Dataset Information

Replication and Refinement of an Algorithm for Automated Drusen Segmentation on Optical Coherence Tomography.

ABSTRACT: Here, we investigate the extent to which re-implementing a previously published algorithm for OCT-based drusen quantification permits replicating the reported accuracy on an independent dataset. We refined that algorithm so that its accuracy is increased. Following a systematic literature search, an algorithm was selected based on its reported excellent results. Several steps were added to improve its accuracy. The replicated and refined algorithms were evaluated on an independent dataset with the same metrics as in the original publication. Accuracy of the refined algorithm (overlap ratio 36-52%) was significantly greater than the replicated one (overlap ratio 25-39%). In particular, separation of the retinal pigment epithelium and the ellipsoid zone could be improved by the refinement. However, accuracy was still lower than reported previously on different data (overlap ratio 67-76%). This is the first replication study of an algorithm for OCT image analysis. Its results indicate that current standards for algorithm validation do not provide a reliable estimate of algorithm performance on images that differ with respect to patient selection and image quality. In order to contribute to an improved reproducibility in this field, we publish both our replication and the refinement, as well as an exemplary dataset.

SUBMITTER: Wintergerst MWM

PROVIDER: S-EPMC7192932 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Replication and Refinement of an Algorithm for Automated Drusen Segmentation on Optical Coherence Tomography.

Wintergerst Maximilian W M MWM Gorgi Zadeh Shekoufeh S Wiens Vitalis V Thiele Sarah S Schmitz-Valckenberg Steffen S Holz Frank G FG Finger Robert P RP Schultz Thomas T

Scientific reports 20200430 1

Here, we investigate the extent to which re-implementing a previously published algorithm for OCT-based drusen quantification permits replicating the reported accuracy on an independent dataset. We refined that algorithm so that its accuracy is increased. Following a systematic literature search, an algorithm was selected based on its reported excellent results. Several steps were added to improve its accuracy. The replicated and refined algorithms were evaluated on an independent dataset with t ...[more]

PMID: 32355285

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Project description:PurposeTo evaluate the performance of the Pegasus-OCT (Visulytix Ltd) multiclass automated fluid segmentation algorithms on independent spectral domain optical coherence tomography data sets.MethodsThe Pegasus automated fluid segmentation algorithms were applied to three data sets with edematous pathology, comprising 750, 600, and 110 b-scans, respectively. Intraretinal fluid (IRF), sub-retinal fluid (SRF), and pigment epithelial detachment (PED) were automatically segmented by Pegasus-OCT for each b-scan where ground truth from data set owners was available. Detection performance was assessed by calculating sensitivities and specificities, while Dice coefficients were used to assess agreement between the segmentation methods.ResultsFor two data sets, IRF detection yielded promising sensitivities (0.98 and 0.94, respectively) and specificities (1.00 and 0.98) but less consistent agreement with the ground truth (dice coefficients 0.81 and 0.59); likewise, SRF detection showed high sensitivity (0.86 and 0.98) and specificity (0.83 and 0.89) but less consistent agreement (0.59 and 0.78). PED detection on the first data set showed moderate agreement (0.66) with high sensitivity (0.97) and specificity (0.98). IRF detection in a third data set yielded less favorable agreement (0.46-0.57) and sensitivity (0.59-0.68), attributed to image quality and ground truth grader discordance.ConclusionsThe Pegasus automated fluid segmentation algorithms were able to detect IRF, SRF, and PED in SD-OCT b-scans acquired across multiple independent data sets. Dice coefficients and sensitivity and specificity values indicate the potential for application to automated detection and monitoring of retinal diseases such as age-related macular degeneration and diabetic macular edema.Translational relevanceThe potential of Pegasus-OCT for automated fluid quantification and differentiation of IRF, SRF, and PED in OCT images has application to both clinical practice and research.

Dataset Information

Replication and Refinement of an Algorithm for Automated Drusen Segmentation on Optical Coherence Tomography.

Publications

Replication and Refinement of an Algorithm for Automated Drusen Segmentation on Optical Coherence Tomography.

Similar Datasets

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