{"id":32257,"date":"2026-01-21T17:01:36","date_gmt":"2026-01-21T17:01:36","guid":{"rendered":"https:\/\/lamarr-institute.org\/publication\/neural-network-assisted-annotation-and-analysis-tool-to-study-in-vivo-foveolar-cone-photoreceptor-topography\/"},"modified":"2026-06-08T13:18:59","modified_gmt":"2026-06-08T13:18:59","slug":"neural-network-assisted-annotation-and-analysis-tool-to-study-in-vivo-foveolar-cone-photoreceptor-topography","status":"publish","type":"publication","link":"https:\/\/lamarr-institute.org\/de\/publication\/neural-network-assisted-annotation-and-analysis-tool-to-study-in-vivo-foveolar-cone-photoreceptor-topography\/","title":{"rendered":"Neural network assisted annotation and analysis tool to study in-vivo foveolar cone photoreceptor topography"},"content":{"rendered":"<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 \\$\\$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.<\/p>\n","protected":false},"excerpt":{"rendered":"<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 [&hellip;]<\/p>\n","protected":false},"author":12,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[30],"class_list":["post-32257","publication","type-publication","status-publish","hentry","publication-type-article"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32257","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/types\/publication"}],"author":[{"embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/users\/12"}],"version-history":[{"count":0,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32257\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=32257"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=32257"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}