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<p>The foveola, the central region of the human retina, plays a crucial role in sharp color vision and is challenging to study due to its unique anatomy and technical limitations in imaging. We present ConeMapper, an open-source MATLAB software that integrates a fully convolutional neural network (FCN) for the automatic detection and analysis of cone photoreceptors in confocal adaptive optics scanning light ophthalmoscopy (AOSLO) images of the foveal center. The FCN was trained on a dataset of 49 healthy retinas and showed improved performance over previously published neural networks, particularly in the central fovea, achieving an <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_8028_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="18" /> </InlineMediaObject> <EquationSource Format="TEX">\(F_1\)</EquationSource> </InlineEquation> score of 0.9769 across the validation set, critically reducing analysis time. In addition to automatic cone detection, ConeMapper provides efficient manual annotation tools, visualizations and topographical analysis, offering users detailed metrics for further analysis. ConeMapper is freely available, with ongoing development aimed at enhancing functionality and adaptability to different retinal imaging modalities.</p>

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Neural network assisted annotation and analysis tool to study in-vivo foveolar cone photoreceptor topography

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月4日

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  • Aleksandr Gutnikov,
  • Patrick Hähn-Schumacher,
  • Julius Ameln,
  • Shekoufeh Gorgi Zadeh,
  • Thomas Schultz,
  • Wolf Harmening

摘要

The foveola, the central region of the human retina, plays a crucial role in sharp color vision and is challenging to study due to its unique anatomy and technical limitations in imaging. We present ConeMapper, an open-source MATLAB software that integrates a fully convolutional neural network (FCN) for the automatic detection and analysis of cone photoreceptors in confocal adaptive optics scanning light ophthalmoscopy (AOSLO) images of the foveal center. The FCN was trained on a dataset of 49 healthy retinas and showed improved performance over previously published neural networks, particularly in the central fovea, achieving an \(F_1\) score of 0.9769 across the validation set, critically reducing analysis time. In addition to automatic cone detection, ConeMapper provides efficient manual annotation tools, visualizations and topographical analysis, offering users detailed metrics for further analysis. ConeMapper is freely available, with ongoing development aimed at enhancing functionality and adaptability to different retinal imaging modalities.