The choroid is a vascular plexus located between the retina and the sclera, providing oxygen and nourishment to the outer layers of the retina. Thickness changes in the choroid are of importance in the pathophysiology of various ocular diseases such as glaucoma, age-related macular degeneration (AMD), and others. Our previous choroidal layer segmentation method of 3D macular optical coherence tomography (OCT) scans using choroidal vessel segmentation tended to segment thinner choroidal layers than ground truths, requiring long running time and much memory. To overcome these drawbacks, we introduce a new, fast, and memory-efficient multiresolution LOGISMOS (layered optimal graph image segmentation for multiple objects and surfaces) method. The key idea of the method is to consequently segment the choroidal layer in the higher resolution sub-OCT image volume constrained by the layer segmented in the lower resolution OCT image volume to reduce the size of columns for graph search. Generally, it outperformed our previous method and showed a similar performance to the inter-observer variability between 2 experts.
Image segmentation is important for quantitative analysis of medical image data. Recently, our research group has introduced a 3-D graph search method which can simultaneously segment optimal interacting surfaces with respect to the cost function in volumetric images. Although it provides excellent segmentation accuracy, it is computationally demanding (both CPU and memory) to simultaneously segment multiple surfaces from large volumetric images. Therefore, we propose a new, fast, and memory-efficient graph search method for intraretinal layer segmentation of 3-D macular optical coherence tomograpy (OCT) scans. The key idea is to reduce the size of a graph by combining the nodes with high costs based on the multiscale approach. The new approach requires significantly less memory and achieves significantly faster processing speeds (p < 0.01) with only small segmentation differences compared to the original graph search method. This paper discusses sub-optimality of this approach and assesses trade-off relationships between decreasing processing speed and increasing segmentation differences from that of the original method as a function of employed scale of the underlying graph construction.
In ophthalmology, various modalities and tests are utilized to obtain vital information on the eye’s structure and function. For example, optical coherence tomography (OCT) is utilized to diagnose, screen, and aid treatment of eye diseases like macular degeneration or glaucoma. Such data are complemented by photographic retinal fundus images and functional tests on the visual field. DICOM isn’t widely used yet, though, and frequently images are encoded in proprietary formats. The eXtensible Neuroimaging Archive Tool (XNAT) is an open-source NIH-funded framework for research PACS and is in use at the University of Iowa for neurological research applications. Its use for ophthalmology was hence desirable but posed new challenges due to data types thus far not considered and the lack of standardized formats. We developed custom tools for data types not natively recognized by XNAT itself using XNAT’s low-level REST API. Vendor-provided tools can be included as necessary to convert proprietary data sets into valid DICOM. Clients can access the data in a standardized format while still retaining the original format if needed by specific analysis tools. With respective project-specific permissions, results like segmentations or quantitative evaluations can be stored as additional resources to previously uploaded datasets. Applications can use our abstract-level Python or C/C++ API to communicate with the XNAT instance. This paper describes concepts and details of the designed upload script templates, which can be customized to the needs of specific projects, and the novel client-side communication API which allows integration into new or existing research applications.
Glaucoma is one of the major causes of blindness worldwide. One important structural parameter for the
diagnosis and management of glaucoma is the cup-to-disc ratio (CDR), which tends to become larger as glaucoma
progresses. While approaches exist for segmenting the optic disc and cup within fundus photographs, and more
recently, within spectral-domain optical coherence tomography (SD-OCT) volumes, no approaches have been
reported for the simultaneous segmentation of these structures within both modalities combined. In this work, a
multimodal pixel-classification approach for the segmentation of the optic disc and cup within fundus photographs
and SD-OCT volumes is presented. In particular, after segmentation of other important structures (such as the
retinal layers and retinal blood vessels) and fundus-to-SD-OCT image registration, features are extracted from
both modalities and a k-nearest-neighbor classification approach is used to classify each pixel as cup, rim, or
background. The approach is evaluated on 70 multimodal image pairs from 35 subjects in a leave-10%-out fashion
(by subject). A significant improvement in classification accuracy is obtained using the multimodal approach
over that obtained from the corresponding unimodal approach (97.8% versus 95.2%; p < 0:05; paired t-test).
The introduction of spectral Optical Coherence Tomography (OCT) scanners has enabled acquisition of high
resolution, 3D cross-sectional volumetric images of the retina. 3D-OCT is used to detect and manage eye diseases
such as glaucoma and age-related macular degeneration. To follow-up patients over time, image registration is
a vital tool to enable more precise, quantitative comparison of disease states. In this work we present a 3D
registrationmethod based on a two-step approach. In the first step we register both scans in the XY domain using
an Iterative Closest Point (ICP) based algorithm. This algorithm is applied to vessel segmentations obtained
from the projection image of each scan. The distance minimized in the ICP algorithm includes measurements
of the vessel orientation and vessel width to allow for a more robust match. In the second step, a graph-based
method is applied to find the optimal translation along the depth axis of the individual A-scans in the volume to
match both scans. The cost image used to construct the graph is based on the mean squared error (MSE) between
matching A-scans in both images at different translations. We have applied this method to the registration of
Optic Nerve Head (ONH) centered 3D-OCT scans of the same patient. First, 10 3D-OCT scans of 5 eyes with
glaucoma imaged in vivo were registered for a qualitative evaluation of the algorithm performance. Then, 17
OCT data set pairs of 17 eyes with known deformation were used for quantitative assessment of the method's
robustness.
Image segmentation is of paramount importance for quantitative analysis of medical image data. Recently, a 3-D
graph search method which can detect globally optimal interacting surfaces with respect to the cost function of
volumetric images has been introduced, and its utility demonstrated in several application areas. Although the
method provides excellent segmentation accuracy, its limitation is a slow processing speed when many surfaces
are simultaneously segmented in large volumetric datasets. Here, we propose a novel method of parallel graph
search, which overcomes the limitation and allows the quick detection of multiple surfaces. To demonstrate
the obtained performance with respect to segmentation accuracy and processing speedup, the new approach
was applied to retinal optical coherence tomography (OCT) image data and compared with the performance
of the former non-parallel method. Our parallel graph search methods for single and double surface detection
are approximately 267 and 181 times faster than the original graph search approach in 5 macular OCT volumes
(200 x 5 x 1024 voxels) acquired from the right eyes of 5 normal subjects. The resulting segmentation differences
were small as demonstrated by the mean unsigned differences between the non-parallel and parallel methods of
0.0 ± 0.0 voxels (0.0 ± 0.0 μm) and 0.27 ± 0.34 voxels (0.53 ± 0.66 μm) for the single- and dual-surface
approaches, respectively.
Changes in intraretinal layer thickness occur in a variety of diseases such as glaucoma, macular edema and
diabetes. To segment the intraretinal layers from macular spectral-domain OCT (SD-OCT) scans, we previously
introduced an automated multiscale 3-D graph search method and validated its performance by computing
unsigned border positioning differences when compared with human expert tracings. However, it is also important
to study the reproducibility of resulting layer thickness measurements, as layer thickness is a commonly used
clinical parameter. In this work, twenty eight (14 x 2) repeated macular OCT volumes were acquired from the
right eyes of 14 normal subjects using two Zeiss-Cirrus SD-OCT scanners. After segmentation of 10 intraretinal
layers and rigid registration of layer thickness maps from the repeated OCT scans, the thickness difference of
each layer was calculated. The overall mean global and regional thickness differences of 10 intraretinal layers
were 0.46 ± 0.25 μm (1.70 ± 0.72 %) and 1.16 ± 0.84 μm (4.03 ± 2.05 %), respectively. No specific local region
showed a consistent thickness difference across the layers.
Segmentation of retinal blood vessels can provide important information for detecting and tracking retinal vascular
diseases including diabetic retinopathy, arterial hypertension, arteriosclerosis and retinopathy of prematurity
(ROP). Many studies on 2-D segmentation of retinal blood vessels from a variety of medical images have been
performed. However, 3-D segmentation of retinal blood vessels from spectral-domain optical coherence tomography
(OCT) volumes, which is capable of providing geometrically accurate vessel models, to the best of our
knowledge, has not been previously studied. The purpose of this study is to develop and evaluate a method that
can automatically detect 3-D retinal blood vessels from spectral-domain OCT scans centered on the optic nerve
head (ONH). The proposed method utilized a fast multiscale 3-D graph search to segment retinal surfaces as well
as a triangular mesh-based 3-D graph search to detect retinal blood vessels. An experiment on 30 ONH-centered
OCT scans (15 right eye scans and 15 left eye scans) from 15 subjects was performed, and the mean unsigned
error in 3-D of the computer segmentations compared with the independent standard obtained from a retinal
specialist was 3.4 ± 2.5 voxels (0.10 ± 0.07 mm).
The shape of the optic nerve head (ONH) is reconstructed automatically using stereo fundus color images by a robust
stereo matching algorithm, which is needed for a quantitative estimate of the amount of nerve fiber loss for patients with
glaucoma. Compared to natural scene stereo, fundus images are noisy because of the limits on illumination conditions
and imperfections of the optics of the eye, posing challenges to conventional stereo matching approaches. In this paper,
multi scale pixel feature vectors which are robust to noise are formulated using a combination of both pixel intensity and
gradient features in scale space. Feature vectors associated with potential correspondences are compared with a disparity
based matching score. The deep structures of the optic disc are reconstructed with a stack of disparity estimates in scale
space. Optical coherence tomography (OCT) data was collected at the same time, and depth information from 3D
segmentation was registered with the stereo fundus images to provide the ground truth for performance evaluation. In
experiments, the proposed algorithm produces estimates for the shape of the ONH that are close to the OCT based shape,
and it shows great potential to help computer-aided diagnosis of glaucoma and other related retinal diseases.
Optical coherence tomography (OCT), being a noninvasive imaging modality, has begun to find vast use in
the diagnosis and management of ocular diseases such as glaucoma, where the retinal nerve fiber layer (RNFL)
has been known to thin. Furthermore, the recent availability of the considerably larger volumetric data with
spectral-domain OCT has increased the need for new processing techniques. In this paper, we present an
automated 3-D graph-theoretic approach for the segmentation of 7 surfaces (6 layers) of the retina from 3-D
spectral-domain OCT images centered on the optic nerve head (ONH). The multiple surfaces are detected
simultaneously through the computation of a minimum-cost closed set in a vertex-weighted graph constructed
using edge/regional information, and subject to a priori determined varying surface interaction and smoothness
constraints. The method also addresses the challenges posed by presence of the large blood vessels and the optic
disc. The algorithm was compared to the average manual tracings of two observers on a total of 15 volumetric
scans, and the border positioning error was found to be 7.25 ± 1.08 μm and 8.94 ± 3.76 μm for the normal and
glaucomatous eyes, respectively. The RNFL thickness was also computed for 26 normal and 70 glaucomatous
scans where the glaucomatous eyes showed a significant thinning (p < 0.01, mean thickness 73.7 ± 32.7 μm in
normal eyes versus 60.4 ± 25.2 μm in glaucomatous eyes).
The recent introduction of next generation spectral OCT scanners has enabled routine acquisition of high resolution,
3D cross-sectional volumetric images of the retina. 3D OCT is used in the detection and management
of serious eye diseases such as glaucoma and age-related macular degeneration. For follow-up studies, image
registration is a vital tool to enable more precise, quantitative comparison of disease states. This work presents a
registration method based on a recently introduced extension of the 2D Scale-Invariant Feature Transform (SIFT)
framework1 to 3D.2 The SIFT feature extractor locates minima and maxima in the difference of Gaussian scale
space to find salient feature points. It then uses histograms of the local gradient directions around each found
extremum in 3D to characterize them in a 4096 element feature vector. Matching points are found by comparing
the distance between feature vectors. We apply this method to the rigid registration of optic nerve head- (ONH)
and macula-centered 3D OCT scans of the same patient that have only limited overlap. Three OCT data set
pairs with known deformation were used for quantitative assessment of the method's robustness and accuracy
when deformations of rotation and scaling were considered. Three-dimensional registration accuracy of 2.0±3.3
voxels was observed. The accuracy was assessed as average voxel distance error in N=1572 matched locations.
The registration method was applied to 12 3D OCT scans (200 x 200 x 1024 voxels) of 6 normal eyes imaged in
vivo to demonstrate the clinical utility and robustness of the method in a real-world environment.
Glaucoma is a group of diseases which can cause vision loss and blindness due to gradual damage to the optic
nerve. The ratio of the optic disc cup to the optic disc is an important structural indicator for assessing the
presence of glaucoma. The purpose of this study is to develop and evaluate a method which can segment the
optic disc cup and neuroretinal rim in spectral-domain OCT scans centered on the optic nerve head. Our method
starts by segmenting 3 intraretinal surfaces using a fast multiscale 3-D graph search method. Based on one of
the segmented surfaces, the retina of the OCT volume is flattened to have a consistent shape across scans and
patients. Selected features derived from OCT voxel intensities and intraretinal surfaces were used to train a
k-NN classifier that can determine which A-scans in the OCT volume belong to the background, optic disc cup
and neuroretinal rim. Through 3-fold cross validation with a training set of 20 optic nerve head-centered OCT
scans (10 right eye scans and 10 left eye scans from 10 glaucoma patients) and a testing set of 10 OCT scans (5
right eye scans and 5 left eye scans from 5 different glaucoma patients), segmentation results of the optic disc
cup and rim for all 30 OCT scans were obtained. The average unsigned errors of the optic disc cup and rim were
1.155 ± 1.391 pixels (0.035 ± 0.042 mm) and 1.295 ± 0.816 pixels (0.039 ± 0.024 mm), respectively.
The optic disc margin is of interest due to its use for detecting and managing glaucoma. We developed a
method for segmenting the optic disc margin of the optic nerve head (ONH) in spectral-domain optical coherence
tomography (OCT) images using a graph-theoretic approach. A small number of slices surrounding the Bruch's
membrane opening (BMO) plane was taken and used for creating planar 2-D projection images. An edge-based
cost function - more specifically, a signed edge-based term favoring a dark-to-bright transition in the
vertical direction of polar projection images (corresponding to the radial direction in Cartesian coordinates)
- was obtained. Information from the segmented vessels was used to suppress the vasculature influence by
modifying the polar cost function and remedy the segmentation difficulty due to the presence of large vessels.
The graph search was performed in the modified edge-based cost images. The algorithm was tested on 22
volumetric OCT scans. The segmentation results were compared with expert segmentations on corresponding
stereo fundus disc photographs. We found a signed mean difference of 0.0058 ± 0.0706 mm and an unsigned
mean difference of 0.1083 ± 0.0350 mm between the automatic and expert segmentations.
KEYWORDS: Image segmentation, 3D modeling, Quantitative analysis, Computed tomography, Medical imaging, Statistical modeling, 3D image processing, Medicine, Visualization, Surgery
An abdominal aortic aneurysm (AAA) is an area of a localized widening of the abdominal aorta, with a frequent
presence of thrombus. A ruptured aneurysm can cause death due to severe internal bleeding. AAA thrombus
segmentation and quantitative analysis are of paramount importance for diagnosis, risk assessment, and
determination of treatment options. Until now, only a small number of methods for thrombus segmentation
and analysis have been presented in the literature, either requiring substantial user interaction or exhibiting
insufficient performance. We report a novel method offering minimal user interaction and high accuracy. Our
thrombus segmentation method is composed of an initial automated luminal surface segmentation, followed by a
cost function-based optimal segmentation of the inner and outer surfaces of the aortic wall. The approach utilizes
the power and flexibility of the optimal triangle mesh-based 3-D graph search method, in which cost functions for
thrombus inner and outer surfaces are based on gradient magnitudes. Sometimes local failures caused by image
ambiguity occur, in which case several control points are used to guide the computer segmentation without the
need to trace borders manually. Our method was tested in 9 MDCT image datasets (951 image slices). With the
exception of a case in which the thrombus was highly eccentric, visually acceptable aortic lumen and thrombus
segmentation results were achieved. No user interaction was used in 3 out of 8 datasets, and 7.80 ± 2.71 mouse
clicks per case / 0.083 ± 0.035 mouse clicks per image slice were required in the remaining 5 datasets.
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