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.

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.

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.

Glucocorticoid induced impairment of lung structure assessed by digital image analysis

Glucocorticoids (GC) are successfully applied in neonatology to improve lung maturation in preterm born babies. Animal studies show that GC can also impair lung development. In this investigation, we used a new approach based on digital image analysis. Microscopic images of lung parenchyma were skeletonised and the geometrical properties of the septal network characterised by analysing the 'skeletal' parameters. Inhibition of the process of alveolarisation after extensive administration of small doses of GC in newborn rats was confirmed by significant changes in the 'skeletal' parameters. The induced structural changes in the lung parenchyma were still present after 60 days in adult rats, clearly indicating a long lasting or even definitive impairment of lung development and maturation caused by GC. Conclusion: digital image analysis and skeletonisation proved to be a highly suited approach to assess structural changes in lung parenchyma.

Mapping material distribution in a heterogeneous sand tank by image analysis

Water flow and solute transport through soils are strongly influenced by the spatial arrangement of soil materials with different hydraulic and chemical properties. Knowing the specific or statistical arrangement of these materials is considered as a key toward improved predictions of solute transport. Our aim was to obtain two-dimensional material maps from photographs of exposed profiles. We developed a segmentation and classification procedure and applied it to the images of a very heterogeneous sand tank, which was used for a series of flow and transport experiments. The segmentation was based on thresholds of soil color, estimated from local median gray values, and of soil texture, estimated from local coefficients of variation of gray values. Important steps were the correction of inhomogeneous illumination and reflection, and the incorporation of prior knowledge in filters used to extract the image features and to smooth the results morphologically. We could check and confirm the success of our mapping by comparing the estimated with the designed sand distribution in the tank. The resulting material map was used later as input to model flow and transport through the sand tank. Similar segmentation procedures may be applied to any high-density raster data, including photographs or spectral scans of field profiles.

Image analysis of immunohistochemistry is superior to visual scoring as shown for patient outcome of esophageal adenocarcinoma.

Quantification of protein expression based on immunohistochemistry (IHC) is an important step in clinical diagnoses and translational tissue-based research. Manual scoring systems are used in order to evaluate protein expression based on staining intensities and distribution patterns. However, visual scoring remains an inherently subjective approach. The aim of our study was to explore whether digital image analysis proves to be an alternative or even superior tool to quantify expression of membrane-bound proteins. We analyzed five membrane-binding biomarkers (HER2, EGFR, pEGFR, β-catenin, and E-cadherin) and performed IHC on tumor tissue microarrays from 153 esophageal adenocarcinomas patients from a single center study. The tissue cores were scored visually applying an established routine scoring system as well as by using digital image analysis obtaining a continuous spectrum of average staining intensity. Subsequently, we compared both assessments by survival analysis as an end point. There were no significant correlations with patient survival using visual scoring of β-catenin, E-cadherin, pEGFR, or HER2. In contrast, the results for digital image analysis approach indicated that there were significant associations with disease-free survival for β-catenin, E-cadherin, pEGFR, and HER2 (P = 0.0125, P = 0.0014, P = 0.0299, and P = 0.0096, respectively). For EGFR, there was a greater association with patient survival when digital image analysis was used compared to when visual scoring was (visual: P = 0.0045, image analysis: P < 0.0001). The results of this study indicated that digital image analysis was superior to visual scoring. Digital image analysis is more sensitive and, therefore, better able to detect biological differences within the tissues with greater accuracy. This increased sensitivity improves the quality of quantification.

Aortic morphometry at endograft position as assessed by 3D image analysis affects risk of type I endoleak formation after TEVAR

PURPOSE The purpose of this study was to identify morphologic factors affecting type I endoleak formation and bird-beak configuration after thoracic endovascular aortic repair (TEVAR). METHODS Computed tomography (CT) data of 57 patients (40 males; median age, 66 years) undergoing TEVAR for thoracic aortic aneurysm (34 TAA, 19 TAAA) or penetrating aortic ulcer (n = 4) between 2001 and 2010 were retrospectively reviewed. In 28 patients, the Gore TAG® stent-graft was used, followed by the Medtronic Valiant® in 16 cases, the Medtronic Talent® in 8, and the Cook Zenith® in 5 cases. Proximal landing zone (PLZ) was in zone 1 in 13, zone 2 in 13, zone 3 in 23, and zone 4 in 8 patients. In 14 patients (25%), the procedure was urgent or emergent. In each case, pre- and postoperative CT angiography was analyzed using a dedicated image processing workstation and complimentary in-house developed software based on a 3D cylindrical intensity model to calculate aortic arch angulation and conicity of the landing zones (LZ). RESULTS Primary type Ia endoleak rate was 12% (7/57) and subsequent re-intervention rate was 86% (6/7). Left subclavian artery (LSA) coverage (p = 0.036) and conicity of the PLZ (5.9 vs. 2.6 mm; p = 0.016) were significantly associated with an increased type Ia endoleak rate. Bird-beak configuration was observed in 16 patients (28%) and was associated with a smaller radius of the aortic arch curvature (42 vs. 65 mm; p = 0.049). Type Ia endoleak was not associated with a bird-beak configuration (p = 0.388). Primary type Ib endoleak rate was 7% (4/57) and subsequent re-intervention rate was 100%. Conicity of the distal LZ was associated with an increased type Ib endoleak rate (8.3 vs. 2.6 mm; p = 0.038). CONCLUSIONS CT-based 3D aortic morphometry helps to identify risk factors of type I endoleak formation and bird-beak configuration during TEVAR. These factors were LSA coverage and conicity within the landing zones for type I endoleak formation and steep aortic angulation for bird-beak configuration.

A new technique to automatically quantify microstructures of fine grained carbonate mylonites: two-step etching combined with SEM imaging and image analysis

A two-step etching technique for fine-grained calcite mylonites using 0.37% hydrochloric and 0.1% acetic acid produces a topographic relief which reflects the grain boundary geometry. With this technique, calcite grain boundaries become more intensely dissolved than their grain interiors but second phase minerals like dolomite, quartz, feldspars, apatite, hematite and pyrite are not affected by the acid and therefore form topographic peaks. Based on digital backscatter electron images and element distribution maps acquired on a scanning electron microscope, the geometry of calcite and the second phase minerals can be automatically quantified using image analysis software. For research on fine-grained carbonate rocks (e.g. dolomite calcite mixtures), this low-cost approach is an attractive alternative to the generation of manual grain boundary maps based on photographs from ultra-thin sections or orientation contrast images.

Impact of optic media opacities and image compression on quantitative analysis of optical coherence tomography

To analyze the impact of opacities in the optical pathway and image compression of 32-bit raw data to 8-bit jpg images on quantified optical coherence tomography (OCT) image analysis.

Mesh-based vs. Image-based Statistical Appearance Model of the Human Femur: a Preliminary Comparison Study for the Creation of Finite Element Meshes

Statistical models have been recently introduced in computational orthopaedics to investigate the bone mechanical properties across several populations. A fundamental aspect for the construction of statistical models concerns the establishment of accurate anatomical correspondences among the objects of the training dataset. Various methods have been proposed to solve this problem such as mesh morphing or image registration algorithms. The objective of this study is to compare a mesh-based and an image-based statistical appearance model approaches for the creation of nite element(FE) meshes. A computer tomography (CT) dataset of 157 human left femurs was used for the comparison. For each approach, 30 finite element meshes were generated with the models. The quality of the obtained FE meshes was evaluated in terms of volume, size and shape of the elements. Results showed that the quality of the meshes obtained with the image-based approach was higher than the quality of the mesh-based approach. Future studies are required to evaluate the impact of this finding on the final mechanical simulations.

Geometry-Aware Multiscale Image Registration via OBBTree-Based Polyaffine Log-Demons

Non-linear image registration is an important tool in many areas of image analysis. For instance, in morphometric studies of a population of brains, free-form deformations between images are analyzed to describe the structural anatomical variability. Such a simple deformation model is justified by the absence of an easy expressible prior about the shape changes. Applying the same algorithms used in brain imaging to orthopedic images might not be optimal due to the difference in the underlying prior on the inter-subject deformations. In particular, using an un-informed deformation prior often leads to local minima far from the expected solution. To improve robustness and promote anatomically meaningful deformations, we propose a locally affine and geometry-aware registration algorithm that automatically adapts to the data. We build upon the log-domain demons algorithm and introduce a new type of OBBTree-based regularization in the registration with a natural multiscale structure. The regularization model is composed of a hierarchy of locally affine transformations via their logarithms. Experiments on mandibles show improved accuracy and robustness when used to initialize the demons, and even similar performance by direct comparison to the demons, with a significantly lower degree of freedom. This closes the gap between polyaffine and non-rigid registration and opens new ways to statistically analyze the registration results.