Automatic Detection of Periapical Osteolytic Lesions on CBCT Using Deep CNNs

INTRODUCTION

Cone beam computed tomography (CBCT) is an essential diagnostic tool in oral radiology. Radiolucent periapical lesions (PALs) represent the most frequent jaw lesions. However, the description, interpretation, and documentation of radiological findings, especially incidental findings, are time-consuming and resource-intensive, requiring a high degree of expertise. To improve quality, dentists may use artificial intelligence in the form of deep learning tools. This study was conducted to develop and validate a deep convolutional neuronal network for the automated detection of osteolytic PALs in CBCT datasets.

METHODS

CBCT datasets from routine clinical operations (maxilla, mandible, or both) performed from January to October 2020 were retrospectively screened and selected. A two-step approach was used for automatic PAL detection. First, tooth localization and identification were performed using the SpatialConfiguration-Net based on heatmap regression. Second, binary segmentation of lesions was performed using a modified U-Net architecture. A total of 144 CBCT images were used to train and test the networks. The method was evaluated using the four-fold cross-validation technique.

RESULTS

The success detection rate of the tooth localization network ranged between 72.6% and 97.3%, whereas the sensitivity and specificity values of lesion detection were 97.1% and 88.0%, respectively.

CONCLUSIONS

Although PALs showed variations in appearance, size, and shape in the CBCT dataset, and a high imbalance existed between teeth with and without PALs, the proposed fully automated method provided excellent results compared with related literature.

A massively multi-scale approach to characterizing tissue architecture by synchrotron micro-CT applied to the human placenta

Multi-scale structural assessment of biological soft tissue is challenging but essential to gain insight into structure-function relationships of tissue/organ. Using the human placenta as an example, this study brings together sophisticated sample preparation protocols, advanced imaging and robust, validated machine-learning segmentation techniques to provide the first massively multi-scale and multi-domain information that enables detailed morphological and functional analyses of both maternal and fetal placental domains. Finally, we quantify the scale-dependent error in morphological metrics of heterogeneous placental tissue, estimating the minimal tissue scale needed in extracting meaningful biological data. The developed protocol is beneficial for high-throughput investigation of structure-function relationships in both normal and diseased placentas, allowing us to optimize therapeutic approaches for pathological pregnancies. In addition, the methodology presented is applicable in the characterization of tissue architecture and physiological behaviours of other complex organs with similarity to the placenta, where an exchange barrier possesses circulating vascular and avascular fluid spaces.

Cardioprotective actions of the sodium-activated potassium channel Slack (aka Slo2.2, KNa1.1 or KCNT1)

Background

Sodium-activated potassium channels (KNa) Slack (sequence like a calcium-activated potassium channel, aka Slo 2.2, KNa1.1 or KCNT1) are widely expressed throughout neuronal tissue, whereas their presence and function in the cardiovascular system is not well understood. Due to high intracellular sodium concentrations ([Na+]i) necessary to induce half-maximal channel activation, we hypothesized that Slack function is attributed to pathophysiological conditions such as myocardial ischemia.

Purpose

To elucidate the putative functions of Slack in the murine heart and in cardiomyocytes (CMs) and to explore whether the ischemia and reperfusion (I/R)-induced cardiac damage is affected by endogenous Slack channel activity.

Methods

I/R injury was evaluated in global and CM-specific Slack knockout mice (Slack gKO, CM Slack KO) and compared to litter-matched controls (Slack gWT, CM Slack CTR) by applying an in vivo model of acute myocardial infarction (MI). Infarct size (IS) was assessed at baseline, after ischemic pre- (iPre) and postconditioning (iPost) and in response to cinaciguat (CIN), a cGMP-elevating agent. Moreover, Slack expression and function in CMs was studied by biochemical and electrophysiological means and by utilizing the newly developed FRET-based K+ probe GEPII 1.0.

Results

IS in Slack gKO mice was increased in comparison to gWT littermates. In addition, the cardioprotection afforded by iPost was attenuated in the absence of Slack. To test if the increased vulnerability to I/R injury of the Slack gKO mouse model was originating from Slack activity in CMs, we subjected CM-specific Slack CTR and KO mutants to an identical MI procedure. IS measurements confirmed increased cardiac damage at baseline and reduced cardioprotective effects afforded by iPre and iPost in CM Slack KO mice. Interestingly, CIN (i.p., 30 min prior to I/R) reduced IS to a similar extent in both genotypes, suggesting that Slack functions in a cGMP-independent manner. Whole-cell patch clamp experiments on CMs demonstrated a reduction of the KNa-inhibitors clofilium- and chinidine-sensitive K+ outward currents in Slack gKO CM. Extracellular potassium ([K+]ex) accumulation measured with GEPII 1.0 was lower in Slack gKO versus gWT CM pools exposed to membrane permeabilizing agent digitonin. Accordingly, [K+]ex evoked by the Slack activators niclosamide and bithionol was lower in the absence of functional Slack in CMs.

Conclusion

The presented findings establish an important role of Slack channels for cardioprotective signalling mechanisms during I/R in vivo and for mediating beneficial effects of mechanical conditioning on IS. Corroborating in vitro studies on adult CMs exhibit an impaired [K+]ex dynamic in response to genetic or pharmacological modulation of Slack activity. Thus, we conclude that Slack-dependent K+ signalling pathways in CMs may represent a promising drug target that renders the heart muscle less vulnerable to the I/R-induced damage.

Funding Acknowledgement

Type of funding source: Public grant(s) – National budget only. Main funding source(s): Work in the authors' laboratories is supported by grants from the Deutsche Forschungsgemeinschaft (DFG) (to R.L.) and the DFG Research Unit 2060, “cGMP Signaling in Cell Growth and Survival” (to R.L. and P.R.).

Deep Metric Learning with BIER: Boosting Independent Embeddings Robustly

Learning similarity functions between image pairs with deep neural networks yields highly correlated activations of embeddings. In this work, we show how to improve the robustness of such embeddings by exploiting the independence within ensembles. To this end, we divide the last embedding layer of a deep network into an embedding ensemble and formulate the task of training this ensemble as an online gradient boosting problem. Each learner receives a reweighted training sample from the previous learners. Further, we propose two loss functions which increase the diversity in our ensemble. These loss functions can be applied either for weight initialization or during training. Together, our contributions leverage large embedding sizes more effectively by significantly reducing correlation of the embedding and consequently increase retrieval accuracy of the embedding. Our method works with any differentiable loss function and does not introduce any additional parameters during test time. We evaluate our metric learning method on image retrieval tasks and show that it improves over state-of-the-art methods on the CUB-200-2011, Cars-196, Stanford Online Products, In-Shop Clothes Retrieval and VehicleID datasets. Therefore, our findings suggest that by dividing deep networks at the end into several smaller and diverse networks, we can significantly reduce overfitting.

Purification and Application of Genetically Encoded Potassium Ion Indicators for Quantification of Potassium Ion Concentrations within Biological Samples

Vital cells maintain a steep potassium ion (K+ ) gradient across the plasma membrane. Intracellular potassium ion concentrations ([K+ ]) and especially the [K+ ] within the extracellular matrix are strictly regulated, the latter within a narrow range of ∼3.5 to 5.0 mM. Alterations of the extracellular K+ homeostasis are associated with severe pathological alterations and systemic diseases including hypo- or hypertension, heart rate alterations, heart failure, neuronal damage or abnormal skeleton muscle function. In higher eukaryotic organisms, the maintenance of the extracellular [K+ ] is mainly achieved by the kidney, responsible for K+ excretion and reabsorption. Thus, renal dysfunctions are typically associated with alterations in serum- or plasma [K+ ]. Generally, [K+ ] quantifications within bodily fluids are performed using ion selective electrodes. However, tracking such alterations in experimental models such as mice features several difficulties, mainly due to the small blood volume of these animals, hampering the repetitive collection of sample volumes required for measurements using ion selective electrodes. We have recently developed highly sensitive, genetically encoded potassium ion indicators, the GEPIIs, applicable for in vitro determinations of [K+ ]. In addition to the determination of [K+ ] within bodily fluids, GEPIIs proved suitable for the real-time visualization of cell viability over time and the exact determination of the number of dead cells. © 2019 The Authors.

Segmenting and tracking cell instances with cosine embeddings and recurrent hourglass networks

Differently to semantic segmentation, instance segmentation assigns unique labels to each individual instance of the same object class. In this work, we propose a novel recurrent fully convolutional network architecture for tracking such instance segmentations over time, which is highly relevant, e.g., in biomedical applications involving cell growth and migration. Our network architecture incorporates convolutional gated recurrent units (ConvGRU) into a stacked hourglass network to utilize temporal information, e.g., from microscopy videos. Moreover, we train our network with a novel embedding loss based on cosine similarities, such that the network predicts unique embeddings for every instance throughout videos, even in the presence of dynamic structural changes due to mitosis of cells. To create the final tracked instance segmentations, the pixel-wise embeddings are clustered among subsequent video frames by using the mean shift algorithm. After showing the performance of the instance segmentation on a static in-house dataset of muscle fibers from H&E-stained microscopy images, we also evaluate our proposed recurrent stacked hourglass network regarding instance segmentation and tracking performance on six datasets from the ISBI celltracking challenge, where it delivers state-of-the-art results.

Integrating spatial configuration into heatmap regression based CNNs for landmark localization

In many medical image analysis applications, only a limited amount of training data is available due to the costs of image acquisition and the large manual annotation effort required from experts. Training recent state-of-the-art machine learning methods like convolutional neural networks (CNNs) from small datasets is a challenging task. In this work on anatomical landmark localization, we propose a CNN architecture that learns to split the localization task into two simpler sub-problems, reducing the overall need for large training datasets. Our fully convolutional SpatialConfiguration-Net (SCN) learns this simplification due to multiplying the heatmap predictions of its two components and by training the network in an end-to-end manner. Thus, the SCN dedicates one component to locally accurate but ambiguous candidate predictions, while the other component improves robustness to ambiguities by incorporating the spatial configuration of landmarks. In our extensive experimental evaluation, we show that the proposed SCN outperforms related methods in terms of landmark localization error on a variety of size-limited 2D and 3D landmark localization datasets, i.e., hand radiographs, lateral cephalograms, hand MRIs, and spine CTs.

Structured Labels in Random Forests for Semantic Labelling and Object Detection

Ensembles of randomized decision trees, known as Random Forests, have become a valuable machine learning tool for addressing many computer vision problems. Despite their popularity, few works have tried to exploit contextual and structural information in random forests in order to improve their performance. In this paper, we propose a simple and effective way to integrate contextual information in random forests, which is typically reflected in the structured output space of complex problems like semantic image labelling. Our paper has several contributions: We show how random forests can be augmented with structured label information and be used to deliver structured low-level predictions. The learning task is carried out by employing a novel split function evaluation criterion that exploits the joint distribution observed in the structured label space. This allows the forest to learn typical label transitions between object classes and avoid locally implausible label configurations. We provide two approaches for integrating the structured output predictions obtained at a local level from the forest into a concise, global, semantic labelling. We integrate our new ideas also in the Hough-forest framework with the view of exploiting contextual information at the classification level to improve the performance on the task of object detection. Finally, we provide experimental evidence for the effectiveness of our approach on different tasks: Semantic image labelling on the challenging MSRCv2 and CamVid databases, reconstruction of occluded handwritten Chinese characters on the Kaist database and pedestrian detection on the TU Darmstadt databases.

Intuitive presentation of clinical forensic data using anonymous and person-specific 3D reference manikins

The increasing use of CT/MR devices in forensic analysis motivates the need to present forensic findings from different sources in an intuitive reference visualization, with the aim of combining 3D volumetric images along with digital photographs of external findings into a 3D computer graphics model. This model allows a comprehensive presentation of forensic findings in court and enables comparative evaluation studies correlating data sources. The goal of this work was to investigate different methods to generate anonymous and patient-specific 3D models which may be used as reference visualizations. The issue of registering 3D volumetric as well as 2D photographic data to such 3D models is addressed to provide an intuitive context for injury documentation from arbitrary modalities. We present an image processing and visualization work-flow, discuss the major parts of this work-flow, compare the different investigated reference models, and show a number of cases studies that underline the suitability of the proposed work-flow for presenting forensically relevant information in 3D visualizations.

Towards automatic bone age estimation from MRI: localization of 3D anatomical landmarks

Bone age estimation (BAE) is an important procedure in forensic practice which recently has seen a shift in attention from X-ray to MRI based imaging. To automate BAE from MRI, localization of the joints between hand bones is a crucial first step, which is challenging due to anatomical variations, different poses and repeating structures within the hand. We propose a landmark localization algorithm using multiple random regression forests, first analyzing the shape of the hand from information of the whole image, thus implicitly modeling the global landmark configuration, followed by a refinement based on more local information to increase prediction accuracy. We are able to clearly outperform related approaches on our dataset of 60 T1-weighted MR images, achieving a mean landmark localization error of 1.4 ± 1.5mm, while having only 0.25% outliers with an error greater than 10mm.

Global localization of 3D anatomical structures by pre-filtered Hough forests and discrete optimization

The accurate localization of anatomical landmarks is a challenging task, often solved by domain specific approaches. We propose a method for the automatic localization of landmarks in complex, repetitive anatomical structures. The key idea is to combine three steps: (1) a classifier for pre-filtering anatomical landmark positions that (2) are refined through a Hough regression model, together with (3) a parts-based model of the global landmark topology to select the final landmark positions. During training landmarks are annotated in a set of example volumes. A classifier learns local landmark appearance, and Hough regressors are trained to aggregate neighborhood information to a precise landmark coordinate position. A non-parametric geometric model encodes the spatial relationships between the landmarks and derives a topology which connects mutually predictive landmarks. During the global search we classify all voxels in the query volume, and perform regression-based agglomeration of landmark probabilities to highly accurate and specific candidate points at potential landmark locations. We encode the candidates' weights together with the conformity of the connecting edges to the learnt geometric model in a Markov Random Field (MRF). By solving the corresponding discrete optimization problem, the most probable location for each model landmark is found in the query volume. We show that this approach is able to consistently localize the model landmarks despite the complex and repetitive character of the anatomical structures on three challenging data sets (hand radiographs, hand CTs, and whole body CTs), with a median localization error of 0.80 mm, 1.19 mm and 2.71 mm, respectively.

Leukocyte segmentation and classification in blood-smear images

The detection and classification of leukocytes in blood smear images is a routine task in medical diagnosis. In this paper we present a fully automated approach to leukocyte segmentation that is robust with respect to cell appearance and image quality. A set of features is used to describe cytoplasm and nucleus properties. Pairwise SVM classification is used to discriminate between different cell types. Evaluation on a set of 1166 images (13 classes) resulted in 95% correct segmentations and 75% to 99% correct classification (with reject option).

Forensic-case analysis: from 3D imaging to interactive visualization

An interactive framework prepares raw computed-tomography and magnetic-resonance-imaging scans for courtroom presentations. The framework makes use of combined computer graphics and computer vision techniques to enable a forensic case analysis workflow.

On-line inverse multiple instance boosting for classifier grids

Classifier grids have shown to be a considerable choice for object detection from static cameras. By applying a single classifier per image location the classifier's complexity can be reduced and more specific and thus more accurate classifiers can be estimated. In addition, by using an on-line learner a highly adaptive but stable detection system can be obtained. Even though long-term stability has been demonstrated such systems still suffer from short-term drifting if an object is not moving over a long period of time. The goal of this work is to overcome this problem and thus to increase the recall while preserving the accuracy. In particular, we adapt ideas from multiple instance learning (MIL) for on-line boosting. In contrast to standard MIL approaches, which assume an ambiguity on the positive samples, we apply this concept to the negative samples: inverse multiple instance learning. By introducing temporal bags consisting of background images operating on different time scales, we can ensure that each bag contains at least one sample having a negative label, providing the theoretical requirements. The experimental results demonstrate superior classification results in presence of non-moving objects.

In melanoma, beta-catenin is a suppressor of invasion

Cell-type specific signalling determines cell fate under physiological conditions, but it is increasingly apparent that also in cancer development the impact of any given oncogenic pathway on the individual cancer pathology is dependent on cell-lineage specific molecular traits. For instance in colon and liver cancer canonical Wnt signalling produces increased cytoplasmic and nuclear localised beta-catenin, which correlates with invasion and poor prognosis. In contrast, in melanoma increased cytoplasmic and nuclear beta-catenin is currently emerging as a marker for good prognosis and thus appears to have a different function compared to other cancer types; however this function is unknown. We discovered that in contrast to its function in other cancers, in melanoma, beta-catenin blocks invasion. We demonstrate that this opposing role of nuclear beta-catenin in melanoma is mediated through MITF, a melanoma-specific protein that defines the lineage background of this cancer type. Downstream of beta-catenin MITF not only suppresses the Rho-GTPase regulated cell-morphology of invading melanoma cells, but also interferes with beta-catenin induced expression of the essential collagenase MT1-MMP, thus affecting all aspects of an invasive phenotype. Importantly, overexpression of MITF in invasive colon cancer cells modifies beta-catenin directed signalling and induces a ‘melanoma-phenotype’. In summary, the cell type specific presence of MITF in melanoma affects beta-catenin’s pro-invasive properties otherwise active in colon or liver cancer. Thus our study reveals the general importance of considering cell-type specific signalling for the accurate interpretation of tumour markers and ultimately for the design of rational therapies.

Stereoscopic motion analysis in densely packed clusters: 3D analysis of the shimmering behaviour in Giant honey bees

BACKGROUND

The detailed interpretation of mass phenomena such as human escape panic or swarm behaviour in birds, fish and insects requires detailed analysis of the 3D movements of individual participants. Here, we describe the adaptation of a 3D stereoscopic imaging method to measure the positional coordinates of individual agents in densely packed clusters. The method was applied to study behavioural aspects of shimmering in Giant honeybees, a collective defence behaviour that deters predatory wasps by visual cues, whereby individual bees flip their abdomen upwards in a split second, producing Mexican wave-like patterns.

RESULTS

Stereoscopic imaging provided non-invasive, automated, simultaneous, in-situ 3D measurements of hundreds of bees on the nest surface regarding their thoracic position and orientation of the body length axis. Segmentation was the basis for the stereo matching, which defined correspondences of individual bees in pairs of stereo images. Stereo-matched "agent bees" were re-identified in subsequent frames by the tracking procedure and triangulated into real-world coordinates. These algorithms were required to calculate the three spatial motion components (dx: horizontal, dy: vertical and dz: towards and from the comb) of individual bees over time.

CONCLUSIONS

The method enables the assessment of the 3D positions of individual Giant honeybees, which is not possible with single-view cameras. The method can be applied to distinguish at the individual bee level active movements of the thoraces produced by abdominal flipping from passive motions generated by the moving bee curtain. The data provide evidence that the z-deflections of thoraces are potential cues for colony-intrinsic communication. The method helps to understand the phenomenon of collective decision-making through mechanoceptive synchronization and to associate shimmering with the principles of wave propagation. With further, minor modifications, the method could be used to study aspects of other mass phenomena that involve active and passive movements of individual agents in densely packed clusters.

Rapid Communication with a "P300" Matrix Speller Using Electrocorticographic Signals (ECoG)

A brain-computer interface (BCI) can provide a non-muscular communication channel to severely disabled people. One particular realization of a BCI is the P300 matrix speller that was originally described by Farwell and Donchin (1988). This speller uses event-related potentials (ERPs) that include the P300 ERP. All previous online studies of the P300 matrix speller used scalp-recorded electroencephalography (EEG) and were limited in their communication performance to only a few characters per minute. In our study, we investigated the feasibility of using electrocorticographic (ECoG) signals for online operation of the matrix speller, and determined associated spelling rates. We used the matrix speller that is implemented in the BCI2000 system. This speller used ECoG signals that were recorded from frontal, parietal, and occipital areas in one subject. This subject spelled a total of 444 characters in online experiments. The results showed that the subject sustained a rate of 17 characters/min (i.e., 69 bits/min), and achieved a peak rate of 22 characters/min (i.e., 113 bits/min). Detailed analysis of the results suggests that ERPs over visual areas (i.e., visual evoked potentials) contribute significantly to the performance of the matrix speller BCI system. Our results also point to potential reasons for the apparent advantages in spelling performance of ECoG compared to EEG. Thus, with additional verification in more subjects, these results may further extend the communication options for people with serious neuromuscular disabilities.

Does the 'P300' speller depend on eye gaze?

Many people affected by debilitating neuromuscular disorders such as amyotrophic lateral sclerosis, brainstem stroke or spinal cord injury are impaired in their ability to, or are even unable to, communicate. A brain-computer interface (BCI) uses brain signals, rather than muscles, to re-establish communication with the outside world. One particular BCI approach is the so-called 'P300 matrix speller' that was first described by Farwell and Donchin (1988 Electroencephalogr. Clin. Neurophysiol. 70 510-23). It has been widely assumed that this method does not depend on the ability to focus on the desired character, because it was thought that it relies primarily on the P300-evoked potential and minimally, if at all, on other EEG features such as the visual-evoked potential (VEP). This issue is highly relevant for the clinical application of this BCI method, because eye movements may be impaired or lost in the relevant user population. This study investigated the extent to which the performance in a 'P300' speller BCI depends on eye gaze. We evaluated the performance of 17 healthy subjects using a 'P300' matrix speller under two conditions. Under one condition ('letter'), the subjects focused their eye gaze on the intended letter, while under the second condition ('center'), the subjects focused their eye gaze on a fixation cross that was located in the center of the matrix. The results show that the performance of the 'P300' matrix speller in normal subjects depends in considerable measure on gaze direction. They thereby disprove a widespread assumption in BCI research, and suggest that this BCI might function more effectively for people who retain some eye-movement control. The applicability of these findings to people with severe neuromuscular disabilities (particularly in eye-movements) remains to be determined.

Localization and trajectory reconstruction in surveillance cameras with nonoverlapping views

This paper proposes a method that localizes two surveillance cameras and simultaneously reconstructs object trajectories in 3D space. The method is an extension of the Direct Reference Plane method, which formulates the localization and the reconstruction as a system of linear equations that is globally solvable by Singular Value Decomposition. The method's assumptions are static synchronized cameras, smooth trajectories, known camera internal parameters, and the rotation between the cameras in a world coordinate system. The paper describes the method in the context of self-calibrating cameras, where the internal parameters and the rotation can be jointly obtained assuming a man-made scene with orthogonal structures. Experiments with synthetic and real--image data show that the method can recover the camera centers with an error less than half a meter even in the presence of a 4 meter gap between the fields of view.

DT-MRI based computation of collagen fiber deformation in human articular cartilage: a feasibility study

Accurate techniques for simulating the deformation of soft biological tissues are an increasingly valuable tool in many areas of biomechanical analysis and medical image computing. To model the complex morphology and response of articular cartilage, a hyperviscoelastic (dispersed) fiber-reinforced constitutive model is employed to complete two specimen-specific finite element (FE) simulations of an indentation experiment, with and without considering fiber dispersion. Ultra-high field Diffusion Tensor Magnetic Resonance Imaging (17.6 T DT-MRI) is performed on a specimen of human articular cartilage before and after indentation to approximately 20% compression. Based on this DT-MRI data, we detail a novel FE approach to determine the geometry (edge detection from first eigenvalue), the meshing (semi-automated smoothing of DTI measurement voxels), and the fiber structural input (estimated principal fiber direction and dispersion). The global and fiber fabric deformations of both the un-dispersed and dispersed fiber models provide a satisfactory match to that estimated experimentally. In both simulations, the fiber fabric in the superficial and middle zones becomes more aligned with the articular surface, although the dispersed model appears more consistent with the literature. In the future, a multi-disciplinary combination of DT-MRI and numerical simulation will allow the functional state of articular cartilage to be determined in vivo.

Segmentation of interwoven 3d tubular tree structures utilizing shape priors and graph cuts

The segmentation of tubular tree structures like vessel systems in volumetric datasets is of vital interest for many medical applications. We present a novel approach that allows to simultaneously separate and segment multiple interwoven tubular tree structures. The algorithm consists of two main processing steps. First, the tree structures are identified and corresponding shape priors are generated by using a bottom-up identification of tubular objects combined with a top-down grouping of these objects into complete tree structures. The grouping step allows us to separate interwoven trees and to handle local disturbances. Second, the generated shape priors are utilized for the intrinsic segmentation of the different tubular systems to avoid leakage or undersegmentation in locally disturbed regions. We have evaluated our method on phantom and different clinical CT datasets and demonstrated its ability to correctly obtain/separate different tree structures, accurately determine the surface of tubular tree structures, and robustly handle noise, disturbances (e.g., tumors), and deviations from cylindrical tube shapes like for example aneurysms.

A Phenomenological Approach Toward Patient-Specific Computational Modeling of Articular Cartilage Including Collagen Fiber Tracking

To model the cartilage morphology and the material response, a phenomenological and patient-specific simulation approach incorporating the collagen fiber fabric is proposed. Cartilage tissue response is nearly isochoric and time-dependent under physiological pressure levels. Hence, a viscoelastic constitutive model capable of reproducing finite strains is employed, while the time-dependent deformation change is purely isochoric. The model incorporates seven material parameters, which all have a physical interpretation. To calibrate the model and facilitate further analysis, five human cartilage specimens underwent a number of tests. A series of magnetic resonance imaging (MRI) sequences is taken, next the cartilage surface is imaged, then mechanical indentation tests are completed at 2–7 different locations per sample, resulting in force/displacement data over time, and finally, the underlying bone surface is imaged. Imaging and mechanical testing are performed with a custom-built robotics-based testing device. Stereo reconstruction of the cartilage and subchondral bone surface is employed, which, together with the proposed constitutive model, led to specimen-specific finite element simulations of the mechanical indentation tests. The force-time response of 23 such indentation experiment simulations is optimized to estimate the mean material parameters and corresponding standard deviations. The model is capable of reproducing the deformation behavior of human articular cartilage in the physiological loading domain, as demonstrated by the good agreement between the experiment and numerical results (R2=0.95±0.03, mean±standard deviation of force-time response for 23 indentation tests). To address validation, a sevenfold cross-validation experiment is performed on the 21 experiments representing healthy cartilage. To quantify the predictive error, the mean of the absolute force differences and Pearson’s correlation coefficient are both calculated. Deviations in the mean absolute difference, normalized by the peak force, range from 4% to 90%, with 40±25%(M±SD). The correlation coefficients across all predictions have a minimum of 0.939, and a maximum of 0.993 with 0.975±0.013(M±SD), which demonstrates an excellent match of the decay characteristics. A novel feature of the proposed method is 3D sample-specific numerical tracking of the fiber fabric deformation under general loading. This feature is demonstrated by comparing the estimated fiber fabric deformation with recently published experimental data determined by diffusion tensor MRI. The proposed approach is efficient enough to enable large-scale 3D contact simulations of knee joint loading in simulations with accurate joint geometries.

Comparison and evaluation of methods for liver segmentation from CT datasets

This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.

A practical procedure for real-time functional mapping of eloquent cortex using electrocorticographic signals in humans

Functional mapping of eloquent cortex is often necessary prior to invasive brain surgery, but current techniques that derive this mapping have important limitations. In this article, we demonstrate the first comprehensive evaluation of a rapid, robust, and practical mapping system that uses passive recordings of electrocorticographic signals. This mapping procedure is based on the BCI2000 and SIGFRIED technologies that we have been developing over the past several years. In our study, we evaluated 10 patients with epilepsy from four different institutions and compared the results of our procedure with the results derived using electrical cortical stimulation (ECS) mapping. The results show that our procedure derives a functional motor cortical map in only a few minutes. They also show a substantial concurrence with the results derived using ECS mapping. Specifically, compared with ECS maps, a next-neighbor evaluation showed no false negatives, and only 0.46 and 1.10% false positives for hand and tongue maps, respectively. In summary, we demonstrate the first comprehensive evaluation of a practical and robust mapping procedure that could become a new tool for planning of invasive brain surgeries.

Automatic quantification of joint space narrowing and erosions in rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic disease that affects and potentially destroys the joints of the appendicular skeleton. The precise and reproducible quantification of the progression of joint space narrowing and the erosive bone destructions caused by RA is crucial during treatment and in imaging biomarkers in clinical trials. Current manual scoring methods exhibit high interreader variability, even after intensive training, and thus, impede the efficient monitoring of the disease. We propose a fully automatic quantitative assessment of the radiographic changes that result from RA, to increase the accuracy, reproducibility, and speed of image interpretation. Initial joint location estimates are obtained by local linear mappings based on texture features. Bone contours are delineated by active shape models comprised of statistical models of bone shape and local texture. These models are refined by snakes which increase the accuracy and allow for a fitting of pathological deviations from the training population. The method then measures joint space widths and detects erosions on the bone contour. Joint space widths are measured with a coefficient of variation of 2%-7% for repeated measurements and erosion detection exhibits an area under the receiver operating characteristic (ROC) curve of 0.89. Model landmarks serve as a reference system along the contour. These landmarks enable the definition of joint regions and more specific follow-up monitoring. The automatic quantification allows for a remote analysis, relevant for multicenter clinical trials, and reduces the workload of clinical experts since parts of the process can be managed by nonexpert personnel.

Toward self-paced brain-computer communication: navigation through virtual worlds

The self-paced control paradigm enables users to operate brain-computer interfaces (BCI) in a more natural way: no longer is the machine in control of the timing and speed of communication, but rather the user is. This is important to enhance the usability, flexibility, and response time of a BCI. In this work, we show how subjects, after performing cue-based feedback training (smiley paradigm), learned to navigate self-paced through the "freeSpace" virtual environment (VE). Similar to computer games, subjects had the task of picking up items by using the following navigation commands: rotate left, rotate right, and move forward ( three classes). Since the self-paced control paradigm allows subjects to make voluntary decisions on time, type, and duration of mental activity, no cues or routing directives were presented. The BCI was based only on three bipolar electroencephalogram channels and operated by motor imagery. Eye movements (electrooculogram) and electromyographic artifacts were reduced and detected online. The results of three able-bodied subjects are reported and problems emerging from self-paced control are discussed.

A duality based algorithm for TV-L1-optical-flow image registration

Nonlinear image registration is a challenging task in the field of medical image analysis. In many applications discontinuities may be present in the displacement field, and intensity variations may occur. In this work we therefore utilize an energy functional which is based on Total Variation regularization and a robust data term. We propose a novel, fast and stable numerical scheme to find the minimizer of this energy. Our approach combines a fixed-point procedure derived from duality principles combined with a fast thresholding step. We show experimental results on synthetic and clinical CT lung data sets at different breathing states as well as registration results on inter-subject brain MRIs.

Model-based erosion spotting and visualization in rheumatoid arthritis

RATIONALE AND OBJECTIVES

A method for the automatic detection and the visualization of erosions caused by rheumatoid arthritis is investigated. Erosion-enhanced viewing is a contribution to the computer-aided diagnosis of rheumatoid arthritis. It supports the clinician by providing the automatic marking of erosions and the visualization of any deviations from intact anatomy for a concise reviewing interface.

MATERIALS AND METHODS

A generative appearance model is used to capture the variability of intact bone and erosions. The algorithm marks erosions on hand radiographs using this model, and visualizes these erosions with the help of the residual appearance error after fitting the model built from intact bone texture. The algorithm was evaluated on 17 hand radiographs. The standard of reference was an annotation of the erosions by a musculoskeletal radiologist.

RESULTS

Detection results from the algorithm are reported for a set of 17 radiographs of moderately diseased hands. With a specificity of 84%, the detection of unequivocal erosions achieved a sensitivity of 85%. A receiver operating characteristic analysis yields an area under the curve of 0.92. The visualization provided a clear representation of the erosions as determined by two musculoskeletal radiologists.

CONCLUSION

The automatic spotting of erosions provides promising results, and the visualization of the deviation from healthy anatomy aids clinicians in the evaluation of the erosions and in the reviewing of automatic detection results.

Algorithmic differentiation: application to variational problems in computer vision

Many vision problems can be formulated as minimization of appropriate energy functionals. These energy functionals are usually minimized, based on the calculus of variations (Euler-Lagrange equation). Once the Euler-Lagrange equation has been determined, it needs to be discretized in order to implement it on a digital computer. This is not a trivial task and, is moreover, error-prone. In this paper, we propose a flexible alternative. We discretize the energy functional and, subsequently, apply the mathematical concept of algorithmic differentiation to directly derive algorithms that implement the energy functional's derivatives. This approach has several advantages: First, the computed derivatives are exact with respect to the implementation of the energy functional. Second, it is basically straightforward to compute second-order derivatives and, thus, the Hessian matrix of the energy functional. Third, algorithmic differentiation is a process which can be automated. We demonstrate this novel approach on three representative vision problems (namely, denoising, segmentation, and stereo) and show that state-of-the-art results are obtained with little effort.

Micro-colony array based high throughput platform for enzyme library screening

Enzymes are becoming increasingly important tools for synthesizing and modifying fine and bulk chemicals. The availability of biocatalysts which fulfil the requirements of industrial processes is often limited. Recruiting suited enzymes from natural (e.g. metagenomes) and artificial (e.g. directed evolution) biodiversity is based on screening libraries of microbial clones expressing enzyme variants. However, exploring the complex diversity of such libraries needs efficient screening methods. Overcoming the "screening bottleneck" requires rapid high throughput technology allowing the analysis of a large diversity of different enzymes and applying different screening conditions. Facing these facts an efficient and cost effective method for high throughput screening of large enzyme libraries at the colony level was developed. Therefore, ordered high density micro-colony arrays were combined with optical sensor technology and automated image analysis. The system generally allows the simultaneous monitoring of enzyme activities reflected by up to 7000 micro-colonies spotted on a filter in the size of a micro-titer plate. A developed replica option also allows the analysis of clones under varying external conditions. The method was verified by a model screening using esterases and was proved to provide reliable enzyme activity measurements within single micro-colonies allowing the discrimination of activity differences in the range of 10-20%.

Object localization based on Markov random fields and symmetry interest points

We present an approach to detect anatomical structures by configurations of interest points, from a single example image. The representation of the configuration is based on Markov Random Fields, and the detection is performed in a single iteration by the MAX-SUM algorithm. Instead of sequentially matching pairs of interest points, the method takes the entire set of points, their local descriptors and the spatial configuration into account to find an optimal mapping of modeled object to target image. The image information is captured by symmetry-based interest points and local descriptors derived from Gradient Vector Flow. Experimental results are reported for two data-sets showing the applicability to complex medical data.