Jérôme Schmid

MRI Bone Segmentation Using Deformable Models and Shape Priors

This paper addresses the problem of automatically segmenting bone structures in low resolution clinical MRI datasets. The novel aspect of the proposed method is the combination of physically-based deformable models with shape priors. Models evolve under the influence of forces that exploit image information and prior knowledge on shape variations. The prior defines a Principal Component Analysis (PCA) of global shape variations and a Markov Random Field (MRF) of local deformations, imposing spatial restrictions in shapes evolution. For a better efficiency, various levels of details are considered and the differential equations system is solved by a fast implicit integration scheme. The result is an automatic multilevel segmentation procedure effective with low resolution images. Experiments on femur and hip bones segmentation from clinical MRI depict a promising approach (mean accuracy: 1.44 +/- 1.1 mm, computation time: 2 mn 43 s).

Robust Statistical Shape Models for MRI Bone Segmentation in Presence of Small Field of View

Accurate bone modeling from medical images is essential in the diagnosis and treatment of patients because it supports the detection of abnormal bone morphology, which is often responsible for many musculoskeletal diseases (MSDs) of human articulations. In a clinical setting, images of the suspected joints are acquired in a high resolution but with a small field of view (FOV) in order to maximize the image quality while reducing acquisition time. However bones are only partially visible in such small FOVs. This presents difficult challenges in automated bone segmentation and thus limits the application of sophisticated algorithms such as statistical shape models (SSM), which have been generally proven to be an efficient technique for bone segmentation. Indeed, the reduced image information affects the initialization and evolution of these deformable model-based approaches. In this paper, we present a robust multi-resolution SSM algorithm with an adapted initialization to address the segmentation of MRI bone images acquired in small FOVs for modeling and computer-aided diagnosis. Our innovation stems from the derivation of a robust SSM based on complete and corrupted shapes, as well as from a simultaneous optimization of transformation and shape parameters to yield an efficient initialization technique. We demonstrate our segmentation algorithm using 86 clinical MRI images of the femur and hip bones. These images have a varied resolution and limited FOVs. The results of our segmentation (e.g., average distance error of 1.12 ± 0.46 mm) are within the needs of image-based clinical diagnosis.

A GPU framework for parallel segmentation of volumetric images using discrete deformable models

Despite the ability of current GPU processors to treat heavy parallel computation tasks, its use for solving medical image segmentation problems is still not fully exploited and remains challenging. A lot of difficulties may arise related to, for example, the different image modalities, noise and artifacts of source images, or the shape and appearance variability of the structures to segment. Motivated by practical problems of image segmentation in the medical field, we present in this paper a GPU framework based on explicit discrete deformable models, implemented over the NVidia CUDA architecture, aimed for the segmentation of volumetric images. The framework supports the segmentation in parallel of different volumetric structures as well as interaction during the segmentation process and real-time visualization of the intermediate results. Promising results in terms of accuracy and speed on a real segmentation experiment have demonstrated the usability of the system.

Musculoskeletal Simulation Model Generation from MRI Data Sets and Motion Capture Data

Today computer models and computer simulations of the musculoskeletal system are widely used to study the mechanisms behind human gait and its disorders. The common way of creating musculoskeletal models is to use a generic musculoskeletal model based on data derived from anatomical and biomechanical studies of cadaverous specimens. To adapt this generic model to a specific subject, the usual approach is to scale it. This scaling has been reported to introduce several errors because it does not always account for subject-specific anatomical differences. As a result, a novel semi-automatic workflow is proposed that creates subject-specific musculoskeletal models from magnetic resonance imaging (MRI) data sets and motion capture data. Based on subject-specific medical data and a model-based automatic segmentation approach, an accurate modeling of the anatomy can be produced while avoiding the scaling operation. This anatomical model coupled with motion capture data, joint kinematics information, and muscle-tendon actuators is finally used to create a subject-specific musculoskeletal model.

Virtual Hip Joint: from Computer Graphics to Computer-Assisted Diagnosis

Osteoarthritis (OA) is a major musculoskeletal disorder which causes are not always fully understood. Femoroacetabular impingements such as cam/ pincer cannot always explain observed OA in hips with normal morphology. This paper investigates the hypothesis of extreme repetitive movements as a source of cartilage degeneration. We present a clinical study conducted with professional ballet dancers and a methodology to perform functional simulations of the hip joint in extreme postures. Throughout the process, various computer graphics techniques are used, like motion capture, 3D body scanning and physically-based models. In addition to accelerate and strengthen some tasks, these techniques strongly participate in the clinical understanding of OA related to motion. Preliminary results have indeed shown a significant correlation between the location of impingements and radiologically observed damage zones in the labrum cartilage.

Analysis of hip range of motion in everyday life: a pilot study

Patients undergoing total hip arthroplasty are increasingly younger and have a higher demand concerning hip range of motion. To date, there is no clear consensus as to the amplitude of the “normal hip” in everyday life. It is also unknown if the physical examination is an accurate test for setting the values of true hip motion. The purpose of this study was: 1) to precisely determine the necessary hip joint mobility for everyday tasks in young active subjects to be used in computer simulations of prosthetic models in order to evaluate impingement and instability during their practice; 2) to assess the accuracy of passive hip range of motion measurements during clinical examination. A total of 4 healthy volunteers underwent Magnetic Resonance Imaging and 2 motion capture experiments. During experiment 1, routine activities were recorded and applied to prosthetic hip 3D models including nine cup configurations. During experiment 2, a clinical examination was performed, while the motion of the subjects was simultaneously captured. Important hip flexion (mean range 95°-107°) was measured during daily activities that could expose the prosthetic hip to impingement and instability. The error made by the clinicians during physical examination varied in the range of ±10°, except for flexion and abduction where the error was higher. This study provides useful information for the surgical planning to help restore hip mobility and stability, when dealing with young active patients. The physical examination seems to be a precise method for determining passive hip motion, if care is taken to stabilise the pelvis during hip flexion and abduction.

Multimedia application to the simulation of human Musculoskeletal system: A visual lower limb model from multimodal captured data

Multimedia has been widely applied in life science research and clinical applications, including research on musculoskeletal system. Musculoskeletal disorders are the most notorious and common causes of severe long-term pain and physical disability, affecting hundreds of millions of people across the world. In the European Research Project 3D Anatomical Human (http:// 3dah.miralab.unige.ch/), we are building an accurate generic lower limb model that can be simulated in motion, using individual multimodal medical data. For clinical every-day use, medically relevant validation and an efficient visualization and interaction framework are required. We are defining a generic functional model of the lower limb (consisting of bones and soft-tissues) that can be simulated in motion. Relevant patientpsilas anatomical, kinematical and mechanical data extracted from images (MRI, CT), motion capture (dynamic MRI, optical motion capture) and other modalities (EMG, mechanical properties measuring device), as well as statistical data, are being adjust the generic model to the patient. A fully functional model will be presented with many individual cases study and medical validation.

Segmentation of X-ray Images by 3D-2D Registration Based on Multibody Physics

X-ray imaging is commonly used in clinical routine. In radiotherapy, spatial information is extracted from X-ray images to correctly position patients before treatment. Similarly, orthopedic surgeons assess the positioning and migration of implants after Total Hip Replacement (THR) with X-ray images. However, the projective nature of X-ray imaging hinders the reliable extraction of rigid structures in X-ray images, such as bones or metallic components. We developed an approach based on multibody physics that simultaneously registers multiple 3D shapes with one or more 2D X-ray images. Considered as physical bodies, shapes are driven by image forces, which exploit image gradient, and constraints, which enforce spatial dependencies between shapes. Our method was tested on post-operative radiographs of THR and thoroughly validated with gold standard datasets. The final target registration error was in average \(0.3\pm 0.16\) mm and the capture range improved more than 40 % with respect to reference registration methods.

Sensitivity of hip tissues contact evaluation to the methods used for estimating the hip joint center of rotation

Computer-based simulations of human hip joints generally include investigating contacts happening among soft or hard tissues during hip movement. In many cases, hip movement is approximated as rotation about an estimated hip center. In this paper, we investigate the effect of different methods used for estimating hip joint center of rotation on the results acquired from hip simulation. For this reason, we use three dimensional models of hip tissues reconstructed from MRI datasets of 10 subjects, and estimate their center of rotation by applying five different methods (including both predictive and functional approaches). Then, we calculate the amount of angular and radial penetrations that happen among three dimensional meshes of cartilages, labrum, and femur bone, when hip is rotating about different estimated centers of rotation. The results indicate that hip simulation can be highly affected by the method used for estimating hip center of rotation. However, under some conditions (e.g. when Adduction or External Rotation are considered) we can expect to have a more robust simulation. In addition, it was observed that applying some methods (e.g. the predictive approach based on acetabulum) may result in less robust simulation, comparing to the other methods.

Collaborative telemedicine for interactive multiuser segmentation of volumetric medical images

Telemedicine has evolved rapidly in recent years to enable unprecedented access to digital medical data, such as with networked image distribution/sharing and online (distant) collaborative diagnosis, largely due to the advances in telecommunication and multimedia technologies. However, interactive collaboration systems which control editing of an object among multiple users are often limited to a simple “locking” mechanism based on a conventional client/server architecture, where only one user edits the object which is located in a specific server, while all other users become viewers. Such systems fail to provide the needs of a modern day telemedicine applications that demand simultaneous editing of the medical data distributed in diverse local sites. In this study, we introduce a novel system for telemedicine applications, with its application to an interactive segmentation of volumetric medical images. We innovate by proposing a collaborative mechanism with a scalable data sharing architecture which makes users interactively edit on a single shared image scattered in local sites, thus enabling collaborative editing for, e.g., collaborative diagnosis, teaching, and training. We demonstrate our collaborative telemedicine mechanism with a prototype image editing system developed and evaluated with a user case study. Our result suggests that the ability for collaborative editing in a telemedicine context can be of great benefit and hold promising potential for further research.