PURPOSE: To evaluate a new computerized segmentation technique for the quantification of intraretinal and subretinal fluid in spectral-domain optical coherence tomography (SD OCT) images of the retina. DESIGN: Prospective, cross-sectional study. METHODS: Thirty-seven B-scan images of 37 patients with exudative age-related macular degeneration were chosen randomly from SD OCT volume scans (1 per volume scan). All hyporeflective areas in the image first were segmented automatically as candidate regions by the program. Researchers who were masked to the candidate region information selected each fluid region from the original image using a single mouse click. The program then delineated the boundary of each region selected and calculated quantitative parameters, including total area of fluid regions if multiple regions were selected. The performance of our technique was validated by comparing the results with the measurements obtained from boundaries manually delineated by 2 masked observers. Time efficiency, agreement with manual delineation, and intraobserver and interobserver agreement of using the program were evaluated. RESULTS: The proposed technique reduced the average processing time per image approximately 6-fold (15 seconds for computerized segmentation vs 90 seconds for manual delineation). There was good agreement between computerized segmentation and manual delineation measured by intraclass correlation coefficient (range, 0.897 to 0.979) and the Dice coefficient (range, 0.721 to 0.785). The proposed technique has excellent intraobserver and interobserver agreement (intraclass correlation coefficient range, 0.998 to 0.999; Dice coefficient range. 0.959 to 0.981). CONCLUSIONS: This computerized segmentation method allows for accurate and fast quantification of fluid in retinal SD OCT images and could assist in monitoring disease progression and evaluating therapeutic intervention.
PURPOSE: To evaluate a new computerized segmentation technique for the quantification of intraretinal and subretinal fluid in spectral-domain optical coherence tomography (SD OCT) images of the retina. DESIGN: Prospective, cross-sectional study. METHODS: Thirty-seven B-scan images of 37 patients with exudative age-related macular degeneration were chosen randomly from SD OCT volume scans (1 per volume scan). All hyporeflective areas in the image first were segmented automatically as candidate regions by the program. Researchers who were masked to the candidate region information selected each fluid region from the original image using a single mouse click. The program then delineated the boundary of each region selected and calculated quantitative parameters, including total area of fluid regions if multiple regions were selected. The performance of our technique was validated by comparing the results with the measurements obtained from boundaries manually delineated by 2 masked observers. Time efficiency, agreement with manual delineation, and intraobserver and interobserver agreement of using the program were evaluated. RESULTS: The proposed technique reduced the average processing time per image approximately 6-fold (15 seconds for computerized segmentation vs 90 seconds for manual delineation). There was good agreement between computerized segmentation and manual delineation measured by intraclass correlation coefficient (range, 0.897 to 0.979) and the Dice coefficient (range, 0.721 to 0.785). The proposed technique has excellent intraobserver and interobserver agreement (intraclass correlation coefficient range, 0.998 to 0.999; Dice coefficient range. 0.959 to 0.981). CONCLUSIONS: This computerized segmentation method allows for accurate and fast quantification of fluid in retinal SD OCT images and could assist in monitoring disease progression and evaluating therapeutic intervention.
Authors: Jie Wang; Miao Zhang; Alex D Pechauer; Liang Liu; Thomas S Hwang; David J Wilson; Dengwang Li; Yali Jia Journal: Biomed Opt Express Date: 2016-03-30 Impact factor: 3.732
Authors: Andrew Lang; Aaron Carass; Emily K Swingle; Omar Al-Louzi; Pavan Bhargava; Shiv Saidha; Howard S Ying; Peter A Calabresi; Jerry L Prince Journal: Biomed Opt Express Date: 2014-12-15 Impact factor: 3.732
Authors: A Breger; M Ehler; H Bogunovic; S M Waldstein; A-M Philip; U Schmidt-Erfurth; B S Gerendas Journal: Eye (Lond) Date: 2017-04-21 Impact factor: 3.775