Biomedical subjects

Dimitris Metaxas

Publications and source records attributed to Dimitris Metaxas.

7 recordsLinked to original sources

PubMed2006

Novel method for high-throughput phenotyping of sleep in mice.

Assessment of sleep in mice currently requires initial implantation of chronic electrodes for assessment of electroencephalogram (EEG) and electromyogram (EMG) followed by time to recover from surgery. Hence, it is not ideal for high-throughput screening. To address this deficiency, a method of assessment of sleep and wakefulness in mice has been developed based on assessment of activity/inactivity either by digital video analysis or by breaking infrared beams in the mouse cage. It is based on the algorithm that any episode of continuous inactivity of > or =40 s is predicted to be sleep. The method gives excellent agreement in C57BL/6J male mice with simultaneous assessment of sleep by EEG/EMG recording. The average agreement over 8,640 10-s epochs in 24 h is 92% (n = 7 mice) with agreement in individual mice being 88-94%. Average EEG/EMG determined sleep per 2-h interval across the day was 59.4 min. The estimated mean difference (bias) per 2-h interval between inactivity-defined sleep and EEG/EMG-defined sleep was only 1.0 min (95% confidence interval for mean bias -0.06 to +2.6 min). The standard deviation of differences (precision) was 7.5 min per 2-h interval with 95% limits of agreement ranging from -13.7 to +15.7 min. Although bias significantly varied by time of day (P = 0.0007), the magnitude of time-of-day differences was not large (average bias during lights on and lights off was +5.0 and -3.0 min per 2-h interval, respectively). This method has applications in chemical mutagenesis and for studies of molecular changes in brain with sleep/wakefulness.

Algorithms↗

PubMed2005

Efficient learning by combining confidence-rated classifiers to incorporate unlabeled medical data.

In this paper, we propose a new dynamic learning framework that requires a small amount of labeled data in the beginning, then incrementally discovers informative unlabeled data to be hand-labeled and incorporates them into the training set to improve learning performance. This approach has great potential to reduce the training expense in many medical image analysis applications. The main contributions lie in a new strategy to combine confidence-rated classifiers learned on different feature sets and a robust way to evaluate the "informativeness" of each unlabeled example. Our framework is applied to the problem of classifying microscopic cell images. The experimental results show that 1) our strategy is more effective than simply multiplying the predicted probabilities, 2) the error rate of high-confidence predictions is much lower than the average error rate, and 3) hand-labeling informative examples with low-confidence predictions improves performance efficiently and the performance difference from hand-labeling all unlabeled data is very small.

Algorithms↗

PubMed2005

A hybrid framework for 3D medical image segmentation.

In this paper we propose a novel hybrid 3D segmentation framework which combines Gibbs models, marching cubes and deformable models. In the framework, first we construct a new Gibbs model whose energy function is defined on a high order clique system. The new model includes both region and boundary information during segmentation. Next we improve the original marching cubes method to construct 3D meshes from Gibbs models' output. The 3D mesh serves as the initial geometry of the deformable model. Then we deform the deformable model using external image forces so that the model converges to the object surface. We run the Gibbs model and the deformable model recursively by updating the Gibbs model's parameters using the region and boundary information in the deformable model segmentation result. In our approach, the hybrid combination of region-based methods and boundary-based methods results in improved segmentations of complex structures. The benefit of the methodology is that it produces high quality segmentations of 3D structures using little prior information and minimal user intervention. The modules in this segmentation methodology are developed within the context of the Insight ToolKit (ITK). We present experimental segmentation results of brain tumors and evaluate our method by comparing experimental results with expert manual segmentations. The evaluation results show that the methodology achieves high quality segmentation results with computational efficiency. We also present segmentation results of other clinical objects to illustrate the strength of the methodology as a generic segmentation framework.

Algorithms↗

PubMed2005

Incremental model-based estimation using geometric constraints.

We present a model-based framework for incremental, adaptive object shape estimation and tracking in monocular image sequences. Parametric structure and motion estimation methods usually assume a fixed class of shape representation (splines, deformable superquadrics, etc.) that is initialized prior to tracking. Since the model shape coverage is fixed a priori, the incremental recovery of structure is decoupled from tracking, thereby limiting both processes in their scope and robustness. In this work, we describe a model-based framework that supports the automatic detection and integration of low-level geometric primitives (lines) incrementally. Such primitives are not explicitly captured in the initial model, but are moving consistently with its image motion. The consistency tests used to identify new structure are based on trinocular constraints between geometric primitives. The method allows not only an increase in the model scope, but also improves tracking accuracy by including the newly recovered features in its state estimation. The formulation is a step toward automatic model building, since it allows both weaker assumptions on the availability of a prior shape representation and on the number of features that would otherwise be necessary for entirely bottom-up reconstruction. We demonstrate the proposed approach on two separate image-based tracking domains, each involving complex 3D object structure and motion.

Algorithms↗

PubMed2003

In vivo strain and stress estimation of the heart left and right ventricles from MRI images.

Mechanical properties of the myocardium have been investigated intensively in the last four decades. Many complex strain energy functions have been used to estimate the stress-strain relationship of myocardium because the heart muscle is an inhomogeneous, anisotropic, and nearly incompressible material, which undergoes large deformations. These functions can be effective for fitting in vitro experimental data from myocardial stretch testing. However, it is difficult to model in vivo myocardium using these strain energy functions. Moreover, such estimates have so far been carried out almost exclusively on the left ventricle, because of the relative thinness and complex geometry of the right ventricle. Previous work from our research group has successful estimated the motion and deformation of both the left and the right ventricles, using data from noninvasive tagged magnetic resonance imaging. In this paper, we present a novel statistical model to estimate the in vivo material properties and strain and stress distribution in both ventricles, using such data. Two normal hearts and two hearts with right-ventricular hypertrophy (RVH) were studied and noticeable differences were found between the strain and stress distributions for normal volunteers and RVH patients. Compared to the strain energy function approach, our model is more intuitively understandable.

Humans↗

PubMed2002

Human motion planning based on recursive dynamics and optimal control techniques.

This paper presents an efficient optimal control and recursive dynamics-based computer animation system for simulating and controlling the motion of articulated figures. A quasi-Newton nonlinear programming technique (super-linear convergence) is implemented to solve minimum torque-based human motion-planning problems. The explicit analytical gradients needed in the dynamics are derived using a matrix exponential formulation and Lie algebra. Cubic spline functions are used to make the search space for an optimal solution finite. Based on our formulations, our method is well conditioned and robust, in addition to being computationally efficient. To better illustrate the efficiency of our method, we present results of natural looking and physically correct human motions for a variety of human motion tasks involving open and closed loop kinematic chains.

Algorithms↗

PubMed2002

Engineering and algorithm design for an image processing Api: a technical report on ITK--the Insight Toolkit.

We present the detailed planning and execution of the Insight Toolkit (ITK), an application programmers interface (API) for the segmentation and registration of medical image data. This public resource has been developed through the NLM Visible Human Project, and is in beta test as an open-source software offering under cost-free licensing. The toolkit concentrates on 3D medical data segmentation and registration algorithms, multimodal and multiresolution capabilities, and portable platform independent support for Windows, Linux/Unix systems. This toolkit was built using current practices in software engineering. Specifically, we embraced the concept of generic programming during the development of these tools, working extensively with C++ templates and the freedom and flexibility they allow. Software development tools for distributed consortium-based code development have been created and are also publicly available. We discuss our assumptions, design decisions, and some lessons learned.

Algorithms↗