We introduce a framework for quantitative evaluation of 3D vessel segmentation approaches using vascular phantoms. Phantoms are designed using a CAD system and created with a 3D printer, and comprise realistic shapes including branches and pathologies such as abdominal aortic aneurysms (AAA). To transfer ground truth information to the 3D image coordinate system, we use a landmark-based registration scheme utilizing fiducial markers integrated in the phantom design. For accurate 3D localization of the markers we developed a novel 3D parametric intensity model that is directly fitted to the markers in the images. We also performed a quantitative evaluation of different vessel segmentation approaches for a phantom of an AAA.
KEYWORDS: Image segmentation, 3D modeling, 3D image processing, Data modeling, Medical imaging, Affine motion model, Diagnostics, Radiology, Pathology, Image resolution
We introduce a new model-based approach for the segmentation of the thoracic aorta and its main branches from follow-up pediatric 3D MRA image data. For robust segmentation of vessels even in difficult cases (e.g., neighboring structures), we propose a new extended parametric cylinder model which requires only relatively few model parameters. The new model is used in conjunction with a two-step fitting scheme for refining the segmentation result yielding an accurate segmentation of the vascular shape. Moreover, we include a novel adaptive background masking scheme and we describe a spatial normalization scheme to align the segmentation results from follow-up examinations. We have evaluated our proposed approach using different 3D synthetic images and we have successfully applied the approach to follow-up pediatric 3D MRA image data.
KEYWORDS: 3D modeling, 3D image processing, Shape analysis, Image segmentation, Spherical lenses, Microscopy, Data modeling, Confocal microscopy, Time lapse microscopy, Visual process modeling
We propose a novel approach for 3D shape analysis of heterochromatin foci in 3D confocal light microscopy images of cell nuclei. The approach is based on a 3D parametric intensity model and uses a spherical harmonics (SH) expansion. The model parameters including the SH coefficients are automatically determined by least squares fitting of the model to the image intensities. Based on the obtained SH coefficients, a shape descriptor is determined, which enables distinguishing heterochromatin foci based on their 3D shape to characterize compaction states of heterochromatin. Our approach has been successfully applied to real static and dynamic 3D microscopy image data.
This paper introduces a new approach to segment vessels from medical images using the fast marching method.
Our approach relies on an iterative scheme: Starting from a given start point and initial direction, the optimal
path within a circular region of interest (ROI) around this point is found using the fast marching method and a
combination of different speed functions. Besides using speed functions based on a vesselness measure and the
vessel radius, we introduce a directional speed function which prefers directions close to the predicted direction.
The end point of the detected path is then used as the new start point to find again the optimal path within a
new ROI centered around this point. This procedure is repeated until the user-specified end point is reached,
or some other termination criterion is satisfied. The final result is the concatenation of the sequence of paths of
the individual ROIs. Our approach has been applied to synthetic and real datasets. The experiments show that
our approach is not only more efficient than a previous fast marching approach but also produces better results
when dealing with short cuts and crossings in the segmentation of long vessels.
We introduce a new approach for spline-based elastic registration using both point landmarks and intensity
information. With this approach, both types of information and a regularization based on the Navier equation
are directly integrated in a single energy minimizing functional. For this functional, we have derived an analytic
solution, which is based on matrix-valued non-radial basis functions. Our approach can cope with monomodal
and multimodal images. For the latter case, we have integrated a computationally efficient analytic similarity
measure. We have successfully applied our approach to synthetic images, phantom images, and MR images of
the human brain.
We introduce a new 3D curved tubular intensity model in conjunction with a model fitting scheme for accurate
segmentation and quantification of thin vessels in 3D tomographic images. The curved tubular model is formulated
based on principles of the image formation process, and we have derived an analytic solution for the model
function. In contrast to previous straight models, the new model allows to accurately represent curved tubular
structures, to directly estimate the local curvature by model fitting, as well as to more accurately estimate the
shape and other parameters of tubular structures. We have successfully applied our approach to 3D synthetic
images as well as 3D MRA and 3D CTA vascular images of the human. It turned out that we achieved more accurate segmentation results in comparison to using a straight model.
We introduce a new model-based approach for automatic quantification of colocalizations in multi-channel 3D
microscopy images. The approach is based on different 3D parametric intensity models in conjunction with a
model fitting scheme to localize and quantify subcellular structures with high accuracy. The central idea is to
determine colocalizations between different channels based on the estimated geometry of subcellular structures as
well as to differentiate between different types of colocalizations. Furthermore, we perform a statistical analysis
to assess the significance of the determined colocalizations. We have successfully applied our approach to about
400 three-channel 3D microscopy images of human soft-tissue tumors.
We present an approach for the quantification of fluorescent spots in time series of 3-D confocal microscopy
images of endoplasmic reticulum exit sites of dividing cells. Fluorescent spots are detected based on extracted
image regions of highest response using the HMAX transform and prior convolution of the 3-D images with a
Gaussian kernel. The sensitivity of the involved parameters was studied and a quantitative evaluation using
both 3-D synthetic and 3-D real data was performed. The approach was successfully applied to more than one
thousand 3-D confocal microscopy images.
KEYWORDS: 3D modeling, 3D image processing, Image segmentation, Particle filters, 3D metrology, Filtering (signal processing), Data modeling, Automatic tracking, Statistical modeling, Affine motion model
We introduce a new approach for tracking-based segmentation of 3D tubular structures. The approach is based
on a novel combination of a 3D cylindrical intensity model and particle filter tracking. In comparison to earlier
work we utilize a 3D intensity model as the measurement model of the particle filter, thus a more realistic 3D
appearance model is used that directly represents the image intensities of 3D tubular structures within semiglobal
regions-of-interest. We have successfully applied our approach using 3D synthetic images and real 3D
MRA image data of the human pelvis.
The qualitative and quantitative comparison of pre- and postoperative image data is an important possibility
to validate surgical procedures, in particular, if computer assisted planning and/or navigation is performed.
Due to deformations after surgery, partially caused by the removal of tissue, a non-rigid registration scheme is
a prerequisite for a precise comparison. Interactive landmark-based schemes are a suitable approach, if high
accuracy and reliability is difficult to achieve by automatic registration approaches. Incorporation of a priori
knowledge about the anatomical structures to be registered may help to reduce interaction time and improve
accuracy. Concerning pre- and postoperative CT data of oncological liver resections the intrahepatic vessels
are suitable anatomical structures. In addition to using branching landmarks for registration, we here introduce
quasi landmarks at vessel segments with high localization precision perpendicular to the vessels and low precision
along the vessels. A comparison of interpolating thin-plate splines (TPS), interpolating Gaussian elastic body
splines (GEBS) and approximating GEBS on landmarks at vessel branchings as well as approximating GEBS
on the introduced vessel segment landmarks is performed. It turns out that the segment landmarks provide
registration accuracies as good as branching landmarks and can improve accuracy if combined with branching
landmarks. For a low number of landmarks segment landmarks are even superior.
We introduce a new hybrid physics-based approach for elastic image registration using approximating splines. As underlying deformation model we employ Gaussian elastic body splines (GEBS), which are an analytic solution of the Navier equation under Gaussian forces and are represented by matrix-valued basis functions. Our approach is formulated as an energy-minimizing functional that incorporates both landmark and intensity information as well as a regularization based on GEBS. We also include landmark localization uncertainties represented by weight matrices. Since the approach is based on a physical deformation model, cross-effects in elastic deformations can be handled. We demonstrate the applicability of our scheme based on MR images of the human brain. It turns out that the new scheme is superior to a pure landmark-based as well as a pure intensity-based scheme.
We introduce a new model-based approach for the segmentation and quantification of the aortic arch morphology in 3D CTA images for endovascular aortic repair (EVAR). The approach is based on a 3D analytic intensity model for thick vessels, which is directly fitted to the image. Based on the fitting results we compute the (local) 3D vessel curvature and torsion as well as the relevant lengths not only along the 3D centerline but particularly along the inner and outer contour. These measurements are important for pre-operative planning in EVAR applications. We have successfully applied our approach using ten 3D CTA images and have compared the results with ground truth obtained by a radiologist. It turned out that our approach yields accurate estimation results. We have also performed a comparison with a commercial vascular analysis software.
We introduce a model-based approach for segmenting and quantifying GFP-tagged subcellular structures of the Golgi apparatus in 2D and 3D microscopy images. The approach is based on 2D and 3D intensity models, which are directly fitted to an image within 2D circular or 3D spherical regions-of-interest (ROIs). We also propose automatic approaches for the detection of candidates, for the initialization of the model parameters, and for adapting the size of the ROI used for model fitting. Based on the fitting results, we determine statistical information about the spatial distribution and the total amount of intensity (fluorescence) of the subcellular structures. We demonstrate the applicability of our new approach based on 2D and 3D microscopy images.
KEYWORDS: 3D modeling, Image segmentation, 3D image processing, Data modeling, Hough transforms, Medical imaging, Arteries, Visual process modeling, Tomography, Filtering (signal processing)
We introduce an adaptive model fitting approach for the segmentation of vessels from 3D tomographic images.
With this approach the shape and size of the 3D region-of-interest (ROI) used for model fitting are automatically
adapted to the local width, curvature, and orientation of a vessel to increase the robustness and accuracy. The
approach uses a 3D cylindrical model and has been successfully applied to segment human vessels from 3D
MRA image data. Our experiments show that the new adaptive scheme yields superior segmentation results in
comparison to using a fixed size ROI. Moreover, a validation of the approach based on ground-truth provided
by a radiologist confirms its accuracy. In addition, we also performed an experimental comparison of the new
approach with a previous scheme.
KEYWORDS: 3D modeling, 3D image processing, Image segmentation, Microscopy, Luminescence, Bacteria, Point spread functions, Superposition, Image processing, Data modeling
We introduce a new model-based approach for segmenting and quantifying fluorescent bacteria in 3D microscopy
live cell images. The approach is based on a new 3D superellipsoidal parametric intensity model, which is directly
fitted to the image intensities within 3D regions-of-interest. Based on the fitting results, we can directly compute
the total amount of intensity (fluorescence) of each cell. In addition, we introduce a method for automatic
initialization of the model parameters, and we propose a method for simultaneously fitting clustered cells by
using a superposition of 3D superellipsoids for model fitting. We demonstrate the applicability of our approach
based on 3D synthetic and real 3D microscopy images.
KEYWORDS: Image registration, 3D image processing, Magnetic resonance imaging, Medical imaging, 3D acquisition, Image restoration, Neuroimaging, Brain, Computing systems, Tumors
We introduce a new hybrid approach for spline-based elastic image registration using both landmarks and intensity
information. As underlying deformation model we use Gaussian elastic body splines (GEBS), which
are solutions of the Navier equation of linear elasticity under Gaussian forces. We also incorporate landmark
localization uncertainties, which are characterized by weight matrices representing anisotropic errors. To combine
landmarks and intensity information, we formulate an energy-minimizing functional that simultaneously
minimizes w.r.t. both the landmark and intensity information. The resulting functional can be efficiently minimized
using the method of Levenberg/Marquardt. Since the approach is based on a physical deformation model,
cross-effects in elastic deformations can be taken into account. We demonstrate the applicability of our scheme
based on 3D synthetic images, 2D MR images of the brain, as well as 2D gel electrophoresis images. It turns out
that the new scheme achieves more accurate results compared to a pure landmark-based approach.
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