Jan Rexilius

Multispectral brain tumor segmentation based on histogram model adaptation

Brain tumor segmentation and quantification from MR images is a challenging task. The boundary of a tumor and its volume are important parameters that can have direct impact on surgical treatment, radiation therapy, or on quantitative measurements of tumor regression rates. Although a wide range of different methods has already been proposed, a commonly accepted approach is not yet established. Today, the gold standard at many institutions still consists of a manual tumor outlining, which is potentially subjective, and a time consuming and tedious process. We propose a new method that allows for fast multispectral segmentation of brain tumors. An efficient initialization of the segmentation is obtained using a novel probabilistic intensity model, followed by an iterative refinement of the initial segmentation. A progressive region growing that combines probability and distance information provides a new, flexible tumor segmentation. In order to derive a robust model for brain tumors that can be easily applied to a new dataset, we retain information not on the anatomical, but on the global cross-subject intensity variability. Therefore, a set of multispectral histograms from different patient datasets is registered onto a reference histogram using global affine and non-rigid registration methods. The probability model is then generated from manual expert segmentations that are transferred to the histogram feature domain. A forward and backward transformation of a manual segmentation between histogram and image domain allows for a statistical analysis of the accuracy and robustness of the selected features. Experiments are carried out on patient datasets with different tumor shapes, sizes, locations, and internal texture.

Evaluation of accuracy in partial volume analysis of small objects

Accurate and robust assessment of quantitative parameters is a key issue in many fields of medical image analysis, and can have a direct impact on diagnosis and treatment monitoring. Especially for the analysis of small structures such as focal lesions in patients with MS, the finite spatial resolution of imaging devices is often a limiting factor that results in a mixture of different tissue types. We propose a new method that allows an accurate quantification of medical image data, focusing on a dedicated model for partial volume (PV) artifacts. Today, a widely accepted model assumption is that of a uniformly distributed linear mixture of pure tissues. However, several publications have clearly shown that this is not an appropriate choice in many cases. We propose a generalization of current PV models based on the Beta distribution, yielding a more accurate quantification. Furthermore, we present a new classification scheme. Prior knowledge obtained from a set of training data allows a robust initial estimate of the proper model parameters, even in cases of objects with predominant PV artifacts. A maximum likelihood based clustering algorithm is employed, resulting in a robust volume estimate. Experiments are carried out on more than 100 stylized software phantoms as well as on realistic phantom data sets. A comparison with current mixture models shows the capabilities of our approach.

Rapid Prototyping of Clinical Software Assistants

Computer assistance in image-based diagnosis and therapy are continuously growing fields that have gained importance in several medical disciplines. Today, various free and commercial tools are available. However, only few are routinely applied in clinical practice. Especially tools that provide a flsupport of the whole design process from development and evaluation to the actual deployment in a clinical environment are missing. In this work, we introduce a categorization of the design process into different types and fields of application. To this end, we propose a novel framework that allows the development of software assistants that can be integrated into the design process of new algorithms and systems. We focus on the specific features of software prototypes that are valuable for engineers and clinicians, rather than on product development. An important aspect in this work is the categorization of the software design process into different components. Furthermore, we examine the interaction between these categories based on a new knowledge flow model. Finally, an encapsulation of these tasks within an application framework is proposed. We discuss general requirements and present a layered architecture. Several components for data- and workflow-management provide a generic functionality that can be customized on the developer and the user level. A flexible handling of is offered through the use of a visual programming and rapid prototyping platform. Currently, the framework is used in 15 software prototypes and as a basis of commercial products. More than 90 clinical partners all over the world work with these tools.

A software assistant for the design of realistic software phantoms

Segmentation and quantification of medical image data are difficult problems in image analysis. Especially, an accurate and robust assessment of quantitative parameters is a key issue in many fields, and can have a direct impact on diagnosis and treatment planning. To this end, physical and software phantom data sets have become an integral tool during the design, implementation, and optimization of new algorithms. Unfortunately, a common research resource has not been established until now for many applications. In this work we propose a software assistant for the development of realistic software phantoms. Our aim is an easy-to-use tool with an intuitive user interface. Furthermore, we provide a software for researchers including a common basis of reference data, which facilitates a standardized and objective validation of performance and limitations of own developments, as well as the comparison of different methods. The fundamental phantom design focuses on objects that can be incorporated into a given background. This can either be a homogeneous artificial background, or a volunteer or patient data set. For each phantom the exact ground truth of the investigated object is available, which provides us with an excellent tool for the generation of realistic data sets. Several experiments are carried out for a number of different applications including software phantoms of small, hyperintense brain lesions, as well as software phantoms of liver metastases.