Segmentation of Medical Images under Topological Constraints

Major advances in the field of medical imaging over the past two decades have provided physicians with powerful, non-invasive techniques to probe the structure, function, and pathology of the human body. This increasingly vast and detailed amount of information constitutes a great challenge for the medical imaging community, and requires significant innovations in all aspect of image processing

A Genetic Algorithm for the Topology Correction of Cortical Surfaces

We propose a technique to accurately correct the spherical topology of cortical surfaces. We construct a mapping from the original surface onto the sphere to detect topological defects as minimal nonhomeomorphic regions. A genetic algorithm corrects each defect by finding the maximum-a-posteriori retessellation in a Bayesian framework

A Novel Active Contour Framework. Multi-component Level Set Evolution under Topology Control

We present a novel framework to exert a topology control over a level set evolution. Level set methods offer several advantages over parametric active contours, in particular automated topological changes. In some applications, where some a priori knowledge of the target topology is available, topological changes may not be desirable. A method, based on the concept of simple point borrowed from digital topology, was recently proposed to achieve a strict topology preservation during a level set evolution. However, topologically constrained evolutions often generate topological barriers that lead to large geometric inconsistencies. We introduce a topologically controlled level set framework that greatly alleviates this problem. Unlike existing work, our method allows connected components to merge, split or vanish under some specific conditions that ensure that no topological defects are generated. We demonstrate the strength of our method on a wide range of numerical experiments

apport de rechercheVariational, Geometric and Statistical Methods for Modeling Brain Anatomy and Function

Abstract: We survey the recent activities of the Odyssée Laboratory in the area of the application of mathematics to the design of models for studying brain anatomy and function. We start with the problem of reconstructing sources in MEG and EEG and discuss the variational approach we have developed for solving these inverse problems. This motivates the need for geometric models of the head. We present a method for automatically and accurately extracting surface meshes of several tissues of the head from anatomical MR images. Anatomical connectivity can be extracted from Diffusion Tensor Magnetic Resonance Images but, in the current state of the technology, it must be preceded by a robust estimation and regularization stage. We discuss our work based on variational principles and show how the results can be used to track fibers in the white matter as geodesics in some Riemannian space. We then go to the statistical modeling of fMRI signals from the viewpoint of their decomposition in a pseudo-deterministic and stochastic part which we then use to perform clustering of voxels in a way that is inspired by the theory of Support Vector Machines and in a way that is grounded in information theory. Multimodal image matching is discussed next in th

Variational, Geometric and Statistical Methods for Modeling Brain Anatomy and Function

We survey the recent activities of the Odyss ee Laboratory in the area of the application of mathematics to the design of models for studying brain anatomy and function. We start with the problem of reconstructing sources in MEG and EEG and discuss the variational approach we have developed for solving these inverse problems. This motivates the need for geometric models of the head. We present a method for automatically and accurately extracting surface meshes of several tissues of the head from anatomical MR images. Anatomical connectivity can be extracted from Diffusion Tensor Magnetic Resonance Images but, in the current state of the technology, it must be preceded by a robust estimation and regularization stage. We discuss our work based on variational principles and show how the results can be used to track fibers in the white matter as geodesics in some Riemannian space. We then go to the statistical modeling of fMRI signals from the viewpoint of their decomposition in a pseudo-deterministic and stochastic part which we then use to perform clustering of voxels in a way that is inspired by the theory of Support Vector Machines and in a way that is grounded in information theory. Multimodal image matching is discussed next in the framework of image statistics and Partial Differential Equations with an eye on registering fMRI to the anatomy. The paper ends with a discussion of a new theory of random shapes that may prove useful in building anatomical and functional atlases

Quantitative evaluation of automated skull-stripping methods applied to contemporary and legacy images: Effects of diagnosis, bias correction, and slice location

Performance of automated methods to isolate brain from nonbrain tissues in magnetic resonance (MR) structural images may be influenced by MR signal inhomogeneities, type of MR image set, regional anatomy, and age and diagnosis of subjects studied. The present study compared the performance of four methods: Brain Extraction Tool (BET; Smith [2002]: Hum Brain Mapp 17:143-155); 3dIntracranial (Ward [1999] Milwaukee: Biophysics Research Institute, Medical College of Wisconsin; in AFNI); a Hybrid Watershed algorithm (HWA, Segonne et al. [2004] Neuroimage 22:1060-1075; in FreeSurfer); and Brain Surface Extractor (BSE, Sandor and Leahy [1997] IEEE Trans Med Imag 16:41-54; Shattuck et al. [2001] Neuroimage 13:856-876) to manually stripped images. The methods were applied to uncorrected and bias-corrected datasets; Legacy and Contemporary T-1-weighted image sets; and four diagnostic groups (depressed, Alzheimer's, young and elderly control). To provide a criterion for outcome assessment, two experts manually stripped six sagittal sections for each dataset in locations where brain and nonbrain tissue are difficult to distinguish. Methods were compared on Jaccard similarity coefficients, Hausdorff distances, and an Expectation-Maximization algorithm. Methods tended to perform better on contemporary datasets; bias correction did not significantly improve method performance. Mesial sections were most difficult for all methods. Although AD image sets were most difficult to strip, HWA and BSE were more robust across diagnostic groups compared with 3dIntracranial and BET. With respect to specificity, BSE tended to perform best across all groups, whereas HWA was more sensitive than other methods. The results of this study may direct users towards a method appropriate to their T-1-weighted datasets and improve the efficiency of processing for large, multisite neuroimaging studies