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Selected Publication:
SHR Neuro Cancer Cardio Lipid Metab Microb
Hadzic, A; Bogensperger, L; Berghold, A; Urschler, M.
Flow Matching-Based Data Synthesis for Robust Anatomical Landmark Localization.
IEEE J Biomed Health Inform. 2025; PP:
Doi: 10.1109/JBHI.2025.3603907
PubMed
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- Leading authors Med Uni Graz
- Hadzic Arnela
- Urschler Martin
- Co-authors Med Uni Graz
- Berghold Andrea
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- Dimensions Citations:
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- Abstract:
- Anatomical landmark localization (ALL) plays a crucial role in medical imaging for applications such as therapy planning and surgical interventions. State-ofthe- art deep learning methods for ALL are often trained on small datasets due to the scarcity of large, annotated medical data. This constraint often leads to overfitting on the training dataset, which in turn reduces the model's ability to generalize to unseen data. To address these challenges, we propose a multi-channel generative approach utilizing Flow Matching to synthesize diverse annotated images for data augmentation in ALL tasks. Each synthetically generated sample consists of a medical image paired with a multi-channel heatmap that encodes its landmark configuration, from which the corresponding landmark annotations can be derived. We assess the quality of synthetic image-heatmap pairs automatically using a Statistical Shape Model to evaluate landmark plausibility and compute the Fréchet Inception Distance score to quantify image quality. Our results show that pairs synthesized via Flow Matching exhibit superior quality and diversity compared with those generated by other state-of-the-art generative models like Generative Adversarial Networks or diffusion models. Furthermore, we investigate the effect of integrating synthetic data into the training process of an ALL network. In our experiments, the ALL network trained with Flow Matching-generated data demonstrates improved robustness, particularly in scenarios with limited training data or occlusions, compared with baselines that utilize solely real images or synthetic data from alternative generative models.