Incorporation of gradient vector flow field in a multimodal graph-theoretic approach for segmenting the internal limiting membrane from glaucomatous optic nerve head-centered SD-OCT volumes
Published in
Computerized Medical Imaging & Graphics, July 2016
The internal limiting membrane (ILM) separates the retina and optic nerve head (ONH) from the vitreous. In the optical coherence tomography volumes of glaucoma patients, while current approaches for the segmentation of the ILM in the peripapillary and macular regions are considered robust, current approaches commonly produce ILM segmentation errors at the ONH due to the presence of blood vessels and/or characteristic glaucomatous deep cupping. Because a precise segmentation of the ILM surface at the ONH is required for computing several newer structural measurements including Bruch's membrane opening-minimum rim width (BMO-MRW) and cup volume, in this study, we propose a multimodal multiresolution graph-based method to precisely segment the ILM surface within ONH-centered spectral-domain optical coherence tomography (SD-OCT) volumes. In particular, the gradient vector flow (GVF) field, which is computed from a multiresolution initial segmentation, is employed for calculating a set of non-overlapping GVF-based columns perpendicular to the initial segmentation. The GVF columns are utilized to resample the volume and also serve as the columns to the graph construction. The ILM surface in the resampled volume is fairly smooth and does not contain the steep slopes. This prior shape knowledge along with the blood vessel information, obtained from registered fundus photographs, are incorporated in a graph-theoretic approach in order to identify the location of the ILM surface. The proposed method is tested on the SD-OCT volumes of 44 subjects with various stages of glaucoma and significantly smaller segmentation errors were obtained than that of current approaches.
This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 February 2021.
All research outputs
#11,927,005
of 34,447,360 outputs
Outputs from Computerized Medical Imaging & Graphics
#371
of 950 outputs
Outputs of similar age
#126,376
of 335,610 outputs
Outputs of similar age from Computerized Medical Imaging & Graphics
#4
of 9 outputs
Altmetric has tracked 34,447,360 research outputs across all sources so far. This one has received more attention than most of these and is in the 64th percentile.
So far Altmetric has tracked 950 research outputs from this source. They receive a mean Attention Score of 4.4. This one has gotten more attention than average, scoring higher than 59% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 335,610 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.
