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

• URL: http://arxiv.org/abs/2210.02416v1
• Date: Wed, 5 Oct 2022 17:35:56 GMT
• Title: A deep learning model for brain vessel segmentation in 3DRA with arteriovenous malformations
• Authors: Camila Garc\'ia and Yibin Fang and Jianmin Liu and Ana Paula Narata and Jos\'e Ignacio Orlando and Ignacio Larrabide
• Abstract summary: This paper introduces a first deep learning model for blood vessel segmentation in 3DRA images of patients with bAVMs. 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.
• Score: 0.46180371154032895
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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