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Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region
Optical coherence tomography (OCT) is a high speed, high resolution and non-invasive imaging modality that enables the capturing of the 3D structure of the retina. The fast and automatic analysis of 3D volume OCT data is crucial taking into account the increased amount of patient-specific 3D imaging...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4530974/ https://www.ncbi.nlm.nih.gov/pubmed/26258430 http://dx.doi.org/10.1371/journal.pone.0133908 |
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author | Tian, Jing Varga, Boglárka Somfai, Gábor Márk Lee, Wen-Hsiang Smiddy, William E. Cabrera DeBuc, Delia |
author_facet | Tian, Jing Varga, Boglárka Somfai, Gábor Márk Lee, Wen-Hsiang Smiddy, William E. Cabrera DeBuc, Delia |
author_sort | Tian, Jing |
collection | PubMed |
description | Optical coherence tomography (OCT) is a high speed, high resolution and non-invasive imaging modality that enables the capturing of the 3D structure of the retina. The fast and automatic analysis of 3D volume OCT data is crucial taking into account the increased amount of patient-specific 3D imaging data. In this work, we have developed an automatic algorithm, OCTRIMA 3D (OCT Retinal IMage Analysis 3D), that could segment OCT volume data in the macular region fast and accurately. The proposed method is implemented using the shortest-path based graph search, which detects the retinal boundaries by searching the shortest-path between two end nodes using Dijkstra’s algorithm. Additional techniques, such as inter-frame flattening, inter-frame search region refinement, masking and biasing were introduced to exploit the spatial dependency between adjacent frames for the reduction of the processing time. Our segmentation algorithm was evaluated by comparing with the manual labelings and three state of the art graph-based segmentation methods. The processing time for the whole OCT volume of 496×644×51 voxels (captured by Spectralis SD-OCT) was 26.15 seconds which is at least a 2-8-fold increase in speed compared to other, similar reference algorithms used in the comparisons. The average unsigned error was about 1 pixel (∼ 4 microns), which was also lower compared to the reference algorithms. We believe that OCTRIMA 3D is a leap forward towards achieving reliable, real-time analysis of 3D OCT retinal data. |
format | Online Article Text |
id | pubmed-4530974 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-45309742015-08-24 Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region Tian, Jing Varga, Boglárka Somfai, Gábor Márk Lee, Wen-Hsiang Smiddy, William E. Cabrera DeBuc, Delia PLoS One Research Article Optical coherence tomography (OCT) is a high speed, high resolution and non-invasive imaging modality that enables the capturing of the 3D structure of the retina. The fast and automatic analysis of 3D volume OCT data is crucial taking into account the increased amount of patient-specific 3D imaging data. In this work, we have developed an automatic algorithm, OCTRIMA 3D (OCT Retinal IMage Analysis 3D), that could segment OCT volume data in the macular region fast and accurately. The proposed method is implemented using the shortest-path based graph search, which detects the retinal boundaries by searching the shortest-path between two end nodes using Dijkstra’s algorithm. Additional techniques, such as inter-frame flattening, inter-frame search region refinement, masking and biasing were introduced to exploit the spatial dependency between adjacent frames for the reduction of the processing time. Our segmentation algorithm was evaluated by comparing with the manual labelings and three state of the art graph-based segmentation methods. The processing time for the whole OCT volume of 496×644×51 voxels (captured by Spectralis SD-OCT) was 26.15 seconds which is at least a 2-8-fold increase in speed compared to other, similar reference algorithms used in the comparisons. The average unsigned error was about 1 pixel (∼ 4 microns), which was also lower compared to the reference algorithms. We believe that OCTRIMA 3D is a leap forward towards achieving reliable, real-time analysis of 3D OCT retinal data. Public Library of Science 2015-08-10 /pmc/articles/PMC4530974/ /pubmed/26258430 http://dx.doi.org/10.1371/journal.pone.0133908 Text en © 2015 Tian et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Tian, Jing Varga, Boglárka Somfai, Gábor Márk Lee, Wen-Hsiang Smiddy, William E. Cabrera DeBuc, Delia Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region |
title | Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region |
title_full | Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region |
title_fullStr | Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region |
title_full_unstemmed | Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region |
title_short | Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region |
title_sort | real-time automatic segmentation of optical coherence tomography volume data of the macular region |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4530974/ https://www.ncbi.nlm.nih.gov/pubmed/26258430 http://dx.doi.org/10.1371/journal.pone.0133908 |
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