Showing papers by "Miguel Ángel González Ballester published in 2007"

Automated bone contour detection in ultrasound B-mode images for minimally invasive registration in computer-assisted surgery-an in vitro evaluation.

Abstract: Background Minimally invasive surgical interventions performed using computer-assisted surgery (CAS) systems require reliable registration methods for pre-operatively acquired patient anatomy representations that are compatible with the minimally invasive paradigm. The use of brightness-mode ultrasound seems to be promising, if associated devices work in a computationally efficient and fully automatic manner. Methods This paper presents a rapid and fully automatic segmentation approach for ultrasound B-mode images capable of detecting echoes from bony structures. The algorithm focuses on the precise and rapid detection of bone contours usable for minimally invasive registration. The article introduces the image-processing scheme and a set-up enabling a direct comparison between manually digitized reference points and the segmented bone contours. The segmentation accuracy was assessed using cadaveric material. Results The experimental evaluation revealed results in the same order of magnitude as a pointer-based surface digitization procedure. Conclusion The suggested segmentation approach provides a reliable means of detecting bony surface patches in ultrasound images. Copyright © 2007 John Wiley & Sons, Ltd.

Accurate and robust reconstruction of proximal femur from sparse intraoperative data and dense point distribution model for surgical navigation

Abstract: Constructing a 3D surface model from sparse-point data is a nontrivial task. Here, we report an accurate and robust approach for reconstructing a surface model of the proximal femur from sparse-point data and a dense-point distribution model (DPDM). The problem is formulated as a three-stage optimal estimation process. The first stage, affine registration, is to iteratively estimate a scale and a rigid transformation between the mean surface model of the DPDM and the sparse input points. The estimation results of the first stage are used to establish point correspondences for the second stage, statistical instantiation, which stably instantiates a surface model from the DPDM using a statistical approach. This surface model is then fed to the third stage, kernel-based deformation, which further refines the surface model. Handling outliers is achieved by consistently employing the least trimmed squares (LTS) approach with a roughly estimated outlier rate in all three stages. If an optimal value of the outlier rate is preferred, we propose a hypothesis testing procedure to automatically estimate it. We present here our validations using four experiments, which include 1 leave-one-out experiment, 2 experiment on evaluating the present approach for handling pathology, 3 experiment on evaluating the present approach for handling outliers, and 4 experiment on reconstructing surface models of seven dry cadaver femurs using clinically relevant data without noise and with noise added. Our validation results demonstrate the robust performance of the present approach in handling outliers, pathology, and noise. An average 95-percentile error of 1.7-2.3 mm was found when the present approach was used to reconstruct surface models of the cadaver femurs from sparse-point data with noise added.

Accurate and Robust Reconstruction of a Surface Model of the Proximal Femur From Sparse-Point Data and a Dense-Point Distribution Model for Surgical Navigation

Abstract: Constructing a 3D surface model from sparse-point data is a nontrivial task. Here, we report an accurate and robust approach for reconstructing a surface model of the proximal femur from sparse-point data and a dense-point distribution model (DPDM). The problem is formulated as a three-stage optimal estimation process. The first stage, affine registration, is to iteratively estimate a scale and a rigid transformation between the mean surface model of the DPDM and the sparse input points. The estimation results of the first stage are used to establish point correspondences for the second stage, statistical instantiation, which stably instantiates a surface model from the DPDM using a statistical approach. This surface model is then fed to the third stage, kernel-based deformation, which further refines the surface model. Handling outliers is achieved by consistently employing the least trimmed squares (LTS) approach with a roughly estimated outlier rate in all three stages. If an optimal value of the outlier rate is preferred, we propose a hypothesis testing procedure to automatically estimate it. We present here our validations using four experiments, which include 1 leave-one-out experiment, 2 experiment on evaluating the present approach for handling pathology, 3 experiment on evaluating the present approach for handling outliers, and 4 experiment on reconstructing surface models of seven dry cadaver femurs using clinically relevant data without noise and with noise added. Our validation results demonstrate the robust performance of the present approach in handling outliers, pathology, and noise. An average 95-percentile error of 1.7-2.3 mm was found when the present approach was used to reconstruct surface models of the cadaver femurs from sparse-point data with noise added.

Unsupervised reconstruction of a patient-specific surface model of a proximal femur from calibrated fluoroscopic images

Abstract: In this paper, we present an unsupervised 2D/3D reconstruction scheme combining a parameterized multiple-component geometrical model and a point distribution model, and show its application to automatically reconstruct a surface model of a proximal femur from a limited number of calibrated fluoroscopic images with no user intervention at all. The parameterized multiple-component geometrical model is regarded as a simplified description capturing the geometrical features of a proximal femur. Its parameters are optimally and automatically estimated from the input images using a particle filter based inference method. The estimated geometrical parameters are then used to initialize a point distribution model based 2D/3D reconstruction scheme for an accurate reconstruction of a surface model of the proximal femur. We designed and conducted in vitro and in vivo experiments to compare the present unsupervised reconstruction scheme to a supervised one. An average mean error of 1.2 mm was found when the supervised reconstruction scheme was used. It increased to 1.3 mm when the unsupervised one was used. However, the unsupervised reconstruction scheme has the advantage of elimination of user intervention, which holds the potential to facilitate the application of the 2D/3D reconstruction in surgical navigation.

Deformable Atlases for the Segmentation of Internal Brain Nuclei in Magnetic Resonance Imaging

Abstract: Magnetic resonance imaging (MRI) is commonly employed for the depiction of soft tissues, most notably the human brain. Computer-aided image analysis techniques lead to image enhancement and automatic detection of anatomical structures. However, the information contained in images does not often offer enough contrast to robustly obtain a good detection of all internal brain structures, not least the deep grey matter nuclei. We propose a method that incorporates prior anatomical knowledge in the shape of digital atlases that deform to fit the image data to be analysed. Our technique is based on a combination of rigid, affine and non-rigid registration, segmentation of key anatomical landmarks and propagation of the information of the atlas to detect deep grey matter nuclei. The Montreal Neurological Institute (MNI) and Zubal atlases are employed. Results show that detecting important structures such as the ventricles and brain outlines greatly improves the results. Our method is fully automatic.

Statistical shape analysis via principal factor analysis

Abstract: Statistical shape analysis techniques commonly employed in the medical imaging community, such as active shape models or active appearance models, rely on principal component analysis (PCA) to decompose shape variability into a reduced set of interpretable components. In this paper we propose principal factor analysis (PFA) as an alternative and complementary tool to PCA providing a decomposition into modes of variation that can be more easily interpretable, while still being a linear efficient technique that performs dimensionality reduction (as opposed to independent component analysis, ICA). The key difference between PFA and PCA is that PFA models covariance between variables, rather than the total variance in the data. The added value of PFA is illustrated on 2D landmark data of corpora callosa outlines. Then, a study of the 3D shape variability of the human left femur is performed. Finally, we report results on vector-valued 3D deformation fields resulting from non-rigid registration of ventricles in MRI of the brain.

Automatic Extraction of Femur Contours from Calibrated X-Ray Images using Statistical Information

Abstract: Automatic identification and extraction of bone contours from x-ray images is an essential first step task for further medical image analysis. In this paper we propose a 3D statistical model based framework for the proximal femur contour extraction from calibrated x-ray images. The automatic initialization to align the 3D model with the x-ray images is solved by an Estimation of Bayesian Network Algorithm to fit a simplified multiple component geometrical model of the proximal femur to the x-ray data. Landmarks can be extracted from the geometrical model for the initialization of the 3D statistical model. The contour extraction is then accomplished by a joint registration and segmentation procedure. We iteratively updates the extracted bone contours and an instanced 3D model to fit the x-ray images. Taking the projected silhouettes of the instanced 3D model on the registered x-ray images as templates, bone contours can be extracted by a graphical model based Bayesian inference. The 3D model can then be updated by a non-rigid 2D/3D registration between the 3D statistical model and the extracted bone contours. Preliminary experiments on clinical data sets verified its validity.

Design and clinical evaluation of an image-guided surgical microscope with an integrated tracking system

Abstract: A new image-guided microscope system using augmented reality image overlays has been developed. With this system, CT cut-views and segmented objects such as tumors that have been previously extracted from preoperative tomographic images can be directly displayed as augmented reality overlays on the microscope image. The novelty of this design stems from the inclusion of a precise mini-tracker directly on the microscope. This device, which is rigidly mounted to the microscope, is used to track the movements of surgical tools and the patient. In addition to an accuracy gain, this setup offers improved ergonomics since it is much easier for the surgeon to keep an unobstructed line of sight to tracked objects. We describe the components of the system: microscope calibration, image registration, tracker assembly and registration, tool tracking, and augmented reality display. The accuracy of the system has been measured by validation on plastic skulls and cadaver heads, obtaining an overlay error of 0.7 mm. In addition, a numerical simulation of the system has been done in order to complement the accuracy study, showing that the integration of the tracker onto the microscope could lead to an improvement of the accuracy to the order of 0.5 mm. Finally, we describe our clinical experience using the system in the operation room, where three operations have been performed to date.

Correspondence Establishment in Statistical Modeling of Shapes with Arbitrary Topology

Abstract: Correspondence establishment is a key step in statistical shape model building. There are several automated methods for solving this problem in 3D, but they usually can only handle objects with simple topology, like that of a sphere or a disc. We propose an extension to correspondence establishment over a population based on the optimization of the minimal description length function, allowing considering objects with arbitrary topology. Instead of using a fixed structure of kernel placement on a sphere for the systematic manipulation of point landmark positions, we rely on an adaptive, hierarchical organization of surface patches. This hierarchy can be built on surfaces of arbitrary topology and the resulting patches are used as a basis for a consistent, multi-scale modification of the surfaces' parameterization, based on point distribution models. The feasibility of the approach is demonstrated on synthetic models with different topologies.

An integrated approach for reconstructing surface models of the proximal femur from sparse input data for surgical navigation

Abstract: A patient-specific surface model of the proximal femur plays an important role in planning and supporting various computer-assisted surgical procedures including total hip replacement, hip resurfacing, and osteotomy of the proximal femur. The common approach to derive 3D models of the proximal femur is to use imaging techniques such as computed tomography (CT) or magnetic resonance imaging (MRI). However, the high logistic effort, the extra radiation (CT-imaging), and the large quantity of data to be acquired and processed make them less functional. In this paper, we present an integrated approach using a multi-level point distribution model (ML-PDM) to reconstruct a patient-specific model of the proximal femur from intra-operatively available sparse data. Results of experiments performed on dry cadaveric bones using dozens of 3D points are presented, as well as experiments using a limited number of 2D X-ray images, which demonstrate promising accuracy of the present approach.

Clustering of deformation modes for quantitative evaluation of statistical shape models

Abstract: Introduction: Factor Analysis (FA) techniques used in statistical shape modelling have proven to be useful to improve efficacy and accuracy in computer-assisted orthopaedic surgery applications (e.g., [1, 2]). One aspect that currently prevents full exploitation of these techniques is their evaluation, with current analyses being based in more or less intuitive aspects (e.g., visual inspection), the outcome is dependent on the observer. Furthermore, the 3D characteristic of the data makes difficult the analysis of results, which is important when one wants to compare results from different FA-based techniques [3]. In this work we present a technique to segment regions that present homogeneous or similar direction of deformation, in an effort to characterize and quantify results from factor analysis techniques. The clusterization technique is based on the minimization of an energy composed by two terms: a first term based on the colinearity between point directions and the preferred direction of a given cluster (a concept similar to the average direction across the cluster), and a second term that considers the area gain when adding a candidate point to a cluster. A weighting parameter allows to adjust the trade-off between the amount of colinearity one searches across a cluster and its area growth. The method is inspired in the work presented in [4], which was built for vector field segmentation of moving objects. In addition, following some key ideas presented in [5] the method is adapted to an unstructured 3D displacement vector field across a surface.