Multi-Scale Modeling for Image Analysis of Brain Tumor Studies

Image-based modeling of tumor growth combines methods from cancer simulation and medical imaging. In this context, we present a novel approach to adapt a healthy brain atlas to MR images of tumor patients. In order to establish correspondence between a healthy atlas and a pathologic patient image, tumor growth modeling in combination with registration algorithms is employed. In a first step, the tumor is grown in the atlas based on a new multi-scale, multi-physics model including growth simulation from the cellular level up to the biomechanical level, accounting for cell proliferation and tissue deformations. Large-scale deformations are handled with an Eulerian approach for finite element computations, which can operate directly on the image voxel mesh. Subsequently, dense correspondence between the modified atlas and patient image is established using nonrigid registration. The method offers opportunities in atlasbased segmentation of tumor-bearing brain images as well as for improved patient-specific simulation and prognosis of tumor progression.

Automatic multi-resolution shape modeling of multi-organ structures.

Point Distribution Models (PDM) are among the most popular shape description techniques and their usefulness has been demonstrated in a wide variety of medical imaging applications. However, to adequately characterize the underlying modeled population it is essential to have a representative number of training samples, which is not always possible. This problem is especially relevant as the complexity of the modeled structure increases, being the modeling of ensembles of multiple 3D organs one of the most challenging cases. In this paper, we introduce a new GEneralized Multi-resolution PDM (GEM-PDM) in the context of multi-organ analysis able to efficiently characterize the different inter-object relations, as well as the particular locality of each object separately. Importantly, unlike previous approaches, the configuration of the algorithm is automated thanks to a new agglomerative landmark clustering method proposed here, which equally allows us to identify smaller anatomically significant regions within organs. The significant advantage of the GEM-PDM method over two previous approaches (PDM and hierarchical PDM) in terms of shape modeling accuracy and robustness to noise, has been successfully verified for two different databases of sets of multiple organs: six subcortical brain structures, and seven abdominal organs. Finally, we propose the integration of the new shape modeling framework into an active shape-model-based segmentation algorithm. The resulting algorithm, named GEMA, provides a better overall performance than the two classical approaches tested, ASM, and hierarchical ASM, when applied to the segmentation of 3D brain MRI.

Hardware accelerated ray cast of volume data and volume gradient for an optimized splines-based multi-resolution 2D-3D registration

This paper describes a method for DRR generation as well as for volume gradients projection using hardware accelerated 2D texture mapping and accumulation buffering and demonstrates its application in 2D-3D registration of X-ray fluoroscopy to CT images. The robustness of the present registration scheme are guaranteed by taking advantage of a coarse-to-fine processing of the volume/image pyramids based on cubic B-splines. A human cadaveric spine specimen together with its ground truth was used to compare the present scheme with a purely software-based scheme in three aspects: accuracy, speed, and capture ranges. Our experiments revealed an equivalent accuracy and capture ranges but with much shorter registration time with the present scheme. More specifically, the results showed 0.8 mm average target registration error, 55 second average execution time per registration, and 10 mm and 10° capture ranges for the present scheme when tested on a 3.0 GHz Pentium 4 computer.

Robust Proximal Femur Segmentation in Conventional X-Ray Images via Random Forest Regression on Multi-resolution Gradient Features

In this paper, we propose a fully automatic, robust approach for segmenting proximal femur in conventional X-ray images. Our method is based on hierarchical landmark detection by random forest regression, where the detection results of 22 global landmarks are used to do the spatial normalization, and the detection results of the 59 local landmarks serve as the image cue for instantiation of a statistical shape model of the proximal femur. To detect landmarks in both levels, we use multi-resolution HoG (Histogram of Oriented Gradients) as features which can achieve better accuracy and robustness. The efficacy of the present method is demonstrated by experiments conducted on 150 clinical x-ray images. It was found that the present method could achieve an average point-to-curve error of 2.0 mm and that the present method was robust to low image contrast, noise and occlusions caused by implants.

A survey of MRI-based medical image analysis for brain tumor studies

MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.

Critical issues in managing protected areas by multi-stakeholder participation - analysis of a process in the Swiss Alps

Balancing the frequently conflicting priorities of conservation and economic development poses a challenge to management of the Swiss Alps Jungfrau-Aletsch World Heritage Site (WHS). This is a complex societal problem that calls for a knowledge-based solution. This in turn requires a transdisciplinary research framework in which problems are defined and solved cooperatively by actors from the scientific community and the life-world. In this article we re-examine studies carried out in the region of the Swiss Alps Jungfrau-Aletsch WHS, covering three key issues prevalent in transdisciplinary settings: integration of stakeholders into participatory processes; perceptions and positions; and negotiability and implementation. In the case of the Swiss Alps Jungfrau-Aletsch WHS the transdisciplinary setting created a situation of mutual learning among stakeholders from different levels and backgrounds. However, the studies showed that the benefits of such processes of mutual learning are continuously at risk of being diminished by the power play inherent in participatory approaches.

Multi-locus microsatellite analysis supports the hypothesis of an autochthonous focus of Echinococcus multilocularis in northern Italy

Echinococcus multilocularis is characterised by a wide geographical distribution, encompassing three continents (North America, Asia and Europe) yet very low genetic variability is documented. Recently, this parasite has been detected in red foxes (Vulpes vulpes) circulating in an Alpine region of Italy, close to Austria. This finding raised the question as to whether an autochthonous cycle exists in Italy or whether the infected foxes originated from the neighbouring regions of Austria. Studies have shown that multi-locus microsatellite analysis can identify genomic regions carrying mutations that result in a local adaptation. We used a tandem repeated multi-locus microsatellite (EmsB) to evaluate the genetic differences amongst adult worms of E. multilocularis collected in Italy, worms from neighbouring Austria and from other European and extra-European countries. Fluorescent PCR was performed on a panel of E. multilocularis samples to assess intra-specific polymorphism. The analysis revealed four closed genotypes for Italian samples of E. multilocularis which were unique compared with the other 25 genotypes from Europe and the five genotypes from Alaska. An analysis in the Alpine watershed, comparing Italian adult worms with those from neighbouring areas in Austria, showed a unique cluster for Italian samples. This result supports the hypothesis of the presence of an autochthonous cycle of E. multilocularis in Italy. EmsB can be useful for 'tracking' the source of infection of this zoonotic parasite and developing appropriate measures for preventing or reducing the risk of human alveolar echinococcosis.

A multi-center retrospective analysis of treatment effects and quality of life in adult patients with cranial ependymomas

Long term quality of life data of adult patients harboring intracranial ependymomas have not been reported. The role of adjuvant radiation therapy in Grade II ependymomas is unclear and differs from study to study. We therefore sought to retrospectively analyze outcome and quality of life of adult patients that were operated on intracranial ependymomas at four different surgical centers in two countries. All patients were attempted to be contacted via telephone to assess quality of life (QoL) at the time of the telephone interview. The standard EORTC QoL Questionnaire C30 (EORTC QLQ-C30) and the EORTC QLQ-Brain Cancer Module (QLQ-BN20) were used. 64 adult patients with intracranial ependymomas were included in the study. The only factor that was associated with increased survival was age <55 years (p < 0.001). Supratentorial location was correlated with shorter progression free survival than infratentorial location (PFS; p = 0.048). In WHO Grade II tumors local irradiation did not lead to increased PFS (p = 0.888) or overall survival (p = 0.801). Even for incompletely resected Grade II tumors local irradiation did not lead to a benefit in PFS (p = 0.911). In a multivariate analysis of QoL, irradiated patients had significantly worse scores in the item "fatigue" (p = 0.037) than non-irradiated patients. Here we present QoL data of adult patients with intracranial ependymomas. Our data show that local radiation therapy may have long-term effects on patients' QoL. Since in the incompletely resected Grade II tumors local irradiation did not lead to a benefit in PFS in this retrospective study, prospective randomized studies are necessary. In addition to age, supratentorial tumor location is associated with a worse prognosis in adult ependymoma patients.

A new approach to detect structural differences in lung parenchyma using digital image analysis

Morphometric investigations using a point and intersection counting strategy in the lung often are not able to reveal the full set of morphologic changes. This happens particularly when structural modifications are not expressed in terms of volume density changes and when rough and fine surface density alterations cancel each other at different magnifications. Making use of digital image processing, we present a methodological approach that allows to easily and quickly quantify changes of the geometrical properties of the parenchymal lung structure and reflects closely the visual appreciation of the changes. Randomly sampled digital images from light microscopic sections of lung parenchyma are filtered, binarized, and skeletonized. The lung septa are thus represented as a single-pixel wide line network with nodal points and end points and the corresponding internodal and end segments. By automatically counting the number of points and measuring the lengths of the skeletal segments, the lung architecture can be characterized and very subtle structural changes can be detected. This new methodological approach to lung structure analysis is highly sensitive to morphological changes in the parenchyma: it detected highly significant quantitative alterations in the structure of lungs of rats treated with a glucocorticoid hormone, where the classical morphometry had partly failed.