Simulation of intra-saccular devices for pre-operative device size selection: Method and validation for sizing and porosity simulation

Muñoz, Romina; Narata, Ana Paula; Obradó, Laura; Fernández, Héctor; Larrabide, Ignacio

doi:10.1016/j.compbiomed.2022.105744

Cited by 4 publications

(1 citation statement)

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“…Moreover, assessment of the segmentation quality varies according to the task at hand. For example, in applications such as simulating the deployment of intrasaccular devices for treating aneurysms, 10,11 it is only required to have an accurate segmentation within the aneurysm region. Standard intensity-based segmentation methods-such as thresholding or region growing-achieve good results in this setting because relevant foreground voxels share uniformity.…”

Section: Introductionmentioning

confidence: 99%

A deep learning model for brain vessel segmentation in 3DRA with arteriovenous malformations

García

Fang

Liu

et al. 2023

18th International Symposium on Medical Information Processing and Analysis

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Segmentation of brain arterio-venous malformations (bAVMs) in 3D rotational angiographies (3DRA) is still an open problem in the literature, with high relevance for clinical practice. While deep learning models have been applied for segmenting the brain vasculature in these images, they have never been used in cases with bAVMs. This is likely caused by the difficulty to obtain sufficiently annotated data to train these approaches. In this paper we introduce a first deep learning model for blood vessel segmentation in 3DRA images of patients with bAVMs. To this end, we densely annotated 5 3DRA volumes of bAVM cases and used these to train two alternative 3DUNet-based architectures with different segmentation objectives. Our results show that the networks reach a comprehensive coverage of relevant structures for bAVM analysis, much better than what is obtained using standard methods. This is promising for achieving a better topological and morphological characterisation of the bAVM structures of interest. Furthermore, the models have the ability to segment venous structures even when missing in the ground truth labelling, which is relevant for planning interventional treatments. Ultimately, these results could be used as more reliable first initial guesses, alleviating the cumbersome task of creating manual labels.

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Section: Introductionmentioning

confidence: 99%