VascX Models: Model Ensembles for Retinal Vascular Analysis from Color Fundus Images
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915785341927424 |
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| author | Quiros, Jose Vargas Liefers, Bart van Garderen, Karin Vermeulen, Jeroen Center, Eyened Reading Consortium, Sinergia Klaver, Caroline |
| author_facet | Quiros, Jose Vargas Liefers, Bart van Garderen, Karin Vermeulen, Jeroen Center, Eyened Reading Consortium, Sinergia Klaver, Caroline |
| contents | We introduce VascX models, a comprehensive set of model ensembles for analyzing retinal vasculature from color fundus images (CFIs). Annotated CFIs were aggregated from public datasets . Additional CFIs, mainly from the population-based Rotterdam Study were annotated by graders for arteries and veins at pixel level, resulting in a dataset diverse in patient demographics and imaging conditions. VascX models demonstrated superior segmentation performance across datasets, image quality levels, and anatomic regions when compared to existing, publicly available models, likely due to the increased size and variety of our training set. Important improvements were observed in artery-vein and disc segmentation performance, particularly in segmentations of these structures on CFIs of intermediate quality, common in large cohorts and clinical datasets. Importantly, these improvements translated into significantly more accurate vascular features when we compared features extracted from VascX segmentation masks with features extracted from segmentation masks generated by previous models. With VascX models we provide a robust, ready-to-use set of model ensembles and inference code aimed at simplifying the implementation and enhancing the quality of automated retinal vasculature analyses. The precise vessel parameters generated by the model can serve as starting points for the identification of disease patterns in and outside of the eye. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_16016 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | VascX Models: Model Ensembles for Retinal Vascular Analysis from Color Fundus Images Quiros, Jose Vargas Liefers, Bart van Garderen, Karin Vermeulen, Jeroen Center, Eyened Reading Consortium, Sinergia Klaver, Caroline Image and Video Processing Computer Vision and Pattern Recognition Tissues and Organs We introduce VascX models, a comprehensive set of model ensembles for analyzing retinal vasculature from color fundus images (CFIs). Annotated CFIs were aggregated from public datasets . Additional CFIs, mainly from the population-based Rotterdam Study were annotated by graders for arteries and veins at pixel level, resulting in a dataset diverse in patient demographics and imaging conditions. VascX models demonstrated superior segmentation performance across datasets, image quality levels, and anatomic regions when compared to existing, publicly available models, likely due to the increased size and variety of our training set. Important improvements were observed in artery-vein and disc segmentation performance, particularly in segmentations of these structures on CFIs of intermediate quality, common in large cohorts and clinical datasets. Importantly, these improvements translated into significantly more accurate vascular features when we compared features extracted from VascX segmentation masks with features extracted from segmentation masks generated by previous models. With VascX models we provide a robust, ready-to-use set of model ensembles and inference code aimed at simplifying the implementation and enhancing the quality of automated retinal vasculature analyses. The precise vessel parameters generated by the model can serve as starting points for the identification of disease patterns in and outside of the eye. |
| title | VascX Models: Model Ensembles for Retinal Vascular Analysis from Color Fundus Images |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Tissues and Organs |
| url | https://arxiv.org/abs/2409.16016 |