294
Q. Chen et al.
of the RNFL is not correct, the algorithm is effective. This is one of the reasons that
RNFL removal is part of the image processing pipeline.
11.2.1.6 Conclusions
We have developed a novel automated drusen segmentation algorithm for SD-OCT
images, which incorporates the 3D spatial information in retinal structures and information in projection images of drusen. Experimental results demonstrated that the
algorithm was able to effectively segment different patterns of drusen. The qualitative
features we extract from drusen may be clinically useful for evaluating the progress
of these lesions. The algorithm does have limitations in that drusen at the edges of
the images and small drusen can be missed. Future refinement and development of
this algorithm will be pursued in an attempt to improve detection and segmentation
of these drusen.
We have described a method for automatic segmentation of drusen on SD-OCT
images, and it addresses the several unsolved challenges emerging from the prior
work: (1) obscuration by drusen of portions of the image needed for accurate estimation of RPE layers, (2) noise in low-SNR OCT images which challenges accurate
segmentation of the RPE, (3) drusen with reflectivity similar with that of the RPE
layer which makes it difficult to segment the RPE layer correctly, and (4) the IS/OS
layers have similar reflectivity as RPE.
Our method, which estimates the RPE layer through interpolation and fitting
procedures, overcomes these challenges to some degree. By finding the middle axes
of the RPE layer, our method is less sensitive to regional areas of obscuration of RPE
by drusen. The method includes a bilateral filtering denoising step which addresses
the challenge of reliably detecting the RPE. Although bilateral filtering might not be
optimal for speckle denoising in SD-OCT, it has a relatively low time complexity
and acceptable performance for the needs of our segmentation algorithm. It is also
known that a pre-processing noise filtering step can increase SNR and potentially the
resulting accuracy of the segmentations, but there is a trade-off in the degrading of the
spatial resolution that could also produce the opposite effect. In the future, we plan
on investigating the effect of adopting more effective denoising methods to improve
the performance of our method. The method can also detect drusen in cases where
drusen and RPE have similar reflectivity; in such cases, the IS/OS layer is similar
reflectivity to RPE and thus difficult to separate from RPE. Our algorithm can still
obtain relatively good segmentation results. The method includes a pre-processing
step to remove the RNFL, so even if the boundary of the RNFL is erroneously
estimated, the drusen segmentation method can be successful.
A novel aspect of the method is inclusion of analysis of the drusen in an en face
projection to eliminate false positive drusen. Not only is this useful to improve the
accuracy of the method, it provides a useful visualization to physicians, similar to
the CFP view with which they are familiar (Fig. 11.5), and it also provides a means
of computing additional imaging biomarkers for drusen evaluation, such as drusen
Q. Chen et al.
of the RNFL is not correct, the algorithm is effective. This is one of the reasons that
RNFL removal is part of the image processing pipeline.
11.2.1.6 Conclusions
We have developed a novel automated drusen segmentation algorithm for SD-OCT
images, which incorporates the 3D spatial information in retinal structures and information in projection images of drusen. Experimental results demonstrated that the
algorithm was able to effectively segment different patterns of drusen. The qualitative
features we extract from drusen may be clinically useful for evaluating the progress
of these lesions. The algorithm does have limitations in that drusen at the edges of
the images and small drusen can be missed. Future refinement and development of
this algorithm will be pursued in an attempt to improve detection and segmentation
of these drusen.
We have described a method for automatic segmentation of drusen on SD-OCT
images, and it addresses the several unsolved challenges emerging from the prior
work: (1) obscuration by drusen of portions of the image needed for accurate estimation of RPE layers, (2) noise in low-SNR OCT images which challenges accurate
segmentation of the RPE, (3) drusen with reflectivity similar with that of the RPE
layer which makes it difficult to segment the RPE layer correctly, and (4) the IS/OS
layers have similar reflectivity as RPE.
Our method, which estimates the RPE layer through interpolation and fitting
procedures, overcomes these challenges to some degree. By finding the middle axes
of the RPE layer, our method is less sensitive to regional areas of obscuration of RPE
by drusen. The method includes a bilateral filtering denoising step which addresses
the challenge of reliably detecting the RPE. Although bilateral filtering might not be
optimal for speckle denoising in SD-OCT, it has a relatively low time complexity
and acceptable performance for the needs of our segmentation algorithm. It is also
known that a pre-processing noise filtering step can increase SNR and potentially the
resulting accuracy of the segmentations, but there is a trade-off in the degrading of the
spatial resolution that could also produce the opposite effect. In the future, we plan
on investigating the effect of adopting more effective denoising methods to improve
the performance of our method. The method can also detect drusen in cases where
drusen and RPE have similar reflectivity; in such cases, the IS/OS layer is similar
reflectivity to RPE and thus difficult to separate from RPE. Our algorithm can still
obtain relatively good segmentation results. The method includes a pre-processing
step to remove the RNFL, so even if the boundary of the RNFL is erroneously
estimated, the drusen segmentation method can be successful.
A novel aspect of the method is inclusion of analysis of the drusen in an en face
projection to eliminate false positive drusen. Not only is this useful to improve the
accuracy of the method, it provides a useful visualization to physicians, similar to
the CFP view with which they are familiar (Fig. 11.5), and it also provides a means
of computing additional imaging biomarkers for drusen evaluation, such as drusen