A2V: A Semi-Supervised Domain Adaptation Framework for Brain Vessel Segmentation via Two-Phase Training Angiography-to-Venography Translation
Francesco Galati (EURECOM),* Daniele Falcetta (EURECOM), Rosa Cortese (University of Siena), Barbara Casolla (CHU Nice), Ferran Prados (University College London), Ninon Burgos (CNRS - Paris Brain Institute), Maria A. Zuluaga (EURECOM)
The 34th British Machine Vision Conference
Abstract
We present a semi-supervised domain adaptation framework for brain vessel segmentation from different image modalities. Existing state-of-the-art methods focus on a single modality, despite the wide range of available cerebrovascular imaging techniques. This can lead to significant distribution shifts that negatively impact the generalization across modalities. By relying on annotated angiographies and a limited number of annotated venographies, our framework accomplishes image-to-image translation and semantic segmentation, leveraging a disentangled and semantically rich latent space to represent heterogeneous data and perform image-level adaptation from source to target domains. Moreover, we reduce the typical complexity of cycle-based architectures and minimize the use of adversarial training, which allows us to build an efficient and intuitive model with stable training. We evaluate our method on magnetic resonance angiographies and venographies. While achieving state-of-the-art performance in the source domain, our method attains a Dice score coefficient in the target domain that is only 8.9% lower, highlighting its promising potential for robust cerebrovascular image segmentation across different modalities.Video
Citation
@inproceedings{Galati_2023_BMVC,
author = {Francesco Galati and Daniele Falcetta and Rosa Cortese and Barbara Casolla and Ferran Prados and Ninon Burgos and Maria A. Zuluaga},
title = {A2V: A Semi-Supervised Domain Adaptation Framework for Brain Vessel Segmentation via Two-Phase Training Angiography-to-Venography Translation},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year = {2023},
url = {https://papers.bmvc2023.org/0750.pdf}
}
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