Measuring the regulation of keratin filament network dynamics.

The organization of the keratin intermediate filament cytoskeleton is closely linked to epithelial function. To study keratin network plasticity and its regulation at different levels, tools are needed to localize and measure local network dynamics. In this paper, we present image analysis methods designed to determine the speed and direction of keratin filament motion and to identify locations of keratin filament polymerization and depolymerization at subcellular resolution. Using these methods, we have analyzed time-lapse fluorescence recordings of fluorescent keratin 13 in human vulva carcinoma-derived A431 cells. The fluorescent keratins integrated into the endogenous keratin cytoskeleton, and thereby served as reliable markers of keratin dynamics. We found that increased times after seeding correlated with down-regulation of inward-directed keratin filament movement. Bulk flow analyses further revealed that keratin filament polymerization in the cell periphery and keratin depolymerization in the more central cytoplasm were both reduced. Treating these cells and other human keratinocyte-derived cells with EGF reversed all these processes within a few minutes, coinciding with increased keratin phosphorylation. These results highlight the value of the newly developed tools for identifying modulators of keratin filament network dynamics and characterizing their mode of action, which, in turn, contributes to understanding the close link between keratin filament network plasticity and epithelial physiology.

Comprehensive validation of computational fluid dynamics simulations of in-vivo blood flow in patient-specific cerebral aneurysms.

PURPOSE: Recently, image-based computational fluid dynamic (CFD) simulations have been proposed to investigate the local hemodynamics inside human cerebral aneurysms. It was suggested that the knowledge of the computed three-dimensional flow fields can be used to assist clinical risk assessment and treatment decision making. Therefore, it was desired to know the reliability of CFD for cerebral blood flow simulation, and be able to provide clinical feedback. However, the validations were not yet comprehensive as they lack either patient-specific boundary conditions (BCs) required for CFD simulations or quantitative comparison methods. METHODS: In this study, based on a recently proposed in-vitro quantitative CFD evaluation approach via virtual angiography, the CFD evaluation was extended from phantom to patient studies. In contrast to previous work, patient-specific blood flow rates obtained by transcranial color coded Doppler ultrasound measurements were used to impose CFD BCs. Virtual angiograms (VAs) were constructed which resemble clinically acquired angiograms (AAs). Quantitative measures were defined to thoroughly evaluate the correspondence of the detailed flow features between the AAs and the VAs, and thus, the reliability of CFD simulations. RESULTS: The proposed simulation pipeline provided a comprehensive validation method of CFD simulation for reproducing cerebral blood flow, with a focus on the aneurysm region. Six patient cases were tested and close similarities were found in terms of spatial and temporal variations of contrast agent (CA) distribution between AAs and VAs. For patient #1 to #5, discrepancies of less than 11% were found for the relative root mean square errors in time intensity curve comparisons from characteristic vasculature positions. For patient #6, where the CA concentration curve at vessel inlet cannot be directly extracted from the AAs and given as a BC, deviations about 20% were found. CONCLUSIONS: As a conclusion, the reliability of the CFD simulations was well confirmed. Besides, it was shown that the accuracy of CFD simulations was closely related to the input BCs.

Design and implementation of multisteerable matched filters.

Image analysis problems such as feature tracking, edge detection, image enhancement, or texture analysis require thedetection of multi-oriented patterns which can appear at arbitrary orientations. Direct rotated matched filtering for feature detection is computationally expensive, but can be sped up with steerable filters. So far, steerable filter approaches were limited to only one direction. Many important low-level image features are, however, characterized by more than a single orientation. We therefore present here a framework for efficiently detecting specific multi-oriented patterns with arbitrary orientations in grayscale images. The core idea is to construct multisteerable filters by appropriate combinations of single-steerable filters. We exploit that steerable filters are closed under addition and multiplication. This allows to derive a design guide for multisteerable filters by means of multivariate polynomials. Furthermore, we describe an efficient implementation scheme and discuss the use of weighting functions to reduce angular oscillations. Applications in camera calibration, junction analysis of images from plant roots, and the discrimination of L, T, and X-junctions demonstrate the potential of this approach.

Signal and noise modeling in confocal laser scanning fluorescence microscopy.

Fluorescence confocal laser scanning microscopy (CLSM) has revolutionized imaging of subcellular structures in biomedical research by enabling the acquisition of 3D time-series of fluorescently-tagged proteins in living cells, hence forming the basis for an automated quantification of their morphological and dynamic characteristics. Due to the inherently weak fluorescence, CLSM images exhibit a low SNR. We present a novel model for the transfer of signal and noise in CLSM that is both theoretically sound as well as corroborated by a rigorous analysis of the pixel intensity statistics via measurement of the 3D noise power spectra, signal-dependence and distribution. Our model provides a better fit to the data than previously proposed models. Further, it forms the basis for (i) the simulation of the CLSM imaging process indispensable for the quantitative evaluation of CLSM image analysis algorithms, (ii) the application of Poisson denoising algorithms and (iii) the reconstruction of the fluorescence signal.

Computer-based classification of small colorectal polyps by using narrow-band imaging with optical magnification.

BACKGROUND: Recent studies have shown that narrow-band imaging (NBI) is a powerful diagnostic tool for the differentiation between neoplastic and non-neoplastic colorectal polyps. OBJECTIVE: To develop a computer-based method for classification of colorectal polyps. DESIGN: A prospective study. SETTING: University hospital. PATIENTS: A total of 214 patients with colorectal polyps who underwent a zoom NBI colonoscopy. INTERVENTIONS: A total of 434 detected polyps 10 mm or smaller were imaged and subsequently removed for histological analysis. MAIN OUTCOME MEASUREMENTS: Diagnostic performance in polyp classification by 2 experts, 2 nonexperts, and a computer-based algorithm. RESULTS: The expert group and the computer-based algorithm achieved a comparable diagnostic performance (expert group: 93.4% sensitivity, 91.8% specificity, and 92.7% accuracy; computer-based algorithm: 95.0% sensitivity, 90.3% specificity, and 93.1% accuracy) and were both significantly superior to the nonexpert group (86.0% sensitivity, 87.8% specificity, and 86.8% accuracy) in terms of sensitivity, negative predictive value, and accuracy. Subgroup analysis of 255 polyps 5 mm or smaller revealed comparable results without significant differences in the overall analysis of all polyps. LIMITATIONS: No fully automatic classification system. CONCLUSIONS: The study demonstrates that computer-based classification of colon polyps can be achieved with high diagnostic performance.

Activity quantification combining conjugate-view planar scintigraphies and SPECT/CT data for patient-specific 3-D dosimetry in radionuclide therapy.

PURPOSE: Three-dimensional dosimetry based on quantitative SPECT/CT has potential advantages over planar approaches, but may be impractical due to acquisition durations. We combine one SPECT/CT with improved quantification of multiple planar scintigraphies to shorten acquisitions. METHODS: A hybrid 2-D/3-D quantification technique is proposed, using SPECT/CT information for robust planar image quantification and creating virtual SPECTs out of conjugate-view planar scintigraphies; these are included in a 3-D absorbed dose calculation. A projection model simulates photon attenuation and scatter as well as camera and collimator effects. Planar and SPECT calibration techniques are described, offering multiple pathways of deriving calibration factors for hybrid quantification. Model, phantom and patient data are used to validate the approach on a per-organ basis, and the similarity of real and virtual SPECTs, and of planar images and virtual SPECT projections, is assessed using linear regression analysis. RESULTS: Organ overlap, background activity and organ geometry are accounted for in the algorithm. Hybrid time-activity curves yield the same information as those derived from a conventional SPECT evaluation. Where correct values are known, hybrid quantification errors are less than 16% for all but two compartments (SPECT/CT 23%). Under partial volume effects, hybrid quantification can provide more robust results than SPECT/CT. The mean correlation coefficient of 3-D data is 0.962 (2-D 0.934). As a consequence of good activity quantification performance, good agreement of absorbed dose estimates and dose-volume histograms with reference results is achieved. CONCLUSION: The proposed activity quantification method for 2-D scintigraphies can speed up SPECT/CT-based 3-D dosimetry without losing accuracy.

Venous tree separation in the liver: graph partitioning using a non-ising model.

Entangled tree-like vascular systems are commonly found in the body (e.g., in the peripheries and lungs). Separation of these systems in medical images may be formulated as a graph partitioning problem given an imperfect segmentation and specification of the tree roots. In this work, we show that the ubiquitous Ising-model approaches (e.g., Graph Cuts, Random Walker) are not appropriate for tackling this problem and propose a novel method based on recursive minimal paths for doing so. To motivate our method, we focus on the intertwined portal and hepatic venous systems in the liver. Separation of these systems is critical for liver intervention planning, in particular when resection is involved. We apply our method to 34 clinical datasets, each containing well over a hundred vessel branches, demonstrating its effectiveness.

Simultaneous reconstruction of activity and attenuation for PET/MR.

Medical investigations targeting a quantitative analysis of the position emission tomography (PET) images require the incorporation of additional knowledge about the photon attenuation distribution in the patient. Today, energy range adapted attenuation maps derived from computer tomography (CT) scans are used to effectively compensate for image quality degrading effects, such as attenuation and scatter. Replacing CT by magnetic resonance (MR) is considered as the next evolutionary step in the field of hybrid imaging systems. However, unlike CT, MR does not measure the photon attenuation and thus does not provide an easy access to this valuable information. Hence, many research groups currently investigate different technologies for MR-based attenuation correction (MR-AC). Typically, these approaches are based on techniques such as special acquisition sequences (alone or in combination with subsequent image processing), anatomical atlas registration, or pattern recognition techniques using a data base of MR and corresponding CT images. We propose a generic iterative reconstruction approach to simultaneously estimate the local tracer concentration and the attenuation distribution using the segmented MR image as anatomical reference. Instead of applying predefined attenuation values to specific anatomical regions or tissue types, the gamma attenuation at 511 keV is determined from the PET emission data. In particular, our approach uses a maximum-likelihood estimation for the activity and a gradient-ascent based algorithm for the attenuation distribution. The adverse effects of scattered and accidental gamma coincidences on the quantitative accuracy of PET, as well as artifacts caused by the inherent crosstalk between activity and attenuation estimation are efficiently reduced using enhanced decay event localization provided by time-of-flight PET, accurate correction for accidental coincidences, and a reduced number of unknown attenuation coefficients. First results achieved with measured whole body PET data and reference segmentation from CT showed an absolute mean difference of 0.005 cm⁻¹ (< 20%) in the lungs, 0.0009 cm⁻¹ (< 2%) in case of fat, and 0.0015 cm⁻¹ (< 2%) for muscles and blood. The proposed method indicates a robust and reliable alternative to other MR-AC approaches targeting patient specific quantitative analysis in time-of-flight PET/MR.

Geometric calibration of lens and filter distortions for multispectral filter-wheel cameras.

High-fidelity color image acquisition with a multispectral camera utilizes optical filters to separate the visible electromagnetic spectrum into several passbands. This is often realized with a computer-controlled filter wheel, where each position is equipped with an optical bandpass filter. For each filter wheel position, a grayscale image is acquired and the passbands are finally combined to a multispectral image. However, the different optical properties and non-coplanar alignment of the filters cause image aberrations since the optical path is slightly different for each filter wheel position. As in a normal camera system, the lens causes additional wavelength-dependent image distortions called chromatic aberrations. When transforming the multispectral image with these aberrations into an RGB image, color fringes appear, and the image exhibits a pincushion or barrel distortion. In this paper, we address both the distortions caused by the lens and by the filters. Based on a physical model of the bandpass filters, we show that the aberrations caused by the filters can be modeled by displaced image planes. The lens distortions are modeled by an extended pinhole camera model, which results in a remaining mean calibration error of only 0.07 pixels. Using an absolute calibration target, we then geometrically calibrate each passband and compensate for both lens and filter distortions simultaneously. We show that both types of aberrations can be compensated and present detailed results on the remaining calibration errors.

Inertial navigation system for bladder endoscopy.

The usage of video endoscopes in cystoscopic interventions of the urinary bladder impedes an intuitive navigation. Although image-based solutions such as panorama images can provide extended views of the surgical field, a real-time 3-D navigation is not supported. Furthermore, the integration of common tracking systems in ambulant clinics is often hindered due to low usability and high costs. Thus, we discuss in this paper a first low-cost inertial navigation system. Our evaluation results show that in spite of lower sensor accuracies, mean errors between < 1° and 4° are achieved for solid angles. Using endoscopes with different view angles we apply an extended endoscope model for an adaptive displacement correction. Furthermore, we implement a first guided navigation tool for tumor re-identification in real-time.

Gap detection in endoscopic video sequences using graphs.

In minimal invasive surgery (MIS) a complete and seamless inspection of organs, e.g. the urinary bladder, using video endoscopes is often required for diagnostics. Since the endoscope is usually guided by free-hand, it is difficult to ensure a sequence of seamless frame transitions. Also 2-D panoramic images showing an extended field of view (FOV) do not provide always reliable results, since their interpretations are limited by potentially strong geometric distortions. To overcome these limitations and provide a direct verification method, we develop a gap detection algorithm using graphs. Exploiting the motion information of the applied zig-zag scan, we construct a graph representation of the video sequence. Without any explicit global image visualization our graph search algorithm identifies reliably frame discontinuities, which would lead to holes and slit artifacts in a panoramic view. The algorithm shows high detection rates and provides a fast method to verify frame discontinuities in the whole video sequence. Missed regions are highlighted by local image compositions which can be displayed during the intervention for assistance and inspection control.

3D segmentation of keratin intermediate filaments in confocal laser scanning microscopy.

In this paper, we propose and compare different methods for the 3D segmentation of keratin intermediate filaments (KFs) in images acquired using confocal laser scanning microscopy (CLSM). KFs are elastic cables forming a complex scaffolding within epithelial cells. They are involved in many basic cell functions. To understand the mechanisms of filament formation and network organisation under physiological and pathological conditions, quantitative measurements of dynamic network alterations are essential. Segmenting KFs is a key component for analyzing their dynamic and biomechanical properties. KFs were labeled with fluorescent keratins to allow high resolution imaging of network dynamics in native cells. Our segmentation methods follow the principle of ridge enhancement filtering and subsequent centerline extraction. The evaluation of the methods is two-fold: (i) We develop synthetic data that exhibit the characteristics of real CLSM data to evaluate the precision of the different methods in terms of centerline localisation and (ii) we perform a connected component analysis on the segmentation results of real KF data to assess whether the connectivity of highly complex networks is being preserved by the segmentation. Our evaluation shows that in the presence of strong noise and despite the highly anisotropic spatial resolution of CLSM images the proposed method is able to accurately localize the centerlines of the KFs and to preserve the KF networks' connectivity. Taken together this is a strong indicator that also the network topology is being preserved.

Phantom-based experimental validation of computational fluid dynamics simulations on cerebral aneurysms.

PURPOSE: Recently, image-based computational fluid dynamics (CFD) simulation has been applied to investigate the hemodynamics inside human cerebral aneurysms. The knowledge of the computed three-dimensional flow fields is used for clinical risk assessment and treatment decision making. However, the reliability of the application specific CFD results has not been thoroughly validated yet. METHODS: In this work, by exploiting a phantom aneurysm model, the authors therefore aim to prove the reliability of the CFD results obtained from simulations with sufficiently accurate input boundary conditions. To confirm the correlation between the CFD results and the reality, virtual angiograms are generated by the simulation pipeline and are quantitatively compared to the experimentally acquired angiograms. In addition, a parametric study has been carried out to systematically investigate the influence of the input parameters associated with the current measuring techniques on the flow patterns. RESULTS: Qualitative and quantitative evaluations demonstrate good agreement between the simulated and the real flow dynamics. Discrepancies of less than 15% are found for the relative root mean square errors of time intensity curve comparisons from each selected characteristic position. The investigated input parameters show different influences on the simulation results, indicating the desired accuracy in the measurements. CONCLUSIONS: This study provides a comprehensive validation method of CFD simulation for reproducing the real flow field in the cerebral aneurysm phantom under well controlled conditions. The reliability of the CFD is well confirmed. Through the parametric study, it is possible to assess the degree of validity of the associated CFD model based on the parameter values and their estimated accuracy range.

Range flow in varying illumination: algorithms and comparisons.

We extend estimation of range flow to handle brightness changes in image data caused by inhomogeneous illumination. Standard range flow computes 3D velocity fields using both range and intensity image sequences. Toward this end, range flow estimation combines a depth change model with a brightness constancy model. However, local brightness is generally not preserved when object surfaces rotate relative to the camera or the light sources, or when surfaces move in inhomogeneous illumination. We describe and investigate different approaches to handle such brightness changes. A straightforward approach is to prefilter the intensity data such that brightness changes are suppressed, for instance, by a highpass or a homomorphic filter. Such prefiltering may, though, reduce the signal-to-noise ratio. An alternative novel approach is to replace the brightness constancy model by 1) a gradient constancy model, or 2) by a combination of gradient and brightness constancy constraints used earlier successfully for optical flow, or 3) by a physics-based brightness change model. In performance tests, the standard version and the novel versions of range flow estimation are investigated using prefiltered or nonprefiltered synthetic data with available ground truth. Furthermore, the influences of additive Gaussian noise and simulated shot noise are investigated. Finally, we compare all range flow estimators on real data.

The keratin-filament cycle of assembly and disassembly.

Continuous and regulated remodelling of the cytoskeleton is crucial for many basic cell functions. In contrast to actin filaments and microtubules, it is not understood how this is accomplished for the third major cytoskeletal filament system, which consists of intermediate-filament polypeptides. Using time-lapse fluorescence microscopy of living interphase cells, in combination with photobleaching, photoactivation and quantitative fluorescence measurements, we observed that epithelial keratin intermediate filaments constantly release non-filamentous subunits, which are reused in the cell periphery for filament assembly. This cycle is independent of protein biosynthesis. The different stages of the cycle occur in defined cellular subdomains: assembly takes place in the cell periphery and newly formed filaments are constantly transported toward the perinuclear region while disassembly occurs, giving rise to diffusible subunits for another round of peripheral assembly. Remaining juxtanuclear filaments stabilize and encage the nucleus. Our data suggest that the keratin-filament cycle of assembly and disassembly is a major mechanism of intermediate-filament network plasticity, allowing rapid adaptation to specific requirements, notably in migrating cells.

Texture classification by modeling joint distributions of local patterns with gaussian mixtures.

Texture classification generally requires the analysis of patterns in local pixel neighborhoods. Statistically, the underlying processes are comprehensively described by their joint probability density functions (jPDFs). Even for small neighborhoods, however, stable estimation of jPDFs by joint histograms (jHSTs) is often infeasible, since the number of entries in the jHST exceeds by far the number of pixels in a typical texture region. Moreover, evaluation of distance functions between jHSTs is often computationally prohibitive. Practically, the number of entries in a jHST is therefore reduced by considering only two-pixel patterns, leading to 2D-jHSTs known as cooccurrence matrices, or by quantization of the gray levels in local patterns to only two gray levels, yielding local binary patterns (LBPs). Both approaches result in a loss of information. We introduce here a framework for supervised texture classification which reduces or avoids this information loss. Local texture neighborhoods are first filtered by a filter bank. Without further quantization, the jPDF of the filter responses is then described parametrically by gaussian mixture models (GMMs). We show that the parameters of the GMMs can be reliably estimated from small image regions. Moreover, distances between the thus modelled jPDFs of different texture patterns can be computed efficiently in closed form from their model parameters. We furthermore extend this texture descriptor to achieve full invariance to rotation. We evaluate the framework for different filter banks on the Brodatz texture set. We first show that combining the LBP difference filters with the GMM-based density estimator outperforms the classical LBP approach and its codebook extensions. When replacing these-rather elementary-difference filters by the wavelet frame transform (WFT), the performance of the framework on all 111 Brodatz textures exceeds the one obtained more recently by spin image and RIFT descriptors by Lazebnik et al.

Local and global panoramic imaging for fluorescence bladder endoscopy.

Endoscopic treatment of bladder cancer is more and more often based on photodynamic diagnostics (PDD), a specialized endoscopic technique where a narrow-band bluish illumination causes tumors to fluoresce reddish. Contrast between tumors and healthy bladder tissue is thus noticeably increased compared to white light endoscopy. A downside of PDD is the low illumination power, which requires that the distance between endoscope and bladder wall be kept low, thus resulting in a small field of view (FOV). We therefore describe an approach to combine several successive frames into a local PDD panorama, which provides a larger and sufficiently bright FOV for treatment. Furthermore, the endoscopic cancer treatment generally starts with a complete scan of the bladder to detect the tumors. For diagnosis, navigation and reporting, a global overview image of the bladder wall is often desired. While construction of such a global panorama can be based on the same algorithm as the local panorama, direct planar visualization of the sphere-shaped bladder may cause severe distortions. Apart from the global panorama computation itself, we therefore analyze these distortions, and provide an alternative visualization which is based on bladder depictions used in standard reporting forms and anatomy textbooks.

Analysis of multiple orientations.

Estimation of local orientations in multivariate signals is an important problem in image processing and computer vision. This general problem formulation also covers optical flow estimation, which can be regarded as orientation estimation in space-time-volumes. Modelling a signal using only a single orientation, however, is often too restrictive, since occlusions and transparencies occur frequently, thus necessitating the modelling and analysis of multiple orientations. We, therefore, develop a unifying mathematical model for multiple orientations: Beyond describing an arbitrary number of orientations in scalar- and vector-valued image data such as color image sequences, it allows the unified treatment of additively and occludingly superimposed oriented structures as well as of combinations of these. Based on this model, we describe estimation schemes for an arbitrary number of additively or occludingly superimposed orientations in images. We confirm the performance of our framework on both synthetic and real image data.

Toward a multimodal cell analysis of brush biopsies for the early detection of oral cancer.

BACKGROUND: This report describes what to the authors' knowledge is the first clinical application of semiautomated multimodal cell analysis (MMCA), a novel technique for the early detection of cancer for cases with a limited number of suspicious cells. In this clinical study, MMCA was applied to oral cancer diagnostics on brush biopsies. The MMCA approach was based on the sequential application of multiple stainings of identical, slide-based cells and repeated relocalizations and measurements of their diagnostic features, resulting in multiparametric features of individual cells. Data integration of the variously stained cells increased diagnostic accuracy. The implementation of MMCA also enabled fully automatic, adaptive image preprocessing, including registration of multimodal images and segmentation of cell nuclei. METHODS: In a preliminary clinical trial, 47 slides from brush biopsies of suspicious oral lesions were analyzed. The final histologic diagnoses included 20 squamous cell carcinomas, 7 hyperkeratotic leukoplakias, and 20 lichen planus mucosae. RESULTS: The stepwise application of 2 additional approaches (morphology, DNA content, argyrophilic nucleolar organizer region counts) increased the specificity of conventional cytologic diagnosis from 92.6% to 100%. This feasibility study provided a proof of concept, demonstrating efficiency, robustness, and diagnostic accuracy on slide-based cytologic specimens. CONCLUSIONS: The authors concluded that MMCA may become a sensitive and highly specific, objective, and reproducible adjuvant diagnostic tool for the identification of neoplastic changes in oral smears that contain only a few abnormal cells.

Video-based measuring of quality parameters for tricuspid xenograft heart valve implants.

Defective heart valves are often replaced by implants in open-heart surgery. Both mechanical and biological implants are available. Among biological implants, xenograft ones-i.e., valves grafted from animals such as pigs, are widely used. Good implants should exhibit certain typical anatomical and functional characteristics to successfully replace the native tissue. Here, we describe a video-based system for measuring quality parameters of xenograft heart valve implants, including the area of the orifice and the fluttering of the valves' leaflets, i.e., their flaps (or cusps). Our system employs automatic methods that provide a precise and reproducible way to infer the quality of an implant. The automatic analysis of both a valve's orifice and the fluttering of its leaflets offers a more comprehensive quality assessment than current, mostly manual methods. We focus on valves with three leaflets, i.e., aortic, pulmonary, and tricuspid valves.