{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Valmaggia P"],"funding":["Swiss National Science Foundation"],"pagination":["25"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9526362"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["11(9)"],"pubmed_abstract":["<h4>Purpose</h4>To evaluate the feasibility of automated segmentation of pigmented choroidal lesions (PCLs) in optical coherence tomography (OCT) data and compare the performance of different deep neural networks.<h4>Methods</h4>Swept-source OCT image volumes were annotated pixel-wise for PCLs and background. Three deep neural network architectures were applied to the data: the multi-dimensional gated recurrent units (MD-GRU), the V-Net, and the nnU-Net. The nnU-Net was used to compare the performance of two-dimensional (2D) versus three-dimensional (3D) predictions.<h4>Results</h4>A total of 121 OCT volumes were analyzed (100 normal and 21 PCLs). Automated PCL segmentations were successful with all neural networks. The 3D nnU-Net predictions showed the highest recall with a mean of 0.77 ± 0.22 (MD-GRU, 0.60 ± 0.31; V-Net, 0.61 ± 0.25). The 3D nnU-Net predicted PCLs with a Dice coefficient of 0.78 ± 0.13, outperforming MD-GRU (0.62 ± 0.23) and V-Net (0.59 ± 0.24). The smallest distance to the manual annotation was found using 3D nnU-Net with a mean maximum Hausdorff distance of 315 ± 172 µm (MD-GRU, 1542 ± 1169 µm; V-Net, 2408 ± 1060 µm). The 3D nnU-Net showed a superior performance compared with stacked 2D predictions.<h4>Conclusions</h4>The feasibility of automated deep learning segmentation of PCLs was demonstrated in OCT data. The neural network architecture had a relevant impact on PCL predictions.<h4>Translational relevance</h4>This work serves as proof of concept for segmentations of choroidal pathologies in volumetric OCT data; improvements are conceivable to meet clinical demands for the diagnosis, monitoring, and treatment evaluation of PCLs."],"journal":["Translational vision science & technology"],"pubmed_title":["Feasibility of Automated Segmentation of Pigmented Choroidal Lesions in OCT Data With Deep Learning."],"pmcid":["PMC9526362"],"funding_grant_id":["323530","199395"],"pubmed_authors":["Hormann B","Kaiser P","Cattin PC","Maloca PM","Sandkuhler R","Scholl HPN","Valmaggia P","Friedli P"],"additional_accession":[]},"is_claimable":false,"name":"Feasibility of Automated Segmentation of Pigmented Choroidal Lesions in OCT Data With Deep Learning.","description":"<h4>Purpose</h4>To evaluate the feasibility of automated segmentation of pigmented choroidal lesions (PCLs) in optical coherence tomography (OCT) data and compare the performance of different deep neural networks.<h4>Methods</h4>Swept-source OCT image volumes were annotated pixel-wise for PCLs and background. Three deep neural network architectures were applied to the data: the multi-dimensional gated recurrent units (MD-GRU), the V-Net, and the nnU-Net. The nnU-Net was used to compare the performance of two-dimensional (2D) versus three-dimensional (3D) predictions.<h4>Results</h4>A total of 121 OCT volumes were analyzed (100 normal and 21 PCLs). Automated PCL segmentations were successful with all neural networks. The 3D nnU-Net predictions showed the highest recall with a mean of 0.77 ± 0.22 (MD-GRU, 0.60 ± 0.31; V-Net, 0.61 ± 0.25). The 3D nnU-Net predicted PCLs with a Dice coefficient of 0.78 ± 0.13, outperforming MD-GRU (0.62 ± 0.23) and V-Net (0.59 ± 0.24). The smallest distance to the manual annotation was found using 3D nnU-Net with a mean maximum Hausdorff distance of 315 ± 172 µm (MD-GRU, 1542 ± 1169 µm; V-Net, 2408 ± 1060 µm). The 3D nnU-Net showed a superior performance compared with stacked 2D predictions.<h4>Conclusions</h4>The feasibility of automated deep learning segmentation of PCLs was demonstrated in OCT data. The neural network architecture had a relevant impact on PCL predictions.<h4>Translational relevance</h4>This work serves as proof of concept for segmentations of choroidal pathologies in volumetric OCT data; improvements are conceivable to meet clinical demands for the diagnosis, monitoring, and treatment evaluation of PCLs.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Sep","modification":"2026-06-01T02:10:26.779Z","creation":"2025-02-19T01:28:33.49Z"},"accession":"S-EPMC9526362","cross_references":{"pubmed":["36156729"],"doi":["10.1167/tvst.11.9.25"]}}