R. Chav

Method for fast and accurate segmentation processing from prior shape: application to femoral head segmentation on x-ray images

This paper proposes a prior shape segmentation method to create a constant-width ribbon-like zone that runs along the boundary to be extracted. The image data corresponding to that zone is transformed into a rectangular image subspace where the boundary is roughly straightened. Every step of the segmentation process is then applied to that straightened subspace image where the final extracted boundary is transformed back into the original image space. This approach has the advantage of producing very efficient filtering and edge detection using conventional techniques. The final boundary is continuous even over image regions where partial information is missing. The technique was applied to the femoral head segmentation where we show that the final segmented boundary is very similar to the one obtained manually by a trained orthopedist and has low sensitivity to the initial positioning of the prior shape.

Surface reconstruction from planar x-ray images using moving least squares

Planar radiographs still are the gold standard for the measurement of the skeletal weight-bearing shape and posture. In this paper, we propose to use an as-rigid-as-possible deformation approach based on moving least squares to obtain 3D personalized bone models from planar x-ray images. Our prototype implementation is capable of performing interactive rate shape editing. The biplane reconstructions of both femur and vertebrae show a good accuracy when compared to CT-scan.

[18F]-fluorodeoxyglucose positron emission tomography of the cat brain: a feasibility study to investigate osteoarthritis-associated pain

The objective of this pilot study was to investigate central nervous system (CNS) changes related to osteoarthritis (OA)-associated chronic pain in cats using [(18)F]-fluorodeoxyglucose ((18)FDG) positron emission tomography (PET) imaging. The brains of five normal, healthy (non-OA) cats and seven cats with pain associated with naturally occurring OA were imaged using (18)FDG-PET during a standardized mild anesthesia protocol. The PET images were co-registered over a magnetic resonance image of a cat brain segmented into several regions of interest. Brain metabolism was assessed in these regions using standardized uptake values. The brain metabolism in the secondary somatosensory cortex, thalamus and periaqueductal gray matter was increased significantly (P ≤ 0.005) in OA cats compared with non-OA cats. This study indicates that (18)FDG-PET brain imaging in cats is feasible to investigate CNS changes related to chronic pain. The results also suggest that OA is associated with sustained nociceptive inputs and increased activity of the descending modulatory pathways.

Coupling 2D/3D registration method and statistical model to perform 3D reconstruction from partial x-rays images data

3D reconstructions of the spine from a frontal and sagittal radiographs is extremely challenging. The overlying features of soft tissues and air cavities interfere with image processing. It is also difficult to obtain information that is accurate enough to reconstruct complete 3D models. To overcome these problems, the proposed method efficiently combines the partial information contained in two images from a patient with a statistical 3D spine model generated from a database of scoliotic patients. The algorithm operates through two simultaneous iterating processes. The first one generates a personalized vertebra model using a 2D/3D registration process with bone boundaries extracted from radiographs, while the other one infers the position and the shape of other vertebrae from the current estimation of the registration process using a statistical 3D model. Experimental evaluations have shown good performances of the proposed approach in terms of accuracy and robustness when compared to CT-scan.

3D shape reconstruction of bone from two x-ray images using 2D/3D non-rigid registration based on moving least-squares deformation

Several studies based on biplanar radiography technologies are foreseen as great systems for 3D-reconstruction applications for medical diagnoses. This paper proposes a non-rigid registration method to estimate a 3D personalized shape of bone models from two planar x-ray images using an as-rigid-as-possible deformation approach based on a moving least-squares optimization method. Based on interactive deformation methods, the proposed technique has the ability to let a user improve readily and with simplicity a 3D reconstruction which is an important step in clinical applications. Experimental evaluations of six anatomical femur specimens demonstrate good performances of the proposed approach in terms of accuracy and robustness when compared to CT-scan.

Guidewire tracking during endovascular neurosurgery

This paper presents a new method for guidewire tracking on fluoroscopic images from endovascular brain intervention. The combination of algorithms chosen can be implemented in real time, so that it can be used in an augmented reality 3D representation to assist physicians performing these interventions. A ribbon-like morphing process combined with a minimal path optimization algorithm is used to track lateral motion between successive frames. Forward motions are then tracked with an endpoint tracking algorithm, based on a circular window processed with the Radon transform. The proposed method was tested on 6 fluoroscopic sequences presenting high-speed motions, which were saved during endovascular brain interventions. The experiments showed above-average precision and robust guidewire tracking, without any permanent error requiring manual correction.

Liver Segmentation on CT and MR Using Laplacian Mesh Optimization

Objective: The purpose of this paper is to describe a semiautomated segmentation method for the liver and evaluate its performance on CT-scan and MR images. Methods: First, an approximate 3-D model of the liver is initialized from a few user-generated contours to globally outline the liver shape. The model is then automatically deformed by a Laplacian mesh optimization scheme until it precisely delineates the patients liver. A correction tool was implemented to allow the user to improve the segmentation until satisfaction. Results: The proposed method was tested against 30 CT-scans from the SLIVER07 challenge repository and 20 MR studies from the Montreal University Hospital Center, covering a wide spectrum of liver morphologies and pathologies. The average volumetric overlap error was 5.1% for CT and 7.6% for MRI and the average segmentation time was 6 min. Conclusion: The obtained results show that the proposed method is efficient, reliable, and could effectively be used routinely in the clinical setting. Significance: The proposed approach can alleviate the cumbersome and tedious process of slice-wise segmentation required for precise hepatic volumetry, virtual surgery, and treatment planning.

Fast 3D reconstruction of the spine from biplanar radiography: a diagnosis tool for routine scoliosis diagnosis and research in biomechanics

Reconstruction methods from biplanar radiography allow a 3D clinical analysis, for patients in standing position, with a low radiation dose. This low-dose and postural imaging modality is thus very interesting for scoliosis clinical diagnosis and research in biomechanics. Nevertheless, such applications require both accurate and fast 3D reconstruction methods. Fast approaches, based on statistical models (Pomero et al. 2004; Gille et al. 2007), allow to obtain an estimate of the spinal 3D reconstruction within 14min. However, this remains too tedious to be used in a clinical routine. The purpose of this study is to propose and evaluate a novel semi-automated 3D reconstruction method of the spine from biplanar radiography. This method relies on a parametric model of the spine using statistical inferences and automatic registration methods based on image processing. Two reconstruction levels are proposed: a first reconstruction level (‘Fast Spine’), providing a fast estimate of the 3D reconstruction and accurate clinical measurements, dedicated to a routine clinical use, and a more accurate second reconstruction level (‘Full Spine’) for applications in biomechanical research.

A computer-based classifier of three-dimensional spinal scoliosis severity

ObjectiveThis article describes a computer-based method for the classification of spine scoliosis severity. This is a first step toward an effective computerized tool to assist general practitioners diagnose spine scoliosis. The method progresses away from Cobb angles toward pattern and magnitude categorization based upon 3D configurations.Materials and methodsThe purpose is to classify spine shapes reconstructed from a pair of calibrated X-ray images into one of three categories, namely, normal spine, moderate scoliosis, and severe scoliosis. The spine shape is represented by the three-dimensional coordinates of a sequence of equidistant points sampled by interpolation on the reconstructed spine shape. Classification is carried out using a self- organizing Kohonen neural network trained using this representation.ResultsThe tests were performed using a database of 174 spine biplane X-rays. The classification accuracy was 97%.ConclusionThe results demonstrate that classification of 3D spine descriptions by a Kohonen neural network affords a solid basis for an effective tool to assist clinicians in assessing scoliosis severity.

Kidney segmentation from a single prior shape in MRI

This paper reports a novel approach to 3D kidney segmentation from a single prior shape in magnetic resonance imaging (MRI) datasets. The proposed method is based on a hierarchic surface deformation algorithm, to generate a pre-personalized model, followed by an anamorphing segmentation algorithm, to extract the kidney capsule. Accuracy and precision are assessed by comparing our method over 20 kidney reconstructions segmented manually by 3 different observers on native MRI images. The experimental results show a volumetric overlap error of 6.39±2.47%, a relative volume difference of 1.87±1.39%, an average symmetric surface distance of 0.80±0.23mm, a root mean squared symmetric distance of 1.03±0.33mm and a maximum symmetric surface distance of 4.18±3.45mm. With our method, the capsules of both kidneys are segment in less than 40 seconds.