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.

Brain areas involved in medial temporal lobe seizures: a principal component analysis of ictal SPECT data

The study describes brain areas involved in medial temporal lobe (mTL) seizures of 12 patients. All patients showed so-called oro-alimentary behavior within the first 20 s of clinical seizure manifestation characteristic of mTL seizures. Single photon emission computed tomography (SPECT) images of regional cerebral blood flow (rCBF) were acquired from the patients in ictal and interictal phases and from normal volunteers. Image analysis employed categorical comparisons with statistical parametric mapping and principal component analysis (PCA) to assess functional connectivity. PCA supplemented the findings of the categorical analysis by decomposing the covariance matrix containing images of patients and healthy subjects into distinct component images of independent variance, including areas not identified by the categorical analysis. Two principal components (PCs) discriminated the subject groups: patients with right or left mTL seizures and normal volunteers, indicating distinct neuronal networks implicated by the seizure. Both PCs were correlated with seizure duration, one positively and the other negatively, confirming their physiological significance. The independence of the two PCs yielded a clear clustering of subject groups. The local pattern within the temporal lobe describes critical relay nodes which are the counterpart of oro-alimentary behavior: (1) right mesial temporal zone and ipsilateral anterior insula in right mTL seizures, and (2) temporal poles on both sides that are densely interconnected by the anterior commissure. Regions remote from the temporal lobe may be related to seizure propagation and include positively and negatively loaded areas. These patterns, the covarying areas of the temporal pole and occipito-basal visual association cortices, for example, are related to known anatomic paths.

An introduction to low-level analysis methods of DNA microarray data

This article gives an overview over the methods used in the low--level analysis of gene expression data generated using DNA microarrays. This type of experiment allows to determine relative levels of nucleic acid abundance in a set of tissues or cell populations for thousands of transcripts or loci simultaneously. Careful statistical design and analysis are essential to improve the efficiency and reliability of microarray experiments throughout the data acquisition and analysis process. This includes the design of probes, the experimental design, the image analysis of microarray scanned images, the normalization of fluorescence intensities, the assessment of the quality of microarray data and incorporation of quality information in subsequent analyses, the combination of information across arrays and across sets of experiments, the discovery and recognition of patterns in expression at the single gene and multiple gene levels, and the assessment of significance of these findings, considering the fact that there is a lot of noise and thus random features in the data. For all of these components, access to a flexible and efficient statistical computing environment is an essential aspect.

Quantitative analysis of OCT characteristics in patients with achromatopsia and blue-cone monochromatism

PURPOSE: To quantify optical coherence tomography (OCT) images of the central retina in patients with blue-cone monochromatism (BCM) and achromatopsia (ACH) compared with healthy control individuals. METHODS: The study included 15 patients with ACH, 6 with BCM, and 20 control subjects. Diagnosis of BCM and ACH was established by visual acuity testing, morphologic examination, color vision testing, and Ganzfeld ERG recording. OCT images were acquired with the Stratus OCT 3 (Carl Zeiss Meditec AG, Oberkochen, Germany). Foveal OCT images were analyzed by calculating longitudinal reflectivity profiles (LRPs) from scan lines. Profiles were analyzed quantitatively to determine foveal thickness and distances between reflectivity layers. RESULTS: Patients with ACH and BCM had a mean visual acuity of 20/200 and 20/60, respectively. Color vision testing results were characteristic of the diseases. The LRPs of control subjects yielded four peaks (P1-P4), presumably representing the RPE (P1), the ovoid region of the photoreceptors (P2), the external limiting membrane (ELM) (P3), and the internal limiting membrane (P4). In patients with ACH, P2 was absent, but foveal thickness (P1-P4) did not differ significantly from that in the control subjects (187 +/- 20 vs. 192 +/- 14 microm, respectively). The distance from P1 to P3 did not differ significantly (78 +/- 10 vs. 82 +/- 5 microm) between ACH and controls subjects. In patients with BCM, P3 was lacking, and P2 advanced toward P1 compared with the control subjects (32 +/- 6 vs. 48 +/- 4 microm). Foveal thickness (153 +/- 16 microm) was significantly reduced compared with that in control subjects and patients with ACH. CONCLUSIONS: Quantitative OCT image analysis reveals distinct patterns for controls subjects and patients with ACH and BCM, respectively. Quantitative analysis of OCT imaging can be useful in differentiating retinal diseases affecting photoreceptors. Foveal thickness is similar in both normal subjects and patients with ACH but is decreased in patients with BCM.

Clinical validation of an automated vessel-segmentation software of the extracranial-carotid arteries based on 3D-MRA: A prospective study

OBJECTIVES: To determine the accuracy of automated vessel-segmentation software for vessel-diameter measurements based on three-dimensional contrast-enhanced magnetic resonance angiography (3D-MRA). METHOD: In 10 patients with high-grade carotid stenosis, automated measurements of both carotid arteries were obtained with 3D-MRA by two independent investigators and compared with manual measurements obtained by digital subtraction angiography (DSA) and 2D maximum-intensity projection (2D-MIP) based on MRA and duplex ultrasonography (US). In 42 patients undergoing carotid endarterectomy (CEA), intraoperative measurements (IOP) were compared with postoperative 3D-MRA and US. RESULTS: Mean interoperator variability was 8% for measurements by DSA and 11% by 2D-MIP, but there was no interoperator variability with the automated 3D-MRA analysis. Good correlations were found between DSA (standard of reference), manual 2D-MIP (rP=0.6) and automated 3D-MRA (rP=0.8). Excellent correlations were found between IOP, 3D-MRA (rP=0.93) and US (rP=0.83). CONCLUSION: Automated 3D-MRA-based vessel segmentation and quantification result in accurate measurements of extracerebral-vessel dimensions.

Dynamic properties of wood using the Split-Hopkinson Pressure Bar

Strain rate significantly affects the strength of a material. The Split-Hopkinson Pressure Bar (SHPB) was initially used to study the effects of high strain rate (~103 1/s) testing of metals. Later modifications to the original technique allowed for the study of brittle materials such as ceramics, concrete, and rock. While material properties of wood for static and creep strain rates are readily available, data on the dynamic properties of wood are sparse. Previous work using the SHPB technique with wood has been limited in scope to variability of only a few conditions and tests of the applicability of the SHPB theory on wood have not been performed. Tests were conducted using a large diameter (3.0 inch (75 mm)) SHPB. The strain rate and total strain applied to a specimen are dependent on the striker bar length and velocity at impact. Pulse shapers are used to further modify the strain rate and change the shape of the strain pulse. A series of tests were used to determine test conditions necessary to produce a strain rate, total strain, and pulse shape appropriate for testing wood specimens. Hard maple, consisting of sugar maple (Acer saccharum) and black maple (Acer nigrum), and eastern white pine (Pinus strobus) specimens were used to represent a dense hardwood and a low-density soft wood. Specimens were machined to diameters of 2.5 and 3.0 inches and an assortment of lengths were tested to determine the appropriate specimen dimensions. Longitudinal specimens of 1.5 inch length and radial and tangential specimens of 0.5 inch length were found to be most applicable to SHPB testing. Stress/strain curves were generated from the SHPB data and validated with 6061-T6 aluminum and wood specimens. Stress was indirectly corroborated with gaged aluminum specimens. Specimen strain was assessed with strain gages, digital image analysis, and measurement of residual strain to confirm the strain calculated from SHPB data. The SHPB was found to be a useful tool in accurately assessing the material properties of wood under high strain rates (70 to 340 1/s) and short load durations (70 to 150 μs to compressive failure).

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.

Tree phenology and carbon dioxide fluxes - use of digital photography for process-based interpretation at the ecosystem scale

Vegetation phenology is an important indicator of climate change and climate variability and it is strongly connected to biospheric–atmospheric gas exchange. We aimed to evaluate the applicability of phenological information derived from digital imagery for the interpretation of CO2 exchange measurements. For the years 2005–2007 we analyzed seasonal phenological development of 2 temperate mixed forests using tower-based imagery from standard RGB cameras. Phenological information was jointly analyzed with gross primary productivity (GPP) derived from net ecosystem exchange data. Automated image analysis provided reliable information on vegetation developmental stages of beech and ash trees covering all seasons. A phenological index derived from image color values was strongly correlated with GPP, with a significant mean time lag of several days for ash trees and several weeks for beech trees in early summer (May to mid-July). Leaf emergence dates for the dominant tree species partly explained temporal behaviour of spring GPP but were also masked by local meteorological conditions. We conclude that digital cameras at flux measurement sites not only provide an objective measure of the physiological state of a forest canopy at high temporal and spatial resolutions, but also complement CO2 and water exchange measurements, improving our knowledge of ecosystem processes.

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.

Automatic quantitation of multiple sclerosis lesions on MR images

A two-pronged approach for the automatic quantitation of multiple sclerosis (MS) lesions on magnetic resonance (MR) images has been developed. This method includes the design and use of a pulse sequence for improved lesion-to-tissue contrast (LTC) and seeks to identify and minimize the sources of false lesion classifications in segmented images. The new pulse sequence, referred to as AFFIRMATIVE (Attenuation of Fluid by Fast Inversion Recovery with MAgnetization Transfer Imaging with Variable Echoes), improves the LTC, relative to spin-echo images, by combining Fluid-Attenuated Inversion Recovery (FLAIR) and Magnetization Transfer Contrast (MTC). In addition to acquiring fast FLAIR/MTC images, the AFFIRMATIVE sequence simultaneously acquires fast spin-echo (FSE) images for spatial registration of images, which is necessary for accurate lesion quantitation. Flow has been found to be a primary source of false lesion classifications. Therefore, an imaging protocol and reconstruction methods are developed to generate "flow images" which depict both coherent (vascular) and incoherent (CSF) flow. An automatic technique is designed for the removal of extra-meningeal tissues, since these are known to be sources of false lesion classifications. A retrospective, three-dimensional (3D) registration algorithm is implemented to correct for patient movement which may have occurred between AFFIRMATIVE and flow imaging scans. Following application of these pre-processing steps, images are segmented into white matter, gray matter, cerebrospinal fluid, and MS lesions based on AFFIRMATIVE and flow images using an automatic algorithm. All algorithms are seamlessly integrated into a single MR image analysis software package. Lesion quantitation has been performed on images from 15 patient volunteers. The total processing time is less than two hours per patient on a SPARCstation 20. The automated nature of this approach should provide an objective means of monitoring the progression, stabilization, and/or regression of MS lesions in large-scale, multi-center clinical trials. ^

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.