Automated extraction and labelling of the arterial tree from whole-body MRA data

Shahzad, Rahil; Dzyubachyk, Oleh; Staring, Marius; Kullberg, Joel; Johansson, Lars; Åhlström, Håkan; Lelieveldt, Boudewijn P. F.; Geest, Rob J. van der

doi:10.1016/j.media.2015.05.008

Cited by 8 publications

(5 citation statements)

References 44 publications

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“…Should the group-wise analysis of the vasculature determine a valid anatomical prior for extracting more accurate and refined subject-specific vascular graphs, at the same time, the novel vectorial approach could potentially impact on traditional vascular image analyses. Similarly to [60], by means of longitudinal and cross-sectional vascular graph-matching, registration and alignment, an over-complete graph of the cerebrovascular system could be determined with both arterial and venous components, and would ideally constitute a comprehensive, quantitative and data-driven vascular atlas of the human brain. This would support, in future works, a better understanding of the morphological and functional normality of the neurovascular system, also of the associated variability and pathology.…”

Section: Discussionmentioning

confidence: 99%

Inference of Cerebrovascular Topology With Geodesic Minimum Spanning Trees

Moriconi

Zuluaga

Jäger

et al. 2019

IEEE Trans. Med. Imaging

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

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Section: Discussionmentioning

confidence: 99%

Inference of Cerebrovascular Topology With Geodesic Minimum Spanning Trees

Moriconi

Zuluaga

Jäger

et al. 2019

IEEE Trans. Med. Imaging

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“…Therefore, automated algorithms are highly desirable; however, automating blood vessel segmentation is associated with challenges, including hardware imperfections, extreme data imbalance, complex geometry of the blood vessels, and heterogeneous tissue near the blood vessels. Several algorithms have been developed to address the aforementioned challenges 9,10 . These algorithms can be categorized into 2 groups: (1) classical techniques that rely on combinations of geometry, appearance, and statistical model‐based approaches with morphologic and handcrafted intensity‐based features; 9,11‐16 and (2) learning‐based techniques that can adaptively find highly representative features by training on the data 10,17‐35 .…”

Section: Introductionmentioning

confidence: 99%

“…9,10 These algorithms can be categorized into 2 groups: (1) classical techniques that rely on combinations of geometry, appearance, and statistical model-based approaches with morphologic and handcrafted intensitybased features; 9,[11][12][13][14][15][16] and (2) learning-based techniques that can adaptively find highly representative features by training on the data. 10,[17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] Lei et al 9 proposed a semiautomatic fuzzy connectedness algorithm for pelvic vessel segmentation. They used fuzzy connectedness to extract the entire vascular bed from the background, followed by separation of arteries and veins in an iterative process.…”

Section: Introductionmentioning

confidence: 99%

“…Several algorithms have been developed to address the aforementioned challenges 9,10 . These algorithms can be categorized into 2 groups: (1) classical techniques that rely on combinations of geometry, appearance, and statistical model‐based approaches with morphologic and handcrafted intensity‐based features; 9,11‐16 and (2) learning‐based techniques that can adaptively find highly representative features by training on the data 10,17‐35 . Lei et al 9 proposed a semi‐automatic fuzzy connectedness algorithm for pelvic vessel segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…They used fuzzy connectedness to extract the entire vascular bed from the background, followed by separation of arteries and veins in an iterative process. Shahzad et al 10 proposed an automatic multi‐atlas‐based approach to extract and label the arterial skeleton from whole‐body MRA images. Their algorithm learns the anatomic knowledge from several atlases to initialize labels for the arterial skeleton, which are then refined via a rule based approach.…”

Section: Introductionmentioning

confidence: 99%

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

Ghodrati

Rivenson

Prosper

et al. 2021

Magnetic Resonance in Med

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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.

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Automatic Aorta Segmentation with Heavily Augmented, High-Resolution 3-D ResUNet: Contribution to the SEG.A Challenge

Wodzinski,

Müller

2024

Segmentation of the Aorta. Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree From Multicenter

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Automated extraction and labelling of the arterial tree from whole-body MRA data

Cited by 8 publications

References 44 publications

Inference of Cerebrovascular Topology With Geodesic Minimum Spanning Trees

Inference of Cerebrovascular Topology With Geodesic Minimum Spanning Trees

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

Automatic Aorta Segmentation with Heavily Augmented, High-Resolution 3-D ResUNet: Contribution to the SEG.A Challenge

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