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  4. Evaluation of TransUNet for the Segmentation of Retinal Structures in OCT-A
 
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2025
Conference Paper
Title

Evaluation of TransUNet for the Segmentation of Retinal Structures in OCT-A

Abstract
Optical coherence tomography angiography (OCT-A) is a novel, noninvasive technology for visualizing retinal structures. Segmentation of these structures can indicate ophthalmic diseases such as diabetic retinopathy or glaucoma to aid diagnosis. However, limited data availability and artifacts make this task challenging. Adapted from other domains, transformer-based models yield promising results in medical image segmentation, challenging U-Net as the de facto standard method. This work evaluates TransUNet, a hybrid transformer and convolutionbased architecture against U-Net in the task of segmenting retinal structures in OCT-A in a multifaceted comparison. Robustness under a reduced training volume and the effect of varying degrees of data augmentation, including noise simulations specific to OCT-A, are included as further aspects of comparison. Although no significant advantage in overall segmentation performance was found, TransUNet outperformed U-Net in the data-reduced setting and showed a better aptitude to capitalize on augmented data.
Author(s)
Schüßler, Leonie
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Hertlein, Anna-Sophia  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Schuster, Alexander K.
University Medical Center of the Johannes Gutenberg University Mainz
Wesarg, Stefan  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Mainwork
Bildverarbeitung für die Medizin 2025  
Conference
Workshop Bildverarbeitung für die Medizin 2025  
DOI
10.1007/978-3-658-47422-5_77
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • Branche: Healthcare

  • Research Line: Computer vision (CV)

  • Research Line: Machine learning (ML)

  • LTA: Machine intelligence, algorithms, and data structures (incl. semantics)

  • Artificial neural networks

  • Image segmentation

  • Medical image processing

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