The OCT-S and OCT-A images (500 × 1536 × 500 pixels, width × depth × height), corresponding to 6 × 3 × 6 mm in physical volume, were exported from the OCT machine and rescaled to isometric voxel images. After normalization of the image, the choroidal vessels in the inverted OCT-S image were segmented following the method previously reported for 3D choroidal vessel segmentation in SD-OCT.
15 Retinal vessel silhouette in the choroid was removed before segmentation. At first, the line representing the retinal pigment epithelium (RPE) surface was detected using the grow-cut module of 3D slicer (
Fig. 2B).
19 The RPE complex (from the surface of the RPE to 5 pixels [60 μm] beneath the RPE) was segmented by translation of the RPE surface line (
Fig. 2C). In practice, we first detected the line corresponding to the RPE surface. We made a copy of the RPE surface line and placed that at 5 pixels under the Bruch's membrane and then removed the area between the two lines as the RPE complex. A maximum intensity projection image of the RPE complex along the
z-axis, which contained the silhouette image of the retinal vessels, was made (
Fig. 2D). The retinal vessel shadows in OCT-S volume images were adjusted after masking outside the silhouette image. The remaining volume of the choroidal tissue, after removal of the RPE complex, was defined as the outer choroidal volume. The preprocessed OCT-S and OCT-A images were enhanced by a multiscale Hessian matrix analysis.
15,20–22 The OCT-A images were not preprocessed to remove the retinal vessel images because retinal vessel shadows appeared as negative images in the choroid in OCT-A. The images were rendered in 3D using 3D slicer. The choroid-scleral border was delineated manually: briefly, about 100 markup points were placed at the choroid-scleral junction on the image of the outer choroidal volume, and a spline surface model was made. The outer choroidal volume was segmented with the volume-clipping module in 3D slicer. The overlay of the segmented vessels in OCT-A (red lines) and OCT-S (blue lines) was placed on the structure image from OCT-S. Yellow segments indicate the intersection of the vessels in OCT-A and OCT-S (
Fig. 3).
The shape and volume of the blood vessel after segmentation depend on the threshold in 8-bit grayscale value when thresholding. In the current study, we applied the fixed 35 for thresholding after normalization of the images for convenience for segmentation of the scalar volume of the vessel image. The outer choroidal volume, the vessel volume in OCT-S, the vessel volume in OCT-A, and the volume of intersection between the two types of vessel images were calculated with the segment statistics module in 3D slicer. The two images taken repeatedly were registered at the center of the macula by inspection using the whole volume of the OCT-S. We made the registration model using translation and B-spline methods in the transform menu of 3D slicer. A Dice similarity coefficient (DSC) was measured with the segment comparison tool in 3D slicer. Similarity, the 3D models were evaluated with a DSC between the repeated images per subject. DSC of two volumes, A and B, was calculated as
\(\def\upalpha{\unicode[Times]{x3B1}}\)\(\def\upbeta{\unicode[Times]{x3B2}}\)\(\def\upgamma{\unicode[Times]{x3B3}}\)\(\def\updelta{\unicode[Times]{x3B4}}\)\(\def\upvarepsilon{\unicode[Times]{x3B5}}\)\(\def\upzeta{\unicode[Times]{x3B6}}\)\(\def\upeta{\unicode[Times]{x3B7}}\)\(\def\uptheta{\unicode[Times]{x3B8}}\)\(\def\upiota{\unicode[Times]{x3B9}}\)\(\def\upkappa{\unicode[Times]{x3BA}}\)\(\def\uplambda{\unicode[Times]{x3BB}}\)\(\def\upmu{\unicode[Times]{x3BC}}\)\(\def\upnu{\unicode[Times]{x3BD}}\)\(\def\upxi{\unicode[Times]{x3BE}}\)\(\def\upomicron{\unicode[Times]{x3BF}}\)\(\def\uppi{\unicode[Times]{x3C0}}\)\(\def\uprho{\unicode[Times]{x3C1}}\)\(\def\upsigma{\unicode[Times]{x3C3}}\)\(\def\uptau{\unicode[Times]{x3C4}}\)\(\def\upupsilon{\unicode[Times]{x3C5}}\)\(\def\upphi{\unicode[Times]{x3C6}}\)\(\def\upchi{\unicode[Times]{x3C7}}\)\(\def\uppsy{\unicode[Times]{x3C8}}\)\(\def\upomega{\unicode[Times]{x3C9}}\)\(\def\bialpha{\boldsymbol{\alpha}}\)\(\def\bibeta{\boldsymbol{\beta}}\)\(\def\bigamma{\boldsymbol{\gamma}}\)\(\def\bidelta{\boldsymbol{\delta}}\)\(\def\bivarepsilon{\boldsymbol{\varepsilon}}\)\(\def\bizeta{\boldsymbol{\zeta}}\)\(\def\bieta{\boldsymbol{\eta}}\)\(\def\bitheta{\boldsymbol{\theta}}\)\(\def\biiota{\boldsymbol{\iota}}\)\(\def\bikappa{\boldsymbol{\kappa}}\)\(\def\bilambda{\boldsymbol{\lambda}}\)\(\def\bimu{\boldsymbol{\mu}}\)\(\def\binu{\boldsymbol{\nu}}\)\(\def\bixi{\boldsymbol{\xi}}\)\(\def\biomicron{\boldsymbol{\micron}}\)\(\def\bipi{\boldsymbol{\pi}}\)\(\def\birho{\boldsymbol{\rho}}\)\(\def\bisigma{\boldsymbol{\sigma}}\)\(\def\bitau{\boldsymbol{\tau}}\)\(\def\biupsilon{\boldsymbol{\upsilon}}\)\(\def\biphi{\boldsymbol{\phi}}\)\(\def\bichi{\boldsymbol{\chi}}\)\(\def\bipsy{\boldsymbol{\psy}}\)\(\def\biomega{\boldsymbol{\omega}}\)\(\def\bupalpha{\bf{\alpha}}\)\(\def\bupbeta{\bf{\beta}}\)\(\def\bupgamma{\bf{\gamma}}\)\(\def\bupdelta{\bf{\delta}}\)\(\def\bupvarepsilon{\bf{\varepsilon}}\)\(\def\bupzeta{\bf{\zeta}}\)\(\def\bupeta{\bf{\eta}}\)\(\def\buptheta{\bf{\theta}}\)\(\def\bupiota{\bf{\iota}}\)\(\def\bupkappa{\bf{\kappa}}\)\(\def\buplambda{\bf{\lambda}}\)\(\def\bupmu{\bf{\mu}}\)\(\def\bupnu{\bf{\nu}}\)\(\def\bupxi{\bf{\xi}}\)\(\def\bupomicron{\bf{\micron}}\)\(\def\buppi{\bf{\pi}}\)\(\def\buprho{\bf{\rho}}\)\(\def\bupsigma{\bf{\sigma}}\)\(\def\buptau{\bf{\tau}}\)\(\def\bupupsilon{\bf{\upsilon}}\)\(\def\bupphi{\bf{\phi}}\)\(\def\bupchi{\bf{\chi}}\)\(\def\buppsy{\bf{\psy}}\)\(\def\bupomega{\bf{\omega}}\)\(\def\bGamma{\bf{\Gamma}}\)\(\def\bDelta{\bf{\Delta}}\)\(\def\bTheta{\bf{\Theta}}\)\(\def\bLambda{\bf{\Lambda}}\)\(\def\bXi{\bf{\Xi}}\)\(\def\bPi{\bf{\Pi}}\)\(\def\bSigma{\bf{\Sigma}}\)\(\def\bPhi{\bf{\Phi}}\)\(\def\bPsi{\bf{\Psi}}\)\(\def\bOmega{\bf{\Omega}}\)\begin{equation}{\rm{DSC}} = {{{\rm{Volume\ of\ intersection\ between\ A\ and\ B}}} \over {2 \times \left( {{\rm{Volume\ A}} + {\rm{Volume\ B}}} \right)}}{\rm {.}}\end{equation}
To compare the two images in each subject, the images were registered by linear translation and B-spline translation. Finally, a 450 × 450-pixel image, centered at the fovea in an en face image, was cropped for the measurement.
The 3D image reconstructed from OCT-S and OCT-A was compared to fluorescein angiography (FA) and ICGA in the patient with nAMD. Dye angiography was performed using the HRA2.