KEYWORDS: Lung, Image segmentation, Tissues, Magnetic resonance imaging, Data modeling, Pathology, Statistical modeling, 3D magnetic resonance imaging, Cancer, Image resolution
MRI perfusion images give information about regional lung function and can be used to detect pulmonary
pathologies in cystic fibrosis (CF) children. However, manual assessment of the percentage of pathologic tissue in
defined lung subvolumes features large inter- and intra-observer variation, making it difficult to determine disease
progression consistently. We present an automated method to calculate a regional score for this purpose. First,
lungs are located based on thresholding and morphological operations. Second, statistical shape models of left
and right children's lungs are initialized at the determined locations and used to precisely segment morphological
images. Segmentation results are transferred to perfusion maps and employed as masks to calculate perfusion
statistics. An automated threshold to determine pathologic tissue is calculated and used to determine accurate
regional scores. We evaluated the method on 10 MRI images and achieved an average surface distance of less
than 1.5 mm compared to manual reference segmentations. Pathologic tissue was detected correctly in 9 cases.
The approach seems suitable for detecting early signs of CF and monitoring response to therapy.
The ratio between the amount of adipose and skeletal muscle tissue is an important determinant of metabolic
health. Recent developments in MRI technology allow whole body scans to be performed for accurate assessment
of body composition. In the present study, a total of 194 participants underwent a 2-point Dixon MRI sequence
of the whole body. A fully automated image segmentation method quantifies the amount of adipose and skeletal
muscle tissue by applying standard image processing techniques including thresholding, region growing and
morphological operators. The adipose tissue is further divided into subcutaneous and visceral adipose tissue by
using statistical shape models. All images were visually inspected. The quantitative analysis was performed
on 44 whole-body MRI data using manual segmentations as ground truth data. We achieved 3.3% and 6.3%
of relative volume difference between the manual and automated segmentation of subcutaneous and visceral
adipose tissue, respectively. The validation of skeletal muscle tissue segmentation resulted in a relative volume
difference of 7.8 ± 4.2% and a volumetric overlap error of 6.4 ± 2.3 %. To our knowledge, we are first to present
a fully automated method which quantifies adipose and skeletal muscle tissue in whole-body MRI data. Due to
the fully automated approach, results are deterministic and free of user bias. Hence, the software can be used in
large epidemiological studies for assessing body fat distribution and the ratio of adipose to skeletal muscle tissue
in relation to metabolic disease risk.
Mitral regurgitation is a wide spread problem. For successful surgical treatment quantification of the mitral
annulus, especially its diameter, is essential. Time resolved 3D transesophageal echocardiography (TEE) is
suitable for this task. Yet, manual measurement in four dimensions is extremely time consuming, which confirms
the need for automatic quantification methods. The method we propose is capable of automatically detecting
the cardiac cycle (systole or diastole) for each time step and measuring the mitral annulus diameter. This is
done using total variation noise filtering, the graph cut segmentation algorithm and morphological operators.
An evaluation took place using expert measurements on 4D TEE data of 13 patients. The cardiac cycle was
detected correctly on 78% of all images and the mitral annulus diameter was measured with an average error of
3.08 mm. Its full automatic processing makes the method easy to use in the clinical workflow and it provides
the surgeon with helpful information.
KEYWORDS: Computed tomography, Error analysis, Time of flight cameras, Data modeling, Data acquisition, Image registration, Natural surfaces, Target detection, Magnetic resonance imaging, Reliability
Range imaging modalities, such as time-of-flight cameras (ToF), are becoming very popular for the acquisition of intra-operative
data, which can be used for registering the patient's anatomy with pre-operative data, such as 3D images generated
by computed tomographies (CT) or magnetic resonance imaging (MRI). However, due to the distortions that appear because
of the different acquisition principles of the input surfaces, the noise, and the deformations that may occur in the intra-operative
environment, we face different surface properties for points lying on the same anatomical locations and unreliable
feature points detection, which are crucial for most surface matching algorithms. In order to overcome these issues,
we present a method for automatically finding correspondences between surfaces that searches for minimally deformed
configurations. For this purpose, an error metric that expresses the reliability of a correspondence set based on its spatial
configuration is employed. The registration error is minimized by a combinatorial analysis through search-trees. Our
method was evaluated with real and simulated ToF and CT data, and showed to be reliable for the registration of partial
multi-modal surfaces with noise and distortions.
KEYWORDS: Medical imaging, Algorithm development, Image processing, Current controlled current source, Cameras, 3D acquisition, 3D image processing, 3D-TOF imaging, Image registration, In vitro testing
Time-of-flight (ToF) cameras are a novel, fast, and robust means for intra-operative 3D surface acquisition. They acquire surface information (range images) in real-time. In the intra-operative registration context, these surfaces must be matched to pre-operative CT or MR surfaces, using so called descriptors, which represent surface characteristics. We present a framework for local and global multi-modal comparison of surface descriptors and characterize the differences between ToF and CT data in an in vitro experiment. The framework takes into account various aspects related to the surface characteristics and does not require high resolution input data in order to establish appropriate correspondences. We show that the presentation of local and global comparison data allows for an accurate assessment of ToF-CT discrepancies. The information gained from our study may be used for developing ToF pre-processing and matching algorithms, or for improving calibration procedures for compensating systematic distance errors. The framework is available in the open-source platform Medical Imaging Interaction Toolkit (MITK).
KEYWORDS: Arteries, Detection and tracking algorithms, Image segmentation, Optical tracking, 3D image processing, Medical imaging, Data modeling, Image quality, Cancer, Medical research
Vessel tree tracking is an important and challenging task for many medical applications. This paper presents
a novel bifurcation detection algorithm for Bayesian tracking of vessel trees. Based on a cylindrical model, we
introduce a bifurcation metric that yields minimal values at potential branching points. This approach avoids
searching for bifurcations in every iteration of the tracking process (as proposed by prior works) and is therefore
computationally more efficient. We use the same geometric model for the bifurcation metric as for the tracking;
no specific bifurcation model is needed. In a preliminary evaluation of our method on 8 CTA datasets of coronary
arteries, all side branches and 95.8% of the main branches were detected correctly.
Obesity is an increasing problem in the western world and triggers diseases like cancer, type two diabetes, and
cardiovascular diseases. In recent years, magnetic resonance imaging (MRI) has become a clinically viable method
to measure the amount and distribution of adipose tissue (AT) in the body. However, analysis of MRI images
by manual segmentation is a tedious and time-consuming process. In this paper, we propose a semi-automatic
method to quantify the amount of different AT types from whole-body MRI data with less user interaction.
Initially, body fat is extracted by automatic thresholding. A statistical shape model of the abdomen is then
used to differentiate between subcutaneous and visceral AT. Finally, fat in the bone marrow is removed using
morphological operators. The proposed method was evaluated on 15 whole-body MRI images using manual
segmentation as ground truth for adipose tissue. The resulting overlap for total AT was 93.7% ± 5.5 with a
volumetric difference of 7.3% ± 6.4. Furthermore, we tested the robustness of the segmentation results with regard
to the initial, interactively defined position of the shape model. In conclusion, the developed method proved
suitable for the analysis of AT distribution from whole-body MRI data. For large studies, a fully automatic
version of the segmentation procedure is expected in the near future.
KEYWORDS: Image segmentation, Data modeling, Statistical modeling, Heart, Process modeling, Shape analysis, Medical imaging, Model-based design, Magnetic resonance imaging, 3D modeling
Due to noise and artifacts often encountered in medical images, segmenting objects in these is one of the most
challenging tasks in medical image analysis. Model-based approaches like statistical shape models (SSMs) incorporate
prior knowledge that supports object detection in case of in-complete evidence from image data. In this paper, we present
a method to increase information of the object's shape in problematic image areas by incorporating mutual shape
information from other entities in the image. This is done by using a common shape space of multiple objects as
additional restriction. Two different approaches to implement mutual shape information are presented. Evaluation was
performed on nine cardiac images by simultaneous segmentation of the epi- and endocardium of the left heart ventricle
using the proposed methods. The results show that the segmentation quality is improved with both methods. For the
better one, the average surface distance error is approx. 40% lower.
The random walker algorithm is a graph-based segmentation method that has become popular over the past few
years. The basis of the algorithm is a large, sparsely occupied system of linear equations, whose size corresponds
to the number of voxels in the image. To solve these systems, typically comprised of millions of equations,
the computational performance of conventional numerical solution methods (e.g. Gauss-Seidel) is no longer
satisfactory. An alternative method that has been described previously for solving 2D random walker problems
is the geometrical multigrid method. In this paper, we present a geometrical multigrid approach for the 3D
random walker problem. Our approach features an optimized calculation of the required Galerkin product and
a robust smoothing using the ILUβ method. To reach better convergence rates, the multigrid solver is used as a
preconditioner for the conjugate gradient solver. We compared the performance of our new multigrid approach
with the conjugate gradient solver on five MRI lung images with a resolution of 96 x 128 x 52 voxels. Initial
results show an increasing in speed of up to four times, reducing the average computation time from six minutes
to less than two minutes when using our proposed approach. Employing a multigrid solver for the random walker
algorithm thus permits accurate interactive segmentation with fewer delays.
Objective quantification of disease specific neurodegenerative changes can facilitate diagnosis and therapeutic
monitoring in several neuropsychiatric disorders. Reproducibility and easy-to-perform assessment are essential
to ensure applicability in clinical environments. Aim of this comparative study is the evaluation of a fully
automated approach that assesses atrophic changes in Alzheimer's disease (AD) and Mild Cognitive Impairment
(MCI).
21 healthy volunteers (mean age 66.2), 21 patients with MCI (66.6), and 10 patients with AD (65.1) were
enrolled. Subjects underwent extensive neuropsychological testing and MRI was conducted on a 1.5 Tesla clinical
scanner. Atrophic changes were measured automatically by a series of image processing steps including state of
the art brain mapping techniques. Results were compared with two reference approaches: a manual segmentation
of the hippocampal formation and a semi-automated estimation of temporal horn volume, which is based upon
interactive selection of two to six landmarks in the ventricular system.
All approaches separated controls and AD patients significantly (10-5 < p < 10-4) and showed a slight but
not significant increase of neurodegeneration for subjects with MCI compared to volunteers. The automated
approach correlated significantly with the manual (r = -0.65, p < 10-6) and semi automated (r = -0.83,
p < 10-13) measurements. It proved high accuracy and at the same time maximized observer independency,
time reduction and thus usefulness for clinical routine.
With many tumor entities, quantitative assessment of lymph node growth over time is important to make therapy choices or to evaluate new therapies. The clinical standard is to document diameters on transversal slices, which is not the best measure for a volume. We present a new algorithm to segment (metastatic) lymph nodes and evaluate the algorithm with 29 lymph nodes in clinical CT images. The algorithm is based on a deformable surface search, which uses statistical shape models to restrict free deformation. To model lymph nodes, we construct an ellipsoid shape model, which strives
for a surface with strong gradients and user-defined gray values. The algorithm is integrated into an application, which also allows interactive correction of the segmentation results. The evaluation shows that the algorithm gives good results in the majority of cases and is comparable to time-consuming manual segmentation. The
median volume error was 10.1% of the reference volume before and 6.1% after manual correction. Integrated into an application, it is possible to perform lymph node volumetry for a whole patient within the 10 to 15 minutes time limit imposed by clinical routine.
KEYWORDS: Image segmentation, 3D modeling, Data modeling, Statistical modeling, Prostate, Ultrasonography, 3D image processing, Databases, Medical imaging, Prostate cancer
Due to the high noise and artifacts typically encountered in ultrasound images, segmenting objects from this
modality is one of the most challenging tasks in medical image analysis. Model-based approaches like statistical
shape models (SSMs) incorporate prior knowledge that supports object detection in case of incomplete evidence
from the image data. How well the model adapts to an unseen image is primarily determined by the suitability of
the used appearance model, which evaluates the goodness of fit during model evolution. In this paper, we compare
two gradient profile models with a region-based approach featuring local histograms to detect the prostate in
3D transrectal ultrasound (TRUS) images. All models are used within an SSM segmentation framework with
optimal surface detection for outlier removal. Evaluation was performed using cross-validation on 35 datasets.
While the histogram model failed in 10 cases, both gradient models had only 2 failures and reached an average
surface distance of 1.16 ± 0.38 mm in comparison with interactively generated reference contours.
KEYWORDS: Image segmentation, Liver, Data modeling, Statistical modeling, Heart, 3D modeling, Distance measurement, Medical imaging, Magnetic resonance imaging, Statistical analysis
Statistical shape models have become a fast and robust method for segmentation of anatomical structures in medical image volumes. In clinical practice, however, pathological cases and image artifacts can lead to local deviations of the detected contour from the true object boundary. These deviations have to be corrected manually. We present an intuitively applicable solution for surface interaction based on Gaussian deformation kernels. The method is evaluated by two radiological experts on segmentations of the liver in contrast-enhanced CT images and of the left heart ventricle (LV) in MRI data. For both applications, five datasets are segmented automatically using deformable shape models, and the resulting surfaces are corrected manually. The interactive correction step improves the average surface distance against ground truth from 2.43mm to 2.17mm for the liver, and from 2.71mm to 1.34mm for the LV. We expect this method to raise the acceptance of automatic segmentation methods in clinical application.
Minimizing the description length (MDL) is one of the most promising methods to automatically generate 3D statistical shape models. By modifying an initial landmark distribution according to the MDL cost function, points across the different training shapes are brought into correspondence. A drawback of the current approach is that the user has no influence on the final landmark positions, which often do not represent the modeled shape adequately. We extend an existing remeshing technique to work with statistical shape models and show how the landmark distribution can be modified anytime during the model construction phase. This procedure is guided by a control map in parameter space that can be set up to produce any desired point distribution, e.g. equally spaced landmarks. To compare our remeshed models with the original approach, we generalize the established generalization and specificity measures to be independent of the underlying landmark distribution. This is accomplished by switching the internal metric from landmark distances to the Tanimoto coefficient, a volumetric overlap measure. In a concluding evaluation, we generate models for two medical datasets with and without landmark redistribution. As the outcome reveals, redistributing landmarks to an equally spaced distribution during the model construction phase improves the quality of the resulting models significantly if the shapes feature prominent bulges or other complex geometry.
Computer-assisted surgery aims at a decreased surgical risk and a reduced recovery time of patients. However, its use is still limited to complex cases because of the high effort. It is often caused by the extensive medical image analysis. Especially, image segmentation requires a lot of manual work. Surgeons and radiologists are suffering from usability problems of many workstations.
In this work, we present a dedicated workplace for interactive segmentation integratd within the CHILI (tele-)radiology system. The software comes with a lot of improvements with respect to its graphical user interface, the segmentation process and the segmentatin methods. We point out important software requirements and give insight into the concepts which were implemented. Further examples and applications illustrate the software system.
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