Comprehensive quantitative evaluation of tumor segmentation technique on large scale clinical data sets is crucial
for routine clinical use of CT based tumor volumetry for cancer diagnosis and treatment response evaluation.
In this paper, we present a systematic validation study of a semi-automatic image segmentation technique for
measuring tumor volume from CT images. The segmentation algorithm was tested using clinical data of 200
tumors in 107 patients with liver, lung, lymphoma and other types of cancer. The performance was evaluated
using both accuracy and reproducibility. The accuracy was assessed using 7 commonly used metrics that can
provide complementary information regarding the quality of the segmentation results. The reproducibility was
measured by the variation of the volume measurements from 10 independent segmentations. The effect of
disease type, lesion size and slice thickness of image data on the accuracy measures were also analyzed. Our
results demonstrate that the tumor segmentation algorithm showed good correlation with ground truth for all
four lesion types (r = 0.97, 0.99, 0.97, 0.98, p < 0.0001 for liver, lung, lymphoma and other respectively). The
segmentation algorithm can produce relatively reproducible volume measurements on all lesion types (coefficient
of variation in the range of 10-20%). Our results show that the algorithm is insensitive to lesion size (coefficient
of determination close to 0) and slice thickness of image data(p > 0.90). The validation framework used in this
study has the potential to facilitate the development of new tumor segmentation algorithms and assist large scale
evaluation of segmentation techniques for other clinical applications.
The manual segmentation and analysis of high-resolution multislice cardiac CT datasets is both labor intensive and time consuming. Therefore it is necessary to supply the cardiologist with powerful software tools to segment the myocardium as well as the cardiac cavities and to compute the relevant diagnostic parameters. In this paper we present an automatic cardiac segmentation procedure with minimal user interaction. It is based on a combined bi-temporal statistical model of the left and right ventricle using the principal component analysis (PCA) as well as the independent component analysis (ICA) to model global and local shape variation. To train the model we used manually drawn end-diastolic as well as end-systolic contours of the right epi- and of the left and right endocardium to create triangular surfaces of training datasets. These surfaces were used to build a mean triangular surface model of the left and right ventricle for the end-diastolic and end-systolic heart phase and to compute the PCA and ICA decorrelation matrices which are used in a point distribution model (PDM) to model the global and local shape variations. In contrast to many previous attempts of model based cardiac segmentation we do not create separate models for the left and the right ventricle and for different heart phases, but instead create one single parameter vector containing the information of both ventricles and both heart phases. This enables us to use the correlation between the phases and between left and right side to create a model which is more robust and less sensitive e.g. to poor contrast at the right ventricle.
Multi-slice computed tomography (MSCT) has developed strongly in the emerging field of cardiovascular imaging. The manual analysis of atherosclerotic plaques in coronary arteries is a very time consuming and labor intensive process and today only qualitative analysis is possible. In this paper we present a new shape-based segmentation and visualization technique for quantitative analysis of atherosclerotic plaques in coronary artery disease. The new technique takes into account several aspects of the vascular anatomy. It uses two surface representations, one for the contrast filled vessel lumen and also one for the vascular wall. The deviation between these two surfaces is defined as plaque volume. These surface representations can be edited by the user manually. With this kind of representation it is possible to calculate sub plaque volumes (such as: lipid rich core, fibrous tissue, calcified tissue) inside this suspicious area. Also a high quality 3D visualization, using Open Inventor is possible.
In the diagnosis of coronary artery disease, 3D-multi-slice
computed tomography (MSCT) has recently become more and more
important. In this work, an anatomical-based method for the
segmentation of atherosclerotic coronary arteries in MSCT is
presented. This technique is able to bridge severe stenosis, image
artifacts or even full vessel occlusions. Different anatomical
structures (aorta, blood-pool of the heart chambers, coronary
arteries and their orifices) are detected successively to
incorporate anatomical knowledge into the algorithm. The coronary
arteries are segmented by a simulated wave propagation method to
be able to extract anatomically spatial relations from the result.
In order to bridge segmentation breaks caused by stenosis or image
artifacts, the spatial location, its anatomical relation and
vessel curvature-propagation are taken into account to span a
dynamic search space for vessel bridging and gap closing. This
allows the prevention of vessel misidentifications and improves
segmentation results significantly. The robustness of this method
is proven on representative medical data sets.
KEYWORDS: Image segmentation, Statistical modeling, Data modeling, Diagnostics, Visualization, Heart, Principal component analysis, Visual process modeling, 3D modeling, Mahalanobis distance
The manual segmentation and analysis of high-resolution multi-slice cardiac CT datasets is both labor intensive and time consuming. Therefore it is necessary to supply the cardiologist with powerful software tools to segment the myocardium and compute the relevant diagnostic parameters. In this work we present a semi-automatic cardiac segmentation approach with minimal user interaction. It is based on a combination of an adaptive slice-based regiongrowing and a modified Active Shape Model (ASM). Starting with a single manual click point in the ascending aorta, the aorta, the left atrium and the left ventricle get segmented with the slice-based adaptive regiongrowing. The approximate position of the aortic and mitral valve as well as the principal axes of the left ventricle (LV) are determined. To prevent the regiongrowing from draining into neighboring anatomical structures via CT artifacts, we implemented a draining control by examining a cubic region around the currently processed voxel. Additionally, we use moment-based parameters to integrate simple anatomical knowledge into the regiongrowing process.
Using the results of the preceding regiongrowing process, a ventricle-centric and normalized coordinate system is established
which is used to adapt a previously trained ASM to the image, using an iterative multi-resolution approach. After fitting the ASM to the image, we can use the generated model-points to create an exact surface model of the left ventricular myocardium for visualization and for computing the diagnostically relevant parameters, like the ventricular blood volume and the myocardial wall thickness.
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