Physiology-Based Simulation of the Retinal Vasculature Enables Annotation-Free Segmentation of OCT Angiographs
Menten MJ, Paetzold JC, Dima A, Menze BH, Knier B, Rueckert D (2022)
Publication Type: Conference contribution
Publication year: 2022
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 13438 LNCS
Pages Range: 330-340
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Singapore, SGP
ISBN: 9783031164514
DOI: 10.1007/978-3-031-16452-1_32
Abstract
Optical coherence tomography angiography (OCTA) can non-invasively image the eye’s circulatory system. In order to reliably characterize the retinal vasculature, there is a need to automatically extract quantitative metrics from these images. The calculation of such biomarkers requires a precise semantic segmentation of the blood vessels. However, deep-learning-based methods for segmentation mostly rely on supervised training with voxel-level annotations, which are costly to obtain. In this work, we present a pipeline to synthesize large amounts of realistic OCTA images with intrinsically matching ground truth labels; thereby obviating the need for manual annotation of training data. Our proposed method is based on two novel components: 1) a physiology-based simulation that models the various retinal vascular plexuses and 2) a suite of physics-based image augmentations that emulate the OCTA image acquisition process including typical artifacts. In extensive benchmarking experiments, we demonstrate the utility of our synthetic data by successfully training retinal vessel segmentation algorithms. Encouraged by our method’s competitive quantitative and superior qualitative performance, we believe that it constitutes a versatile tool to advance the quantitative analysis of OCTA images.
Involved external institutions
Technische Universität München (TUM)
Germany (DE)
University of Zurich / Universität Zürich (UZH)
Switzerland (CH)
Germany (DE)
University of Zurich / Universität Zürich (UZH)
Switzerland (CH)
How to cite
APA:
Menten, M.J., Paetzold, J.C., Dima, A., Menze, B.H., Knier, B., & Rueckert, D. (2022). Physiology-Based Simulation of the Retinal Vasculature Enables Annotation-Free Segmentation of OCT Angiographs. In Linwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 330-340). Singapore, SGP: Springer Science and Business Media Deutschland GmbH.
MLA:
Menten, Martin J., et al. "Physiology-Based Simulation of the Retinal Vasculature Enables Annotation-Free Segmentation of OCT Angiographs." Proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, Singapore, SGP Ed. Linwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li, Springer Science and Business Media Deutschland GmbH, 2022. 330-340.
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