interest can be tagged and visualized, whereas quantitative information about hemodynamics can be retrieved using phase-contrast techniques that are often limited regarding their selectivity. In this study, a method that allows for velocity mapping of individual arteries by incorporating phase-contrast preparation into selective ASL angiography measurements is presented. Several postprocessing steps are required to generate velocity and directional-encoded maps of selected arteries from the data acquired in a single scan.

RESULTS

The method was successfully evaluated in healthy volunteers, and a first application in two selected patients is presented. In one patient, an aneurysm of the middle cerebral artery is investigated, and in the second patient it is used to visualize an arterio-venous malformation.

CONCLUSION

Selective ASL imaging in conjunction with phase-contrast acquisition allows for investigating hemodynamic properties of individual arteries. Magn Reson Med 78:1469-1475, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Brain Structural and Vascular Anatomy Is Altered in Offspring of Pre-Eclamptic Pregnancies: A Pilot Study

BACKGROUND AND PURPOSE

Pre-eclampsia is a serious clinical gestational disorder occurring in 3%-5% of all human pregnancies and characterized by endothelial dysfunction and vascular complications. Offspring born of pre-eclamptic pregnancies are reported to exhibit deficits in cognitive function, higher incidence of depression, and increased susceptibility to stroke. However, no brain imaging reports exist on these offspring. We aimed to assess brain structural and vascular anatomy in 7- to 10-year-old offspring of pre-eclamptic pregnancies compared with matched controls.

MATERIALS AND METHODS

Offspring of pre-eclamptic pregnancies and matched controls (n = 10 per group) were recruited from an established longitudinal cohort examining the effects of pre-eclampsia. Children underwent MR imaging to identify brain structural and vascular anatomic differences. Maternal plasma samples collected at birth were assayed for angiogenic factors by enzyme-linked immunosorbent assay.

RESULTS

Offspring of pre-eclamptic pregnancies exhibited enlarged brain regional volumes of the cerebellum, temporal lobe, brain stem, and right and left amygdalae. These offspring displayed reduced cerebral vessel radii in the occipital and parietal lobes. Enzyme-linked immunosorbent assay analysis revealed underexpression of the placental growth factor among the maternal plasma samples from women who experienced pre-eclampsia.

CONCLUSIONS

This study is the first to report brain structural and vascular anatomic alterations in the population of offspring of pre-eclamptic pregnancies. Brain structural alterations shared similarities with those seen in autism. Vascular alterations may have preceded these structural alterations. This pilot study requires further validation with a larger population to provide stronger estimates of brain structural and vascular outcomes among the offspring of pre-eclamptic pregnancies.

Automatic SWI Venography Segmentation Using Conditional Random Fields

Susceptibility-weighted imaging (SWI) venography can produce detailed venous contrast and complement arterial dominated MR angiography (MRA) techniques. However, these dense reversed-contrast SWI venograms pose new segmentation challenges. We present an automatic method for whole-brain venous blood segmentation in SWI using Conditional Random Fields (CRF). The CRF model combines different first and second order potentials. First-order association potentials are modeled as the composite of an appearance potential, a Hessian-based shape potential and a non-linear location potential. Second-order interaction potentials are modeled using an auto-logistic (smoothing) potential and a data-dependent (edge) potential. Minimal post-processing is used for excluding voxels outside the brain parenchyma and visualizing the surface vessels. The CRF model is trained and validated using 30 SWI venograms acquired within a population of deep brain stimulation (DBS) patients (age range [Formula: see text] years). Results demonstrate robust and consistent segmentation in deep and sub-cortical regions (median kappa = 0.84 and 0.82), as well as in challenging mid-sagittal and surface regions (median kappa = 0.81 and 0.83) regions. Overall, this CRF model produces high-quality segmentation of SWI venous vasculature that finds applications in DBS for minimizing hemorrhagic risks and other surgical and non-surgical applications.

Aneurysm Recurrence Volumetry Is More Sensitive than Visual Evaluation of Aneurysm Recurrences

PURPOSE

Considerable inter-observer variability in the visual assessment of aneurysm recurrences limits its use as an outcome parameter evaluating new coil generations. The purpose of this study was to compare visual assessment of aneurysm recurrences and aneurysm recurrence volumetry with an example dataset of HydroSoft coils (HSC) versus bare platinum coils (BPC).

METHODS

For this retrospective study, 3-dimensional time-of-flight magnetic resonance angiography datasets acquired 6 and 12 months after endovascular therapy using BPC only or mainly HSC were analyzed. Aneurysm recurrence volumes were visually rated by two observersas well as quantified by subtraction of the datasets after intensity-based rigid registration.

RESULTS

A total of 297 aneurysms were analyzed (BPC: 169, HSC: 128). Recurrences were detected by aneurysm recurrence volumetry in 9 of 128 (7.0 %) treated with HSC and in 24 of 169 (14.2 %) treated with BPC (odds ratio: 2.39, 95 % confidence interval: 1.05-5.48; P = 0.039). Aneurysm recurrence volumetry revealed an excellent correlation between observers (Cronbach's alpha = 0.93). In contrast, no significant difference in aneurysm recurrence was found for visual assessment (3.9 % in HSC cases and 4.7 % in BPC cases). Recurrences were observed in aneurysms smaller than the sample median in 10 of 33 (30.3 %) by aneurysm recurrence volumetry and in 1 of 13 (7.7 %) by visual assessment.

CONCLUSIONS

Aneurysm recurrences were detected more frequently by aneurysm recurrence volumetry when compared with visual assessment. By using aneurysm recurrence volumetry, differences between treatment groups were detected with higher sensitivity and inter-observer validity probably because of the higher detection rate of recurrences in small aneurysms.

Aligning 3D time-of-flight MRA datasets for quantitative longitudinal studies: evaluation of rigid registration techniques

OBJECTIVE

3D Time-of-flight (TOF) magnetic resonance angiography is commonly used for vascular analyses. A quantification of longitudinal morphological changes usually requires the registration of TOF image sequences acquired at different time points. The aim of this study was to evaluate the precision of different 3D rigid registration setups such that an optimal quantification of morphological changes can be achieved.

METHODS

Eight different rigid registration techniques were implemented and evaluated in this study using the target registration error (TRE) calculated based on 554 landmarks defined in twenty TOF datasets. The registration techniques differed in integration of brain and vessel segmentation masks and usage of a multi-resolution framework. Furthermore, the benefit of a prior volume-of-interest definition for registration accuracy was evaluated.

RESULTS

The results revealed that the highest registration accuracies can be achieved using a multi-resolution framework and a cerebrovascular segmentation as mask. Numerically, a mean TRE of 1.1mm was calculated. If applicable, a prior definition of a volume-of-interest allows a reduction of the TRE to only 0.6mm.

CONCLUSION

TOF datasets should be registered using vessel segmentations as mask, multi-resolution framework and previous volume-of-interest definition if possible to obtain the highest registration precision. This is especially the case for longitudinal datasets that are separated by several months while the registration technique seems less important for datasets that are only separated by a few days.

Automatic anatomical labeling of the complete cerebral vasculature in mouse models

Study of cerebral vascular structure broadens our understanding of underlying variations, such as pathologies that can lead to cerebrovascular disorders. The development of high resolution 3D imaging modalities has provided us with the raw material to study the blood vessels in small animals such as mice. However, the high complexity and 3D nature of the cerebral vasculature make comparison and analysis of the vessels difficult, time-consuming and laborious. Here we present a framework for automated segmentation and recognition of the cerebral vessels in high resolution 3D images that addresses this need. The vasculature is segmented by following vessel center lines starting from automatically generated seeds and the vascular structure is represented as a graph. Each vessel segment is represented as an edge in the graph and has local features such as length, diameter, and direction, and relational features representing the connectivity of the vessel segments. Using these features, each edge in the graph is automatically labeled with its anatomical name using a stochastic relaxation algorithm. We have validated our method on micro-CT images of C57Bl/6J mice. A leave-one-out test performed on the labeled data set demonstrated the recognition rate for all vessels including major named vessels and their minor branches to be >75%. This automatic segmentation and recognition methods facilitate the comparison of blood vessels in large populations of subjects and allow us to study cerebrovascular variations.

A statistical cerebroarterial atlas derived from 700 MRA datasets

OBJECTIVES

The cerebroarterial system is a complex network of arteries that supply the brain cells with vitally important nutrients and oxygen. The inter-individual differences of the cerebral arteries, especially at a finer level, are still not understood sufficiently. The aim of this work is to present a statistical cerebroarterial atlas that can be used to overcome this problem.

METHODS

Overall, 700 Time-of-Flight (TOF) magnetic resonance angiography (MRA) datasets of healthy subjects were used for atlas generation. Therefore, the cerebral arteries were automatically segmented in each dataset and used for a quantification of the vessel diameters. After this, each TOF MRA dataset as well as the corresponding vessel segmentation and vessel diameter dataset were registered to the MNI brain atlas. Finally, the registered datasets were used to calculate a statistical cerebroarterial atlas that incorporates information about the average TOF intensity, probability for a vessel occurrence and mean vessel diameter for each voxel.

RESULTS

Visual analysis revealed that arteries with a diameter as small as 0.5 mm are well represented in the atlas with quantitative values that are within range of anatomical reference values. Moreover, a highly significant strong positive correlation between the vessel diameter and occurrence probability was found. Furthermore, it was shown that an intensity-based automatic segmentation of cerebral vessels can be considerable improved by incorporating the atlas information leading to results within the range of the inter-observer agreement.

CONCLUSION

The presented cerebroarterial atlas seems useful for improving the understanding about normal variations of cerebral arteries, initialization of cerebrovascular segmentation methods and may even lay the foundation for a reliable quantification of subtle morphological vascular changes.

Computer-aided nidus segmentation and angiographic characterization of arteriovenous malformations

PURPOSE

Exact knowledge about the nidus of an arteriovenous malformation (AVM) and the connected vessels is often required for image-based research projects and optimal therapy planning. The aim of this work is to present and evaluate a computer-aided nidus segmentation technique and subsequent angiographic characterization of the connected vessels that can be visualized in 3D.

METHODS

The proposed method was developed and evaluated based on 15 datasets of patients with an AVM. Each dataset consists of a high-resolution 3D and a 4D magnetic resonance angiography (MRA) image sequence. After automatic cerebrovascular segmentation from the 3D MRA dataset, a voxel-wise support vector machine classification based on four extracted features is performed to generate a new parameter map. The nidus is represented by positive values in this parameter map and can be extracted using volume growing. Finally, the nidus segmentation is dilated and used for an automatic identification of feeding arteries and draining veins by integrating hemodynamic information from the 4D MRA datasets.

RESULTS

A quantitative comparison of the computer-aided AVM nidus segmentation results to manual gold-standard segmentations by two observers revealed a mean Dice coefficient of 0.835, which is comparable to the inter-observer agreement for which a mean Dice coefficient of 0.830 was determined. The angiographic characterization was visually rated feasible for all patients.

CONCLUSION

The presented computer-aided method enables a reproducible and fast extraction of the AVM nidus as well as an automatic angiographic characterization of the connected vessels, which can be used to support image-based research projects and therapy planning of AVMs.

Automatic correction of gaps in cerebrovascular segmentations extracted from 3D time-of-flight MRA datasets

OBJECTIVES

Exact cerebrovascular segmentations are required for several applications in today's clinical routine. A major drawback of typical automatic segmentation methods is the occurrence of gaps within the segmentation. These gaps are typically located at small vessel structures exhibiting low intensities. Manual correction is very time-consuming and not suitable in clinical practice. This work presents a post-processing method for the automatic detection and closing of gaps in cerebrovascular segmentations.

METHODS

In this approach, the 3D centerline is calculated from an available vessel segmentation, which enables the detection of corresponding vessel endpoints. These endpoints are then used to detect possible connections to other 3D centerline voxels with a graph-based approach. After consistency check, reasonable detected paths are expanded to the vessel boundaries using a level set approach and combined with the initial segmentation.

RESULTS

For evaluation purposes, 100 gaps were artificially inserted at non-branching vessels and bifurcations in manual cerebrovascular segmentations derived from ten Time-of-Flight magnetic resonance angiography datasets. The results show that the presented method is capable of detecting 82% of the non-branching vessel gaps and 84% of the bifurcation gaps. The level set segmentation expands the detected connections with 0.42 mm accuracy compared to the initial segmentations. A further evaluation based on 10 real automatic segmentations from the same datasets shows that the proposed method detects 35 additional connections in average per dataset, whereas 92.7% were rated as correct by a medical expert.

CONCLUSION

The presented approach can considerably improve the accuracy of cerebrovascular segmentations and of following analysis outcomes.