Simulation-Based Segmentation of Blood Vessels in Cerebral 3D OCTA Images

2024
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Authors: Bastian Wittmann; Lukas Glandorf; Johannes C. Paetzold; Tamaz Amiranashvili; Thomas Wälchli; Daniel Razansky; Bjoern Menze
Published in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2024
Publisher: Springer Nature Switzerland

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Abstract

The chapter introduces a groundbreaking method for segmenting blood vessels in cerebral 3D OCTA images using synthetic data. By transforming vessel graphs derived from real murine vasculature into voxelized volumes and simulating dominant 3D OCTA image acquisition artifacts, the authors create realistic synthetic images. These synthetic images are then used to train a deep learning-based segmentation network, eliminating the need for time-consuming manual annotations. The proposed method not only addresses the lack of large-scale annotated datasets but also adapts to the high variability in OCT system designs. The chapter includes detailed descriptions of the volume generation, artifact simulation, and segmentation processes, as well as comprehensive experimental results that demonstrate the effectiveness of the proposed approach. By open-sourcing the code, synthetic dataset, and manually annotated OCTA images, the authors aim to advance the field of neurovascular research and facilitate large-scale quantitative analysis of disease repercussions on the vascular network.

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The online version contains supplementary material available at https://doi.org/10.1007/978-3-031-72111-3_61.

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Title: Simulation-Based Segmentation of Blood Vessels in Cerebral 3D OCTA Images
Authors: Bastian Wittmann
Lukas Glandorf
Johannes C. Paetzold
Tamaz Amiranashvili
Thomas Wälchli
Daniel Razansky
Bjoern Menze
Publisher: Springer Nature Switzerland
Book: Medical Image Computing and Computer Assisted Intervention – MICCAI 2024
Print ISBN: 978-3-031-72110-6

Electronic ISBN: 978-3-031-72111-3

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-3-031-72111-3_61

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