ArticlePhotoacoustics2022
Photoacoustic image synthesis with generative adversarial networks.
Article in Photoacoustics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Towards bridging the synthetic-to-real gap in quantitative photoacoustic tomography via unsupervised domain adaptation.Photoacoustics · 2025Article
- A Hybrid Deep Learning Framework for Accurate Cell Segmentation in Whole Slide Images Using YOLOv11, StarDist, and SAM2.Bioengineering (Basel, Switzerland) · 2025Article
- Photoacoustic imaging plus X: a review.Journal of biomedical optics · 2024Review
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Authors and funding
8 authors.
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Abstract
Photoacoustic tomography (PAT) has the potential to recover morphological and functional tissue properties with high spatial resolution. However, previous attempts to solve the optical inverse problem with supervised machine learning were hampered by the absence of labeled reference data. While this bottleneck has been tackled by simulating training data, the domain gap between real and simulated images remains an unsolved challenge. We propose a novel approach to PAT image synthesis that involves subdividing the challenge of generating plausible simulations into two disjoint problems: (1) Probabilistic generation of realistic tissue morphology, and (2) pixel-wise assignment of corresponding optical and acoustic properties. The former is achieved with Generative Adversarial Networks (GANs) trained on semantically annotated medical imaging data. According to a validation study on a downstream task our approach yields more realistic synthetic images than the traditional model-based approach and could therefore become a fundamental step for deep learning-based quantitative PAT (qPAT).
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