Skeletonization method for vessel delineation of arteriovenous malformation

Babin, Danilo; Pižurica, Aleksandra; Velicki, Lazar; Matić, Viktor; Galić, Irena; Leventić, Hrvoje; Zlokolica, Vladimir; Philips, Wilfried

doi:10.1016/j.compbiomed.2017.12.011

Cited by 26 publications

(16 citation statements)

References 17 publications

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“…While X-ray may be sufficient for initial diagnosis, it is not adequate for AAA detection and follow-up. Magnetic resonance (MRI) offers minimally invasive acquisition, but the AAA examination is substantially lengthier [3][4][5][6][7]. On the other hand, CT has higher resolution and better contras and better captures entire access vessels and aorta.…”

Section: A Related Workmentioning

confidence: 99%

Abdominal Aortic Aneurysm Segmentation from CT Images using Modified 3D U-Net with Deep Supervision

Habijan

Galić

Leventić

et al. 2020

2020 International Symposium ELMAR

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An abdominal aortic aneurysm (AAA) is a dangerous cardiovascular disease that can cause serious health complications and death. Methods that can provide automatic and accurate segmentation of the AAA can significantly help in preoperative planning and postoperative follow-ups. Therefore, in this work, we present an automatic method for AAA segmentation from CT images using a modified 3D U-Net network with deep supervision. We compare obtained results for AAA segmentation using original 3D U-Net, and modified 3D U-Net with deep supervision. The trained network is evaluated on 19 volumetric CT images from the publicly available dataset provided by the University Hospitals Leuven, Belgium, using four-fold cross-validation. We obtained DSC of 91.03% using modified 3D U-Net with deep supervision. Additionally, we provide a discussion of the effects of using up-sampling versus deconvolution layers and its influence on the performance of both networks for this specific clinical application.

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Section: A Related Workmentioning

confidence: 99%

Abdominal Aortic Aneurysm Segmentation from CT Images using Modified 3D U-Net with Deep Supervision

Habijan

Galić

Leventić

et al. 2020

2020 International Symposium ELMAR

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“…Previous work [ 19 , 33 ] does not describe an efficient implementation of topology extraction for large input datasets. For example, Chen et al [ 19 ] perform a tree search following skeleton voxels through the volume.…”

Section: Methodsmentioning

confidence: 99%

“…In [ 33 ], the authors use a non-topology preserving distance field guided voxel thinning algorithm. They construct a graph from an intermediate representation based on voxel neighborhood, but do not describe an efficient implementation.…”

Section: Introductionmentioning

confidence: 99%

Scalable robust graph and feature extraction for arbitrary vessel networks in large volumetric datasets

Drees

Scherzinger²,

Hägerling

et al. 2021

BMC Bioinformatics

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Background Recent advances in 3D imaging technologies provide novel insights to researchers and reveal finer and more detail of examined specimen, especially in the biomedical domain, but also impose huge challenges regarding scalability for automated analysis algorithms due to rapidly increasing dataset sizes. In particular, existing research towards automated vessel network analysis does not always consider memory requirements of proposed algorithms and often generates a large number of spurious branches for structures consisting of many voxels. Additionally, very often these algorithms have further restrictions such as the limitation to tree topologies or relying on the properties of specific image modalities. Results We propose a scalable iterative pipeline (in terms of computational cost, required main memory and robustness) that extracts an annotated abstract graph representation from the foreground segmentation of vessel networks of arbitrary topology and vessel shape. The novel iterative refinement process is controlled by a single, dimensionless, a-priori determinable parameter. Conclusions We are able to, for the first time, analyze the topology of volumes of roughly 1 TB on commodity hardware, using the proposed pipeline. We demonstrate improved robustness in terms of surface noise, vessel shape deviation and anisotropic resolution compared to the state of the art. An implementation of the presented pipeline is publicly available in version 5.1 of the volume rendering and processing engine Voreen.

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“…Regarding AVM, Babin and colleagues used a graph-based skeletonization method for vessel delineation in arteriovenous malformations with the main purpose of AVM decomposition into veins, arteries and the nidus (see Fig. 2) [2]. Similarly, the teams of Ogard, and also Sandoval-Garcia used advanced angiographic exploration in AVM patients with both 4D DSA and 2D DSA [37,38,45,46].…”

Section: Advanced Dsamentioning

confidence: 99%

“…A skeleton of the 3-D images of the vascular tree is generated in order to separate the segments of the AVM. From Babin et al with kind permission from Elsevier [2]. Time-resolved MRI sequences allow similar decomposition of vascular segments still leaves a number of structures as possible AVMs.…”

Section: Mechanical Conceptsmentioning

confidence: 99%

Recent progress understanding pathophysiology and genesis of brain AVM—a narrative review

Steiger

2021

Neurosurg Rev

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Considerable progress has been made over the past years to better understand the genetic nature and pathophysiology of brain AVM. For the actual review, a PubMed search was carried out regarding the embryology, inflammation, advanced imaging, and fluid dynamical modeling of brain AVM. Whole-genome sequencing clarified the genetic origin of sporadic and familial AVM to a large degree, although some open questions remain. Advanced MRI and DSA techniques allow for better segmentation of feeding arteries, nidus, and draining veins, as well as the deduction of hemodynamic parameters such as flow and pressure in the individual AVM compartments. Nonetheless, complete modeling of the intranidal flow structure by computed fluid dynamics (CFD) is not possible so far. Substantial progress has been made towards understanding the embryology of brain AVM. In contrast to arterial aneurysms, complete modeling of the intranidal flow and a thorough understanding of the mechanical properties of the AVM nidus are still lacking at the present time.

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Skeletonization method for vessel delineation of arteriovenous malformation

Cited by 26 publications

References 17 publications

Abdominal Aortic Aneurysm Segmentation from CT Images using Modified 3D U-Net with Deep Supervision

Abdominal Aortic Aneurysm Segmentation from CT Images using Modified 3D U-Net with Deep Supervision

Scalable robust graph and feature extraction for arbitrary vessel networks in large volumetric datasets

Recent progress understanding pathophysiology and genesis of brain AVM—a narrative review

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