684 research outputs found
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 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 496x644x51 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 ( approximately 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
3D Automatic Segmentation Method for Retinal Optical Coherence Tomography Volume Data Using Boundary Surface Enhancement
With the introduction of spectral-domain optical coherence tomography
(SDOCT), much larger image datasets are routinely acquired compared to what was
possible using the previous generation of time-domain OCT. Thus, there is a
critical need for the development of 3D segmentation methods for processing
these data. We present here a novel 3D automatic segmentation method for
retinal OCT volume data. Briefly, to segment a boundary surface, two OCT volume
datasets are obtained by using a 3D smoothing filter and a 3D differential
filter. Their linear combination is then calculated to generate new volume data
with an enhanced boundary surface, where pixel intensity, boundary position
information, and intensity changes on both sides of the boundary surface are
used simultaneously. Next, preliminary discrete boundary points are detected
from the A-Scans of the volume data. Finally, surface smoothness constraints
and a dynamic threshold are applied to obtain a smoothed boundary surface by
correcting a small number of error points. Our method can extract retinal layer
boundary surfaces sequentially with a decreasing search region of volume data.
We performed automatic segmentation on eight human OCT volume datasets acquired
from a commercial Spectralis OCT system, where each volume of data consisted of
97 OCT images with a resolution of 496 512; experimental results show that this
method can accurately segment seven layer boundary surfaces in normal as well
as some abnormal eyes.Comment: 27 pages, 19 figure
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