Fine-grained Classification of Port Wine Stains Using Optical Coherence Tomography Angiography
<|reference_start|>Fine-grained Classification of Port Wine Stains Using Optical Coherence Tomography Angiography: Accurate classification of port wine stains (PWS, vascular malformations present at birth), is critical for subsequent treatment planning. However, the current method of classifying PWS based on the external skin appearance rarely reflects the underlying angiopathological heterogeneity of PWS lesions, resulting in inconsistent outcomes with the common vascular-targeted photodynamic therapy (V-PDT) treatments. Conversely, optical coherence tomography angiography (OCTA) is an ideal tool for visualizing the vascular malformations of PWS. Previous studies have shown no significant correlation between OCTA quantitative metrics and the PWS subtypes determined by the current classification approach. This study proposes a new classification approach for PWS using both OCT and OCTA. By examining the hypodermic histopathology and vascular structure of PWS, we have devised a fine-grained classification method that subdivides PWS into five distinct types. To assess the angiopathological differences of various PWS subtypes, we have analyzed six metrics related to vascular morphology and depth information of PWS lesions. The five PWS types present significant differences across all metrics compared to the conventional subtypes. Our findings suggest that an angiopathology-based classification accurately reflects the heterogeneity in PWS lesions. This research marks the first attempt to classify PWS based on angiopathology, potentially guiding more effective subtyping and treatment strategies for PWS.<|reference_end|>
@article{deng2024fine-grained,
    title={Fine-grained Classification of Port Wine Stains Using Optical Coherence
  Tomography Angiography},
    author={Xiaofeng Deng, Defu Chen, Bowen Liu, Xiwan Zhang, Haixia Qiu, Wu Yuan,
  Hongliang Ren},
    journal={arXiv preprint arXiv:2408.16277},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16277},
    primaryClass={eess.IV cs.CV}
}