Segmentation techniques of brain arteriovenous malformations for 3D visualization: a systematic review

BACKGROUND Visualization, analysis and characterization of the angioarchitecture of a brain arteriovenous malformation (bAVM) present crucial steps for understanding and management of these complex lesions. Three-dimensional (3D) segmentation and 3D visualization of bAVMs play hereby a significant role. We performed a systematic review regarding currently available 3D segmentation and visualization techniques for bAVMs. METHODS PubMed, Embase and Google Scholar were searched to identify studies reporting 3D segmentation techniques applied to bAVM characterization. Category of input scan, segmentation (automatic, semiautomatic, manual), time needed for segmentation and 3D visualization techniques were noted. RESULTS Thirty-three studies were included. Thirteen (39%) used MRI as baseline imaging modality, 9 used DSA (27%), and 7 used CT (21%). Segmentation through automatic algorithms was used in 20 (61%), semiautomatic segmentation in 6 (18%), and manual segmentation in 7 (21%) studies. Median automatic segmentation time was 10 min (IQR 33), semiautomatic 25 min (IQR 73). Manual segmentation time was reported in only one study, with the mean of 5-10 min. Thirty-two (97%) studies used screens to visualize the 3D segmentations outcomes and 1 (3%) study utilized a heads-up display (HUD). Integration with mixed reality was used in 4 studies (12%). CONCLUSIONS A golden standard for 3D visualization of bAVMs does not exist. This review describes a tendency over time to base segmentation on algorithms trained with machine learning. Unsupervised fuzzy-based algorithms thereby stand out as potential preferred strategy. Continued efforts will be necessary to improve algorithms, integrate complete hemodynamic assessment and find innovative tools for tridimensional visualization

Computer aided diagnosis of cerebrovascular disease based on DSA image

In recent years, the incidence of cerebrovascular diseases in China has shown a significant upward trend, and it has become a common disease threatening people's lives. Digital Subtraction Angiography (DSA) is the gold standard for the diagnosis of clinical cerebrovascular disease, and it is the most direct method to check the brain lesion. At present, there are the following two problems in the clinical research of DSA images: DSA is a real-time image with numerous frames, containing much useless information in frames; thus, human interpretation and annotation are time-consuming and labor-intensive. The blood vessel structure in DSA images is so complicated that high practical skills are required for clinicians. In the computer-aided diagnosis of DSA sequence images, there is currently a lack of automatic and effective computer-aided diagnosis algorithms for cerebrovascular diseases. Based on the above issues, the main work of this paper is as follows: 1.A multi-target detection algorithm based on Faster-RCNN is designed and applied to the analysis of brain DSA images. The algorithm divides DSA images into arterial phase, capillary phase, pre-venous phase and sinus phase by identifying the main blood vessel structure in each frame. And on this basis, we analyze the time relationship between the time phases. 2.On the basis of DSA phase detection, a key frame location algorithm based on single blood vessel structure detection is designed for moyamoya disease. First, the target detection model is applied to locate the internal carotid artery and the Willis circle. Then, five frames of images are extracted from the arterial period as keyframes. Finally, the nidus' ROI is determined according to the position of the internal carotid artery. 3.A diagnostic method for cerebral arteriovenous malformation (AVM) is designed, which combines temporal features and radiomics features. First, on the basis of DSA time phase detection, we propose a deep learning network to extract vascular time features from the DSA video; then, the time feature is combined with the radiomics features of the static keyframe to establish an AVM diagnosis model. While assisting diagnosis, this method does not require any human intervention, and reduces the workload of clinicians. The diagnostic model that combines time features and radiomics features is applied to the study of AVM staging. The experimental results prove that the classification model trained by fusion features has better diagnostic performance than the model trained by either time features or radiomics features. Based on the above three parts, this paper establishes a cerebrovascular disease analysis framework based on radiomics method and deep learning. We introduce corresponding solutions for DSA automatic image reading, rapid diagnosis of moyamoya disease, and precise diagnosis of AVM. The method proposed in this paper has practical significance for assisting the diagnosis of cerebrovascular disease and reducing the burden of medical staff.Digital Subtraction Angiography(DSA), Radiomics analysis, Arteriovenous malformations, Moyamoya, Faster-RCNN, Temporal features, Fusion feature

Skeletonization method for vessel delineation of arteriovenous malformation

Cerebral arteriovenous malformation (AVM) presents a great health threat due to its high probability of rupture that can cause severe brain damage. Image segmentation alone is not sufficient to support AVM embolization procedure. In order to successfully navigate the catheter and perform embolization, the segmented blood vessels need to be classified into feeding arteries, draining veins and the AVM nidus. For this reason we address here the AVM localization and vessel decomposition problem. We propose in this paper a novel AVM localization and vessel delineation method using ordered thinning-based skeletonization. The main focus of vessel delineation is the delineation of draining veins since it is essential for the embolization procedure. The main contribution is a graph-based method for exact extraction of draining veins which, in combination with our earlier work on AVM detection, allows the AVM decomposition into veins, arteries and the nidus (with an emphasis on the draining veins). We validate the proposed approach on blood vessel phantoms representing the veins and the AVM structure, as well as on cerebral 3D digital rotational angiography (3DRA) images before and after embolization, paired with digital subtraction angiography (DSA) images. Results on AVM delineation show high correspondence to the ground truth structures and indicate potentials for use in surgical planning