Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of OCT angiographs
| Autor: | Menten, Martin J, Paetzold, Johannes C, Dima, Alina, Menze, Bjoern H, Knierim, B, Rueckert, Daniel |
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| Přispěvatelé: | University of Zurich |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Vision and Pattern Recognition (cs.CV) Image and Video Processing (eess.IV) FOS: Electrical engineering electronic engineering information engineering Computer Science - Computer Vision and Pattern Recognition 610 Medicine & health Electrical Engineering and Systems Science - Image and Video Processing 11493 Department of Quantitative Biomedicine |
| Popis: | 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. Accepted at MICCAI 2022 |
| Databáze: | OpenAIRE |
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