BRAVE-NET: Fully Automated Arterial Brain Vessel Segmentation In Patients with Cerebrovascular Disease

Hilbert, Adam; Madai, Vince I.; Akay, E.; Aydin, Orhun Utku; Behland, Jonas; Sobesky, Jan; Galinović, Ivana; Khalil, Ahmed A.; Taha, Abdel Aziz; uumlrfel, Jens W; Dušek, Petr; Niendorf, Thoralf; Fiebach, Jochen B.; Frey, Dietmar; Livne, Michelle

doi:10.1101/2020.04.08.20057570

Cited by 14 publications

(21 citation statements)

References 45 publications

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“…Sixth, we included an external validation set to demonstrate the applicability of our framework with regards to no need of pre-processing steps, and test the generalization of our models on a public, out-of-sample dataset. Here, we made the contribution to generate 20 high quality ground truth labels for voxel-wise segmentation and make them publicly available (Hilbert et al, 2020 ). We advise future works to use these as an external test set in order to create a public benchmark for brain vessel segmentation.…”

Section: Discussionmentioning

confidence: 99%

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BRAVE-NET: Fully Automated Arterial Brain Vessel Segmentation in Patients With Cerebrovascular Disease

Hilbert

Madai

Akay

et al. 2020

Front. Artif. Intell.

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Introduction: Arterial brain vessel assessment is crucial for the diagnostic process in patients with cerebrovascular disease. Non-invasive neuroimaging techniques, such as time-of-flight (TOF) magnetic resonance angiography (MRA) imaging are applied in the clinical routine to depict arteries. They are, however, only visually assessed. Fully automated vessel segmentation integrated into the clinical routine could facilitate the time-critical diagnosis of vessel abnormalities and might facilitate the identification of valuable biomarkers for cerebrovascular events. In the present work, we developed and validated a new deep learning model for vessel segmentation, coined BRAVE-NET, on a large aggregated dataset of patients with cerebrovascular diseases. Methods: BRAVE-NET is a multiscale 3-D convolutional neural network (CNN) model developed on a dataset of 264 patients from three different studies enrolling patients with cerebrovascular diseases. A context path, dually capturing high-and low-resolution volumes, and deep supervision were implemented. The BRAVE-NET model was compared to a baseline Unet model and variants with only context paths and deep supervision, respectively. The models were developed and validated using high-quality manual labels as ground truth. Next to precision and recall, the performance was assessed quantitatively by Dice coefficient (DSC); average Hausdorff distance (AVD); 95-percentile Hausdorff distance (95HD); and via visual qualitative rating. Results: The BRAVE-NET performance surpassed the other models for arterial brain vessel segmentation with a DSC = 0.931, AVD = 0.165, and 95HD = 29.153. The BRAVE-NET model was also the most resistant toward false labelings as revealed by the visual analysis. The performance improvement is primarily attributed to the integration Hilbert et al. Fully-Automated Arterial Brain Vessel Segmentation of the multiscaling context path into the 3-D Unet and to a lesser extent to the deep supervision architectural component. Discussion: We present a new state-of-the-art of arterial brain vessel segmentation tailored to cerebrovascular pathology. We provide an extensive experimental validation of the model using a large aggregated dataset encompassing a large variability of cerebrovascular disease and an external set of healthy volunteers. The framework provides the technological foundation for improving the clinical workflow and can serve as a biomarker extraction tool in cerebrovascular diseases.

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Section: Discussionmentioning

confidence: 99%

“…The same labeling procedure was applied as for 1000Plus data, described in the Data labeling section. Ground truth labels for the external validation are published under (Hilbert et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

BRAVE-NET: Fully Automated Arterial Brain Vessel Segmentation in Patients With Cerebrovascular Disease

Hilbert

Madai

Akay

et al. 2020

Front. Artif. Intell.

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“…With the advent of powerful machine learning segmentation methods in recent years, it is very likely that these steps can be automated with sufficient performance. For segmentation, our group has just recently presented deep learning methods to segment the vasculature from structural scans with very high accuracy [ 25 , 34 ]. The application of deep learning for skeletonization and automated annotation is a current focus of our group.…”

Section: Discussionmentioning

confidence: 99%

A precision medicine framework for personalized simulation of hemodynamics in cerebrovascular disease

et al. 2021

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Background Cerebrovascular disease, in particular stroke, is a major public health challenge. An important biomarker is cerebral hemodynamics. To measure and quantify cerebral hemodynamics, however, only invasive, potentially harmful or time-to-treatment prolonging methods are available. Results We present a simulation-based approach which allows calculation of cerebral hemodynamics based on the patient-individual vessel configuration derived from structural vessel imaging. For this, we implemented a framework allowing segmentation and annotation of brain vessels from structural imaging followed by 0-dimensional lumped simulation modeling of cerebral hemodynamics. For annotation, a 3D-graphical user interface was implemented. For 0D-simulation, we used a modified nodal analysis, which was adapted for easy implementation by code. The simulation enables identification of areas vulnerable to stroke and simulation of changes due to different systemic blood pressures. Moreover, sensitivity analysis was implemented allowing the live simulation of changes to simulate procedures and disease progression. Beyond presentation of the framework, we demonstrated in an exploratory analysis in 67 patients that the simulation has a high specificity and low-to-moderate sensitivity to detect perfusion changes in classic perfusion imaging. Conclusions The presented precision medicine approach using novel biomarkers has the potential to make the application of harmful and complex perfusion methods obsolete.

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“…Moreover, U‐Net framework and its extension have shown excellent performance on medical image segmentation tasks. Livne et al 19 and Hilbert et al 40 performed cerebrovascular segmentation using U‐Net and achieved acceptable results. The difference was that the latter integrated multiscaling and deep supervision based on U‐Net framework.…”

Section: Related Workmentioning

confidence: 99%

A nested parallel multiscale convolution for cerebrovascular segmentation

et al. 2021

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Purpose: Cerebrovascular segmentation in magnetic resonance imaging (MRI) plays an important role in the diagnosis and treatment of cerebrovascular diseases. Many segmentation frameworks based on convolutional neural networks (CNNs) or U‐Net‐like structures have been proposed for cerebrovascular segmentation. Unfortunately, the segmentation results are still unsatisfactory, particularly in the small/thin cerebrovascular due to the following reasons: (1) the lack of attention to multiscale features in encoder caused by the convolutions with single kernel size; (2) insufficient extraction of shallow and deep‐seated features caused by the depth limitation of transmission path between encoder and decoder; (3) insufficient utilization of the extracted features in decoder caused by less attention to multiscale features. Methods: Inspired by U‐Net++, we propose a novel 3D U‐Net‐like framework termed Usception for small cerebrovascular. It includes three blocks: Reduction block, Gap block, and Deep block, aiming to: (1) improve feature extraction ability by grouping different convolution sizes; (2) increase the number of multiscale features in different layers by grouping paths of different depths between encoder and decoder; (3) maximize the ability of decoder in recovering multiscale features from Reduction and Gap block by using convolutions with different kernel sizes. Results: The proposed framework is evaluated on three public and in‐house clinical magnetic resonance angiography (MRA) data sets. The experimental results show that our framework reaches an average dice score of 69.29%, 87.40%, 77.77% on three data sets, which outperform existing state‐of‐the‐art methods. We also validate the effectiveness of each block through ablation experiments. Conclusions: By means of the combination of Inception‐ResNet and dimension‐expanded U‐Net++, the proposed framework has demonstrated its capability to maximize multiscale feature extraction, thus achieving competitive segmentation results for small cerebrovascular.

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BRAVE-NET: Fully Automated Arterial Brain Vessel Segmentation In Patients with Cerebrovascular Disease

Cited by 14 publications

References 45 publications

BRAVE-NET: Fully Automated Arterial Brain Vessel Segmentation in Patients With Cerebrovascular Disease

BRAVE-NET: Fully Automated Arterial Brain Vessel Segmentation in Patients With Cerebrovascular Disease

A precision medicine framework for personalized simulation of hemodynamics in cerebrovascular disease

A nested parallel multiscale convolution for cerebrovascular segmentation

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