Bernard Gibaud

Retrospective evaluation of intersubject brain registration

Although numerous methods to register brains of different individuals have been proposed, no work has been done, as far as we know, to evaluate and objectively compare the performances of different nonrigid (or elastic) registration methods on the same database of subjects. In this paper, we propose an evaluation framework, based on global and local measures of the relevance of the registration. We have chosen to focus more particularly on the matching of cortical areas, since intersubject registration methods are dedicated to anatomical and functional normalization, and also because other groups have shown the relevance of such registration methods for deep brain structures. Experiments were conducted using 6 methods on a database of 18 subjects. The global measures used show that the quality of the registration is directly related to the transformations degrees of freedom. More surprisingly, local measures based on the matching of cortical sulci did not show significant differences between rigid and non rigid methods.

A Virtual Imaging Platform for Multi-Modality Medical Image Simulation

This paper presents the Virtual Imaging Platform (VIP), a platform accessible at http://vip.creatis.insa-lyon.fr to facilitate the sharing of object models and medical image simulators, and to provide access to distributed computing and storage resources. A complete overview is presented, describing the ontologies designed to share models in a common repository, the workίow template used to integrate simulators, and the tools and strategies used to exploit computing and storage resources. Simulation results obtained in four image modalities and with different models show that VIP is versatile and robust enough to support large simulations. The platform currently has 200 registered users who consumed 33 years of CPU time in 2011.

Towards an ontology for sharing medical images and regions of interest in neuroimaging

The goal of the NeuroBase project is to facilitate collaborative research in neuroimaging through a federated system based on semantic web technologies. The cornerstone and focus of this paper is the design of a common semantic model providing a unified view on all data and tools to be shared. For this purpose, we built a multi-layered and multi-components formal ontology. This paper presents two major contributions. The first is related to the general methodology we propose for building an application ontology based on consistent conceptualization choices provided by the DOLCE foundational ontology and core ontologies of domains that we reuse; the second concerns the domain ontology we designed for neuroimaging, which encompasses both the objective nature of image data and the subjective nature of image content, through annotations based on regions of interest made by agents (humans or computer programs). We report on realistic domain use-case queries referring to our application ontology.

3D Reconstruction of Cerebral Blood Vessels

A new technique that uses photogrammetry and computer graphics for the 3D display of cerebral blood vessels provides anatomic images for diagnosis and treatment planning.

Data fusion in medical imaging: merging multimodal and multipatient images, identification of structures and 3D display aspects

Data fusion in medical imaging can be seen into two ways (i) multisensors fusion of anatomical and functional information and (ii) interpatient data fusion by means of warping models. These two aspects set the methodological framework necessary to perform anatomical modelling especially when concerning the modelling of brain structures. The major relevance of the work presented here concerns the interpretation of multimodal 3D neuro-anatomical data bases. Three types of data fusion problems are considered in this paper. The first one concerns the problem of data combination which includes multimodal registration (multisensor fusion applied to CT, MRI, DSA, PET, SPECT, or MEG). In particular, the problem of warping patient data to an anatomical atlas is reviewed and a solution is proposed. The second problem of data fusion addressed in this paper is the identification of anatomical structures by means of image analysis methods. Two techniques have been developed. The first one deals with the analysis of image geometrical features to end up with the determination of a fuzzy mask to label the structure of interest. The second technique consists of labelling major cerebral structures by means of statistical image features associated with relaxation techniques. Finally, the paper presents a review of up to date 3D display techniques with a special emphasis on volume rendering and 3D display of combined data.

Retrospective Evaluation of Inter-subject Brain Registration

Although numerous methods to register brains of different individuals have been proposed, few work has been done to evaluate the performances of different registration methods on the same database of subjects. In this paper, we propose an evaluation framework, based on global and local measures of the quality of the registration. Experiments have been conducted for 5 methods, through a database of 18 subjects. We focused more extensively on the registration of cortical landmarks that have a particular relevance in the context of anatomical-functional normalization. For global measures, results show that the quality of the registration is directly related to the transformations degrees of freedom. However, local measures based on the matching of cortical sulci, did not make it possible to show significant differences between affine and non linear methods.

Model of surgical procedures for multimodal image-guided neurosurgery.

Objective: Improvement of the planning stage of image-guided surgery requires a better anticipation of the surgical procedure and its anatomical and functional environment. This anticipation should be provided by acquisition of multimodal medical images of the patient and by a better understanding of surgical procedures. In this paper, we propose improvements to the planning and performance of multimodal image-guided neurosurgery through the use of information models related to neurosurgical procedures. Materials and Methods: A new generic model of surgical procedures is introduced in the context of multimodal image-guided craniotomies. The basic principle of the model is to break down the surgical procedure into a sequence of steps denning the surgical script. In the model, a step is defined by an action. The model assigns to each surgical step a list of image entities extracted from multimodal preoperative images (i.e., anatomical and/or functional images) which are relevant to the performance of that particular step. A semantic validation of the model was performed by instantiating the model entities for 29 surgical procedures. Results: The resulting generic model is described by a UML class diagram and a textual description. The validation showed the relevance of the model, confirming the main underlying assumptions. It also provided some leads to improve the model. Conclusion: While further validation is needed, the initial benefits of this approach can already be outlined. It should add real value to the different levels of image-guided surgery, from preprocessing to planning, as well as during surgery. Models of surgical procedures can manage image data according to the surgical script, which should lead to better anticipation of surgery through the development of simulation tools. Furthermore, the models may improve the performance of surgery using microscope-based neuronavigation systems by making it possible to adapt both visualization and interaction features of multimodal preoperative images according to the model.

An Anatomic-Based 3D Registration System of Multimodality and Atlas Data in Neurosurgery

The knowledge of patient neuro-anatomy is key information, at least in the understanding of the pathological processes and in the elaboration of precise treatment strategies in neurosurgery. In addition to classical radiology systems like angiography, CT scanner or MRI have greatly contributed to the improvement of the patient anatomy investigation. Each examination modality still carries its own information and the need to make a synthesis between them is obvious but still makes different problems hard to solve. There is no unique imaging facility which can bring out the whole set of known anatomical structures, brought together in a neuro-anatomical atlas. Nevertheless, it is very important for the physician to assign location to these structures from the images delivered by the studies. Only an accurate fusion of these data may help the physician to recognize the precise anatomical structures involved in the therapeutic process he has to set up.

Web ontology language requirements w.r.t expressiveness of taxonomy and axioms in medicine

An important issue is to know whether Web ontology languages, meet the expected requirements of expressiveness and reasoning. This paper aims at contributing to this question in evaluating and comparing several languages. After describing the needs of a Semantic Web in medicine, it analyses Protege and DAML+OIL primitives on a concrete medical ontology, the brain cortex anatomy ontology. It draws conclusions about the requirements that a Web ontology language should meet for the representation of medical taxonomy and axioms. The expressiveness of DAML+OIL or OWL DL seems suited to describe the complex taxonomic knowledge. But rules are required for representing the deductive knowledge (dependencies between relations) and to support several tasks (ontology construction, maintenance, verification, query of heterogeneous distributed information sources). Finally, the paper evaluates the features of the next standard OWL and of an hybrid language CARIN-ALNwith respect to these requirements.

Detection of inter-hemispheric asymmetries of brain perfusion in SPECT

Technetium-99m HMPAO and technetium-99m ECD single photon emission computed tomography (SPECT) imaging is commonly used to highlight brain regions with altered perfusion. It is particularly useful in the investigation of intractable partial epilepsy. However, SPECT suffers from poor spatial resolution that makes interpretation difficult. In this context, we propose an unsupervised voxel neighbourhood based method to assist the detection of significant functional inter-hemispheric asymmetries in brain SPECT, using anatomical information from MRI. For each MRI voxel, the anatomically homologous voxel in the contralateral hemisphere is identified. Both homologous voxel coordinates are then mapped into the SPECT volume using SPECT-MRI registration. Neighbourhoods are then defined around each SPECT voxel and compared to obtain a volume of inter-hemispheric differences. A volume including only the statistically significant inter-hemispheric differences is deduced from this volume using a non-parametric approach. The method was validated using realistic analytical simulated SPECT data including known asymmetries (in size and amplitude) as ground truth (gold standard). Detection performance was assessed using an ROC (receiver operating characteristic) approach based on the measures of the overlap between known and detected asymmetries. Validation with computer-simulated data demonstrates the ability to detect asymmetric zones with relatively small extension and amplitude. The registration of these detected functional asymmetries on the MRI enables good anatomical localization to be achieved.