Heinz-Otto Peitgen

Robust vessel segmentation

In the context of cardiac applications, the primary goal of coronary vessel analysis often consists in supporting the diagnosis of vessel wall anomalies, such as coronary plaque and stenosis. Therefore, a fast and robust segmentation of the coronary tree is a very important but challenging task. We propose a new approach for coronary artery segmentation. Our method is based on an earlier proposed progressive region growing. A new growth front monitoring technique controls the segmentation and corrects local leakage by retrospective detection and removal of leakage artifacts. While progressively reducing the region growing threshold for the whole image, the growing process is locally analyzed using criteria based on the assumption of tubular, gradually narrowing vessels. If a voxel volume limit or a certain shape constraint is exceeded, the growing process is interrupted. Voxels affected by a failed segmentation are detected and deleted from the result. To avoid further processing at these positions, a large neighborhood is blocked for growing. Compared to a global region growing without local correction, our new local growth control and the adapted correction can deal with contrast decrease even in very small coronary arteries. Furthermore, our algorithm can efficiently handle noise artifacts and partial volume effects near the myocardium. The enhanced segmentation of more distal vessel parts was tested on 150 CT datasets. Furthermore, a comparison between the pure progressive region growing and our new approach was conducted.

Clinical relevance of model based computer-assisted diagnosis and therapy

The ability to acquire and store radiological images digitally has made this data available to mathematical and scientific methods. With the step from subjective interpretation to reproducible measurements and knowledge, it is also possible to develop and apply models that give additional information which is not directly visible in the data. In this context, it is important to know the characteristics and limitations of each model. Four characteristics assure the clinical relevance of models for computer-assisted diagnosis and therapy: ability of patient individual adaptation, treatment of errors and uncertainty, dynamic behavior, and in-depth evaluation. We demonstrate the development and clinical application of a model in the context of liver surgery. Here, a model for intrahepatic vascular structures is combined with individual, but in the degree of vascular details limited anatomical information from radiological images. As a result, the model allows for a dedicated risk analysis and preoperative planning of oncologic resections as well as for living donor liver transplantations. The clinical relevance of the method was approved in several evaluation studies of our medical partners and more than 2900 complex surgical cases have been analyzed since 2002.

Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images

Studies have demonstrated the feasibility of late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging for guiding the management of patients with sequelae to myocardial infarction, such as ventricular tachycardia and heart failure. Clinical implementation of these developments necessitates a reproducible and reliable segmentation of the infarcted regions. It is challenging to compare new algorithms for infarct segmentation in the left ventricle (LV) with existing algorithms. Benchmarking datasets with evaluation strategies are much needed to facilitate comparison. This manuscript presents a benchmarking evaluation framework for future algorithms that segment infarct from LGE CMR of the LV. The image database consists of 30 LGE CMR images of both humans and pigs that were acquired from two separate imaging centres. A consensus ground truth was obtained for all data using maximum likelihood estimation. Six widely-used fixed-thresholding methods and five recently developed algorithms are tested on the benchmarking framework. Results demonstrate that the algorithms have better overlap with the consensus ground truth than most of the n-SD fixed-thresholding methods, with the exception of the Full-Width-at-Half-Maximum (FWHM) fixed-thresholding method. Some of the pitfalls of fixed thresholding methods are demonstrated in this work. The benchmarking evaluation framework, which is a contribution of this work, can be used to test and benchmark future algorithms that detect and quantify infarct in LGE CMR images of the LV. The datasets, ground truth and evaluation code have been made publicly available through the website: https://www.cardiacatlas.org/web/guest/challenges.

Motion analysis with quadrature filter based registration of tagged MRI sequences

Analysis of tagged MRI is a valuable tool for assessing regional myocardial function. One major obstacle for existing methods based on feature extraction and registration is the desaturation of the tagging grid over time. We propose a method based on quadrature filters that is invariant to changes in intensity, robust with respect to the grid geometry and provides a dense motion field that allows for the analysis of both global and local movements. A multi-scale and multi-resolution scheme is used to cover different scales of motion and to speed up registration. The described method has been integrated into a prototypical application and applied to a phantom data set and 15 volunteer data sets provided by the STACOM11. The automatic detection of the 4D motion field took about 130 minutes per MRI data set and about 90 minutes per US data set and resulted in plausible motion fields, which will be quantitatively assessed within the motion tracking challenge at MICCAI 2011.

A combined algorithm for breast MRI motion correction

Correction of patient motion is a fundamental preprocessing step for dynamic contrast-enhanced (DCE) breast MRI, removing artifacts induced by involuntary movement and facilitating quantitative analysis of contrast agent kinetics. Image registration algorithms commonly employed for this task align subsequent temporal images of the dynamic MRI by maximizing intensity-, correlation- or entropy-based similarity measures between image pairs. To compensate for global patient motion, frequently an initial affine linear or rigid transformation is estimated. Subsequently, local image variablity is reduced by maximizing local similarity measures and using viscous fluid or elastic regularization terms. We present a novel iterative scheme combining local and global registration into one single algorithm, limiting computational overhead, reducing interpolation artifacts and generally improving the quality of registration results. The relation between local and global motion is adjusted by the introduction of corresponding flexible weighting functions, allowing for a sound combination of both registration types and a potentially wider range of computable transformations. The proposed method is evaluated on both synthetic images and clinical breast MRI data. The results demonstrate that our method works stable and reliably compensates for common motion artifacts typical to DCE MR mammography.

Automatic segmentation of relevant structures in DCE MR mammograms

The automatic segmentation of relevant structures such as skin edge, chest wall, or nipple in dynamic contrast enhanced MR imaging (DCE MRI) of the breast provides additional information for computer aided diagnosis (CAD) systems. Automatic reporting using BI-RADS criteria benefits of information about location of those structures. Lesion positions can be automatically described relatively to such reference structures for reporting purposes. Furthermore, this information can assist data reduction for computation expensive preprocessing such as registration, or for visualization of only the segments of current interest. In this paper, a novel automatic method for determining the air-breast boundary resp. skin edge, for approximation of the chest wall, and locating of the nipples is presented. The method consists of several steps which are built on top of each other. Automatic threshold computation leads to the air-breast boundary which is then analyzed to determine the location of the nipple. Finally, results of both steps are starting point for approximation of the chest wall. The proposed process was evaluated on a large data set of DCE MRI recorded by T1 sequences and yielded reasonable results in all cases.

Mixture-Model-Based segmentation of myocardial delayed enhancement MRI

Myocardial viability assessment is an important task in the diagnosis of coronary heart disease. The measurement of the delayed enhancement effect, the accumulation of contrast agent in defective tissue, has become the gold standard for detecting necrotic tissue with MRI. The purpose of the presented work was to provide a segmentation and quantification method for delayed enhancement MRI. To this end, a suitable mixture model for the myocardial intensity distribution is determined based on expectation maximization and the comparison of the fit accuracy. The subsequent watershed-based segmentation uses the intensity threshold information derived from this model. Preliminary results are derived from an analysis of datasets provided by the STACOM challenge organizers. The segmentation provided reasonable results in all datasets, but the method strongly depends on the underlying myocardium segmentation.

Evaluation of Active Appearance Models for Cardiac MRI

Robust delineation of short-axis cardiac magnetic resonance images (MRI) is a fundamental precondition for functional heart diagnostics. Segmentation of the myocardium and the left ventricular blood pool allows for the analysis of important quantitative parameters. Model-based segmentation methods based on representative image data provide an inherently stable tool for this task. We present an implementation and evaluation of 3-D Active Appearance Models for the segmentation of the left ventricle using actual clinical case images. Models created from varying random data sets have been evaluated and compared with manual segmentations.

Quadrature filter based motion analysis for 3d ultrasound sequences

Analysis of echocardiograms is a valuable tool for assessing myocardial function and diseases. Processing of ultrasound data is challenging due to noise levels and depth-dependent quality of structure edges. We propose to adapt a method based on quadrature filters that is invariant to changes in intensity and has been successfully applied to MRI data earlier. Quadrature-filter-based registration derives the spatial deformation between two images from the local phase shift. Because the local phase is intensity-invariant and requires inhomogeneity, e.g., noise and intensity variations, to properly pick up phase shifts, it is well suited for ultrasound data. A multi-resolution and multi-scale scheme is used to cover different scales of deformations. The type and strength of regularization of the dense deformation field can be specified for each level, allowing for weighting of global and local motion. To speed up the registration, deformation fields are determined slice-wise for three orientations of the original data and subsequently combined into a true 3D deformation field. The method is evaluated with the data and ground truth provided by the Cardiac Motion Analysis Challenge at STACOM 2012.

A novel software assistant for the clinical analysis of MR spectroscopy with MeVisLab

We present a novel software assistant for the analysis of multi-voxel 2D or 3D in-vivo-spectroscopy signals based on the rapid-prototyping platform MeVisLab. Magnetic Resonance Spectroscopy (MRS) is a valuable in-vivo metabolic window into tissue regions of interest, such as the brain, breast or prostate. With this method, the metabolic state can be investigated non-invasively. Different pathologies evoke characteristically different MRS signals, e.g., in prostate cancer, choline levels increase while citrate levels decrease compared to benign tissue. Concerning the majority of processing steps, available MRS tools lack performance in terms of speed. Our goal is to support clinicians in a fast and robust interpretation of MRS signals and to enable them to interactively work with large volumetric data sets. These data sets consist of 3D spatially resolved measurements of metabolite signals. The software assistant provides standard analysis methods for MRS data including data import and filtering, spatio-temporal Fourier transformation, and basic calculation of peak areas and spectroscopic metabolic maps. Visualization relies on the facilities of MeVisLab, a platform for developing clinically applicable software assistants. It is augmented by special-purpose viewing extensions and offers synchronized 1D, 2D, and 3D views of spectra and metabolic maps. A novelty in MRS processing tools is the side-by-side viewing ability of standard FT processed spectra with the results of time-domain frequency analysis algorithms like Linear Prediction and the Matrix Pencil Method. This enables research into the optimal toolset and workflow required to avoid misinterpretation and misapplication.