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.

Novel autoantigens immunogenic in COPD patients

Background Chronic obstructive pulmonary disease (COPD) is a respiratory inflammatory condition with autoimmune features including IgG autoantibodies. In this study we analyze the complexity of the autoantibody response and reveal the nature of the antigens that are recognized by autoantibodies in COPD patients. Methods An array of 1827 gridded immunogenic peptide clones was established and screened with 17 sera of COPD patients and 60 healthy controls. Protein arrays were evaluated both by visual inspection and a recently developed computer aided image analysis technique. By this computer aided image analysis technique we computed the intensity values for each peptide clone and each serum and calculated the area under the receiver operator characteristics curve (AUC) for each clone and the separation COPD sera versus control sera. Results By visual evaluation we detected 381 peptide clones that reacted with autoantibodies of COPD patients including 17 clones that reacted with more than 60% of the COPD sera and seven clones that reacted with more than 90% of the COPD sera. The comparison of COPD sera and controls by the automated image analysis system identified 212 peptide clones with informative AUC values. By in silico sequence analysis we found an enrichment of sequence motives previously associated with immunogenicity. Conclusion The identification of a rather complex humoral immune response in COPD patients supports the idea of COPD as a disease with strong autoimmune features. The identification of novel immunogenic antigens is a first step towards a better understanding of the autoimmune component of COPD.

Computer-aided diagnosis of carotid atherosclerosis based on ultrasound image statistics, laws' texture and neural networks

Quantitative characterisation of carotid atherosclerosis and classification into symptomatic or asymptomatic is crucial in planning optimal treatment of atheromatous plaque. The computer-aided diagnosis (CAD) system described in this paper can analyse ultrasound (US) images of carotid artery and classify them into symptomatic or asymptomatic based on their echogenicity characteristics. The CAD system consists of three modules: a) the feature extraction module, where first-order statistical (FOS) features and Laws' texture energy can be estimated, b) the dimensionality reduction module, where the number of features can be reduced using analysis of variance (ANOVA), and c) the classifier module consisting of a neural network (NN) trained by a novel hybrid method based on genetic algorithms (GAs) along with the back propagation algorithm. The hybrid method is able to select the most robust features, to adjust automatically the NN architecture and to optimise the classification performance. The performance is measured by the accuracy, sensitivity, specificity and the area under the receiver-operating characteristic (ROC) curve. The CAD design and development is based on images from 54 symptomatic and 54 asymptomatic plaques. This study demonstrates the ability of a CAD system based on US image analysis and a hybrid trained NN to identify atheromatous plaques at high risk of stroke.

Mitotic figure counts are significantly overestimated in resection specimens of invasive breast carcinomas

Several authors have demonstrated an increased number of mitotic figures in breast cancer resection specimen when compared with biopsy material. This has been ascribed to a sampling artifact where biopsies are (i) either too small to allow formal mitotic figure counting or (ii) not necessarily taken form the proliferating tumor periphery. Herein, we propose a different explanation for this phenomenon. Biopsy and resection material of 52 invasive ductal carcinomas was studied. We counted mitotic figures in 10 representative high power fields and quantified MIB-1 immunohistochemistry by visual estimation, counting and image analysis. We found that mitotic figures were elevated by more than three-fold on average in resection specimen over biopsy material from the same tumors (20±6 vs 6±2 mitoses per 10 high power fields, P=0.008), and that this resulted in a relative diminution of post-metaphase figures (anaphase/telophase), which made up 7% of all mitotic figures in biopsies but only 3% in resection specimen (P<0.005). At the same time, the percentages of MIB-1 immunostained tumor cells among total tumor cells were comparable in biopsy and resection material, irrespective of the mode of MIB-1 quantification. Finally, we found no association between the size of the biopsy material and the relative increase of mitotic figures in resection specimen. We propose that the increase in mitotic figures in resection specimen and the significant shift towards metaphase figures is not due to a sampling artifact, but reflects ongoing cell cycle activity in the resected tumor tissue due to fixation delay. The dwindling energy supply will eventually arrest tumor cells in metaphase, where they are readily identified by the diagnostic pathologist. Taken together, we suggest that the rapidly fixed biopsy material better represents true tumor biology and should be privileged as predictive marker of putative response to cytotoxic chemotherapy.

Combined effect of heterogeneity, anisotropy and saturation on steady state flow and transport: Structure recognition and numerical simulation

1 Natural soil profiles may be interpreted as an arrangement of parts which are characterized by properties like hydraulic conductivity and water retention function. These parts form a complicated structure. Characterizing the soil structure is fundamental in subsurface hydrology because it has a crucial influence on flow and transport and defines the patterns of many ecological processes. We applied an image analysis method for recognition and classification of visual soil attributes in order to model flow and transport through a man-made soil profile. Modeled and measured saturation-dependent effective parameters were compared. We found that characterizing and describing conductivity patterns in soils with sharp conductivity contrasts is feasible. Differently, solving flow and transport on the basis of these conductivity maps is difficult and, in general, requires special care for representation of small-scale processes.

Multi-modal glioblastoma segmentation: man versus machine

BACKGROUND AND PURPOSE Reproducible segmentation of brain tumors on magnetic resonance images is an important clinical need. This study was designed to evaluate the reliability of a novel fully automated segmentation tool for brain tumor image analysis in comparison to manually defined tumor segmentations. METHODS We prospectively evaluated preoperative MR Images from 25 glioblastoma patients. Two independent expert raters performed manual segmentations. Automatic segmentations were performed using the Brain Tumor Image Analysis software (BraTumIA). In order to study the different tumor compartments, the complete tumor volume TV (enhancing part plus non-enhancing part plus necrotic core of the tumor), the TV+ (TV plus edema) and the contrast enhancing tumor volume CETV were identified. We quantified the overlap between manual and automated segmentation by calculation of diameter measurements as well as the Dice coefficients, the positive predictive values, sensitivity, relative volume error and absolute volume error. RESULTS Comparison of automated versus manual extraction of 2-dimensional diameter measurements showed no significant difference (p = 0.29). Comparison of automated versus manual segmentation of volumetric segmentations showed significant differences for TV+ and TV (p<0.05) but no significant differences for CETV (p>0.05) with regard to the Dice overlap coefficients. Spearman's rank correlation coefficients (ρ) of TV+, TV and CETV showed highly significant correlations between automatic and manual segmentations. Tumor localization did not influence the accuracy of segmentation. CONCLUSIONS In summary, we demonstrated that BraTumIA supports radiologists and clinicians by providing accurate measures of cross-sectional diameter-based tumor extensions. The automated volume measurements were comparable to manual tumor delineation for CETV tumor volumes, and outperformed inter-rater variability for overlap and sensitivity.

A computer-assisted microscopic analysis of bone tissue developed inside a polyactive plymer implanted into an equine articular surface

One of the most promising applications for the restoration of small or moderately sized focal articular lesions is mosaicplasty (MP). Although recurrent hemarthrosis is a rare complication after MP, recently, various strategies have been designed to find an effective filling material to prevent postoperative bleeding from the donor site. The porous biodegradable polymer Polyactive (PA; a polyethylene glycol terephthalate - polybutylene terephthalate copolymer) represents a promising solution in this respect. A histological evaluation of the longterm PA-filled donor sites obtained from 10 experimental horses was performed. In this study, attention was primarily focused on the bone tissue developed in the plug. A computer-assisted image analysis and quantitative polarized light microscopic measurements of decalcified, longitudinally sectioned, dimethylmethylene blue (DMMB)- and picrosirius red (PS) stained sections revealed that the coverage area of the bone trabecules in the PA-filled donor tunnels was substantially (25%) enlarged compared to the neighboring cancellous bone. For this quantification, identical ROIs (regions of interest) were used and compared. The birefringence retardation values were also measured with a polarized light microscope using monochromatic light. Identical retardation values could be recorded from the bone trabeculae developed in the PA and in the neighboring bone, which indicates that the collagen orientation pattern does not differ significantly among these bone trabecules. Based on our new data, we speculate that PA promotes bone formation, and some of the currently identified degradation products of PA may enhance osteo-conduction and osteoinduction inside the donor canal.

Reconstruction of 3D Vertebral Models from a Single 2D Lateral Fluoroscopic Image

Accurate three-dimensional (3D) models of lumbar vertebrae are required for image-based 3D kinematics analysis. MRI or CT datasets are frequently used to derive 3D models but have the disadvantages that they are expensive, time-consuming or involving ionizing radiation (e.g., CT acquisition). In this chapter, we present an alternative technique that can reconstruct a scaled 3D lumbar vertebral model from a single two-dimensional (2D) lateral fluoroscopic image and a statistical shape model. Cadaveric studies are conducted to verify the reconstruction accuracy by comparing the surface models reconstructed from a single lateral fluoroscopic image to the ground truth data from 3D CT segmentation. A mean reconstruction error between 0.7 and 1.4 mm was found.

Graphical Model-Based Vertebra Identification from X-Ray Image(s)

Automated identification of vertebrae from X-ray image(s) is an important step for various medical image computing tasks such as 2D/3D rigid and non-rigid registration. In this chapter we present a graphical model-based solution for automated vertebra identification from X-ray image(s). Our solution does not ask for a training process using training data and has the capability to automatically determine the number of vertebrae visible in the image(s). This is achieved by combining a graphical model-based maximum a posterior probability (MAP) estimate with a mean-shift based clustering. Experiments conducted on simulated X-ray images as well as on a low-dose low quality X-ray spinal image of a scoliotic patient verified its performance.

Automatic X-ray landmark detection and shape segmentation via data-driven joint estimation of image displacements

In this paper, we propose a new method for fully-automatic landmark detection and shape segmentation in X-ray images. To detect landmarks, we estimate the displacements from some randomly sampled image patches to the (unknown) landmark positions, and then we integrate these predictions via a voting scheme. Our key contribution is a new algorithm for estimating these displacements. Different from other methods where each image patch independently predicts its displacement, we jointly estimate the displacements from all patches together in a data driven way, by considering not only the training data but also geometric constraints on the test image. The displacements estimation is formulated as a convex optimization problem that can be solved efficiently. Finally, we use the sparse shape composition model as the a priori information to regularize the landmark positions and thus generate the segmented shape contour. We validate our method on X-ray image datasets of three different anatomical structures: complete femur, proximal femur and pelvis. Experiments show that our method is accurate and robust in landmark detection, and, combined with the shape model, gives a better or comparable performance in shape segmentation compared to state-of-the art methods. Finally, a preliminary study using CT data shows the extensibility of our method to 3D data.

Quantitative microstructure analysis of polymer-modified mortars

Digital light, fluorescence and electron microscopy in combination with wavelength-dispersive spectroscopy were used to visualize individual polymers, air voids, cement phases and filler minerals in a polymer-modified cementitious tile adhesive. In order to investigate the evolution and processes involved in formation of the mortar microstructure, quantifications of the phase distribution in the mortar were performed including phase-specific imaging and digital image analysis. The required sample preparation techniques and imaging related topics are discussed. As a form of case study, the different techniques were applied to obtain a quantitative characterization of a specific mortar mixture. The results indicate that the mortar fractionates during different stages ranging from the early fresh mortar until the final hardened mortar stage. This induces process-dependent enrichments of the phases at specific locations in the mortar. The approach presented provides important information for a comprehensive understanding of the functionality of polymer-modified mortars.

Comprehension of Co-Speech Gestures in Aphasic Patients: An Eye Movement Study.

BACKGROUND: Co-speech gestures are omnipresent and a crucial element of human interaction by facilitating language comprehension. However, it is unclear whether gestures also support language comprehension in aphasic patients. Using visual exploration behavior analysis, the present study aimed to investigate the influence of congruence between speech and co-speech gestures on comprehension in terms of accuracy in a decision task. METHOD: Twenty aphasic patients and 30 healthy controls watched videos in which speech was either combined with meaningless (baseline condition), congruent, or incongruent gestures. Comprehension was assessed with a decision task, while remote eye-tracking allowed analysis of visual exploration. RESULTS: In aphasic patients, the incongruent condition resulted in a significant decrease of accuracy, while the congruent condition led to a significant increase in accuracy compared to baseline accuracy. In the control group, the incongruent condition resulted in a decrease in accuracy, while the congruent condition did not significantly increase the accuracy. Visual exploration analysis showed that patients fixated significantly less on the face and tended to fixate more on the gesturing hands compared to controls. CONCLUSION: Co-speech gestures play an important role for aphasic patients as they modulate comprehension. Incongruent gestures evoke significant interference and deteriorate patients' comprehension. In contrast, congruent gestures enhance comprehension in aphasic patients, which might be valuable for clinical and therapeutic purposes.