A 2D driven 3D vessel segmentation algorithm for 3D digital subtraction angiography data

DIAS: A dataset and benchmark for intracranial artery segmentation in DSA sequences

The automated segmentation of Intracranial Arteries (IA) in Digital Subtraction Angiography (DSA) plays a crucial role in the quantification of vascular morphology, significantly contributing to computer-assisted stroke research and clinical practice. Current research primarily focuses on the segmentation of single-frame DSA using proprietary datasets. However, these methods face challenges due to the inherent limitation of single-frame DSA, which only partially displays vascular contrast, thereby hindering accurate vascular structure representation. In this work, we introduce DIAS, a dataset specifically developed for IA segmentation in DSA sequences. We establish a comprehensive benchmark for evaluating DIAS, covering full, weak, and semi-supervised segmentation methods. Specifically, we propose the vessel sequence segmentation network, in which the sequence feature extraction module effectively captures spatiotemporal representations of intravascular contrast, achieving intracranial artery segmentation in 2D+Time DSA sequences. For weakly-supervised IA segmentation, we propose a novel scribble learning-based image segmentation framework, which, under the guidance of scribble labels, employs cross pseudo-supervision and consistency regularization to improve the performance of the segmentation network. Furthermore, we introduce the random patch-based self-training framework, aimed at alleviating the performance constraints encountered in IA segmentation due to the limited availability of annotated DSA data. Our extensive experiments on the DIAS dataset demonstrate the effectiveness of these methods as potential baselines for future research and clinical applications. The dataset and code are publicly available at https://doi.org/10.5281/zenodo.11401368 and https://github.com/lseventeen/DIAS.

Using temporal and structural data to reconstruct 3D cerebral vasculature from a pair of 2D digital subtraction angiography sequences

PURPOSE

The purpose of this work is to present a new method for reconstructing patient-specific three-dimensional (3D) vasculature of the brain from a pair of digital subtraction angiography (DSA) image sequences from different viewpoints, e.g., from bi-plane angiography. Our long-term goal is to provide high resolution visualization of 3D vasculature with dynamic flow of contrast agent from limited data that is readily available during surgical procedures. The proposed method is the second of a three-stage process composed of 1) augmenting vessel segmentation with vessel radii and timing of the arrival of a bolus of contrast agent, 2) reconstructing a volumetric representation of the augmented vessel data from the augmented 2D segmentations, and 3) generating a 3D model of vessels and flow of contrast agent from the volumetric reconstruction. Unlike previous methods, which are either limited to relatively simple vessel structures or rely on multiple views and/or prior models of the vasculature, our method requires only a single pair of 2D DSA sequences taken from different view directions.

METHODS

We developed a new mathematical algorithm that augments vessel centerlines with vessel radii and bolus arrival times derived directly from the 2D DSA sequences to constrain the 3D reconstruction. We validated this method on digital phantoms derived from clinical data and from fractal models of branching tree structures.

RESULTS

In standard reconstruction methods, reconstruction by projection of two views into 3D space results in 'ghosting' artifacts, i.e., false 3D structure that occurs where vessels or vessel segments overlap in the 2D images. For the complex vascular of the brain, this ghosting is severe and is a major hurdle for methods that attempt to generate 3D structure from 2D images. We show that our approach reduces ghosting by up to 99% in digital phantoms derived from clinical data.

CONCLUSION

Our dramatic reduction in ghosting artifacts in 3D reconstructions from a pair of 2D image sequences is an important step towards generating high resolution 3D vasculature with dynamic flow information from a single DSA sequence acquired using bi-plane angiography.

Automatic centerline extraction of cerebrovascular in 4D CTA based on tubular features

Vascular centerlines have crucial significance in reconstruction, registration, segmentation and vascular parameter analysis. The extraction of vessel structures remains a difficult problem in the completeness and continuity of results. In this paper, we present a novel method to extract cerebrovascular centerlines from four-dimensional computed tomography angiography images. Tubular features and vascular directions are used to extract initial centerlines, and the offset correction is introduced in the vascular orthogonal plane. In addition, we also present a post-processing method to connect interruptions of centerlines. We perform a quantitative validation using clinical images and public data sets of MRA brain images. Our experimental results demonstrate that the proposed algorithm not only shows higher accuracy in complicated vessel structures, but also outperforms previous approaches in terms of high validity and universality.

Three-Dimensional Reconstruction of Blood Vessels of the Human Retina by Fractal Interpolation

In this work, data from two-dimensional (2D) images of the human retina were taken as a case study. First, the characteristic data points had been removed using the Douglas-Peucker (DP) method, and subsequently, more data points were added using random fractal interpolation approach, to reconstruct a three-dimensional (3D) model of the blood vessel. By visualizing the result, we can see that all the small blood vessels in the human retina are more visible and detailed. This algorithm of 3D reconstruction has the advantage of being fast with calculation time less than 40 s and also can reduce the 3D image storage level on a disk with a reduction ratio between 78% and 96.65%.

A fully-automatic locally adaptive thresholding algorithm for blood vessel segmentation in 3D digital subtraction angiography

Subarachnoid hemorrhage due to a ruptured cerebral aneurysm is still a devastating disease. Planning of endovascular aneurysm therapy is increasingly based on hemodynamic simulations necessitating reliable vessel segmentation and accurate assessment of vessel diameters. In this work, we propose a fully-automatic, locally adaptive, gradient-based thresholding algorithm. Our approach consists of two steps. First, we estimate the parameters of a global thresholding algorithm using an iterative process. Then, a locally adaptive version of the approach is applied using the estimated parameters. We evaluated both methods on 8 clinical 3D DSA cases. Additionally, we propose a way to select a reference segmentation based on 2D DSA measurements. For large vessels such as the internal carotid artery, our results show very high sensitivity (97.4%), precision (98.7%) and Dice-coefficient (98.0%) with our reference segmentation. Similar results (sensitivity: 95.7%, precision: 88.9% and Dice-coefficient: 90.7%) are achieved for smaller vessels of approximately 1mm diameter.