A PRISMA flowchart is displayed in Fig. 1. A total of 7212 publications were screened, 37 full-text articles were assessed for eligibility and 32 studies were included in this review.
Fig. 1Summary of search strategy (PRISMA flow chart) for relevant studies
Segmentation input (Table 1)Thirteen out of 33 studies (39%) described high-resolution magnetic resonance imaging (MRI)-based segmentation [20, 27,28,29,30,31,32,33,34,35,36,37,38]. Within this group, tridimensional time of flight-MR-Angiography (TOF-MRA) was used in 11 studies (85%): 9 studies applied a protocol without gadolinium and 2 studies with gadolinium (28, 31). Nine studies (27%) described digital subtraction angiography (DSA)-based segmentations [39,40,41,42,43,44,45,46,47], and 7 (21%) described native CT and CT-Angiography (CTA)-based segmentations [25, 48,49,50,51,52,53] (Table 1).
Segmentation aim (Table 1)Thirteen out the 33 studies (39%) aimed to render feeding arteries, the architecture of the nidus and draining veins [25, 28,29,30, 34, 37, 38, 43, 45, 47, 48, 50, 51]. Seven studies (21%) provided an analysis of feeders and veins, without specific focus on the nidus [18, 27, 31, 33, 49, 52, 54]. The exclusive focus on the nidus was documented in four studies (12%) [20, 21, 36, 54]. Information on the aim of the segmentation is provided in Table 1. Thirty studies (91%) implemented a segmentation strategy to achieve preoperative characterization of bAVMs; however, three studies (9%) segmented with the purpose to visualize on a navigation-linked intraoperative display [38, 55, 56].
Manual and semiautomatic segmentation (Table 2)Table 2 Overview on the segmentation strategies other that fuzzy-based methodsSeven studies (21%) described manual bAVM segmentation [21, 34, 35, 38, 39, 48, 55], and 6 studies (18%) described semiautomatic algorithms [27, 40, 43, 54] In this subgroup, 3 studies (23%) aimed for delineation of all the bAVM components [34, 38, 48], while the other 10 studies (77%) focused on the segmentation on a single component of the bAVM or on the volume of the lesion. Four semiautomatic segmentation studies documented a median duration of 25 (IQR 73) minutes [27, 43, 51, 54].
Automatic segmentation (Table 2)Twenty studies (61%) used an automatic mathematical algorithm to segment bAVMs [20, 25, 28,29,30,31,32,33, 36, 37, 42, 44,45,46,47, 49, 51,52,53, 56]. Eight of these studies (40%) aimed to segment all three bAVM components [25, 28,29,30, 37, 45, 47, 51]. Median segmentation time was 10 min (IQR 33), described in 6 out of the 20 studies. Eight automatic segmentation studies (40%) performed segmentation by an unsupervised fuzzy-based method, with a median processing time of 10 min (IQR 33) [20, 28,29,30,31,32, 36]. Only 1 research group further provided a hemodynamic characterization of the segmented bAVM components. Hemodynamics were provided by integrating temporal blood flow information of the vessels in proximity of the nidus [32, 33].
Other groups included in this cohort performed an automatic image segmentation based on supervised methods [37, 49, 53]. These strategies included supervised principal component analysis [49], supervised 3D V-Net with a compound loss function [
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