Open-Source Software Suite for Cell Detection and Segmentation in Adaptive Optics Retinal Imaging.

Vineeta Das, Tao Liu, Andrei Volkov, Jianfei Liu, Joanne Li, Nancy Aguilera, Johnny Tam

Abstract read

In one paragraph

Article in Translational vision science & technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Vineeta DasNational Eye Institute, National Institutes of Health, Bethesda, MD, USA.

Tao LiuNational Eye Institute, National Institutes of Health, Bethesda, MD, USA.

Andrei VolkovNational Eye Institute, National Institutes of Health, Bethesda, MD, USA.

Jianfei LiuNational Eye Institute, National Institutes of Health, Bethesda, MD, USA.

Joanne LiNational Eye Institute, National Institutes of Health, Bethesda, MD, USA.

Nancy AguileraNational Eye Institute, National Institutes of Health, Bethesda, MD, USA.

Johnny TamNational Eye Institute, National Institutes of Health, Bethesda, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The purpose of this study was to present a suite of open-source software tools for automated and interactive annotation (detection and segmentation) of retinal cells from adaptive optics (AO) images. Methods: Three related software packages were developed, implementing machine learning (ML) approaches for cone photoreceptor detection and segmentation and retinal pigment epithelial (RPE) cell detection in AO images. The software packages were designed to have interactive features allowing multiple users to adjust annotations for systematic grading. Results: The software packages have been extensively tested for usability to enable reliable annotation of cells in AO images. Conclusions: This open-source software suite provides tools for quantitative analysis of retinal cells across different AO modalities, supporting reproducible and standardized measurements across studies and imaging platforms. Translational Relevance: By lowering technical barriers to AO image analysis, these tools enable broader adoption of cellular resolution biomarkers in retinal research and clinical studies, including longitudinal monitoring and therapeutic evaluation.

Indexed as

Image Processing, Computer-AssistedRetinaRetinal Cone Photoreceptor CellsRetinal Pigment EpitheliumSoftwareAlgorithmsAnimalsHumansMachine Learning

Identifiers

PMID42635481

DOI10.1167/tvst.15.8.22

PMCPMC13543598

What Socratic holds

Textmetadata

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.