Piecewise Affine Registration of Biological Images for Volume Reconstruction

This manuscript tackles the reconstruction of 3D volumes via mono-modal registration of series of 2D biological images (histological sections, autoradiographs, cryosections, etc.). The process of acquiring these images typically induces composite transformations that we model as a number of rigid or affine local transformations embedded in an elastic one. We propose a registration approach closely derived from this model. Given a pair of input images, we first compute a dense similarity field between them with a block matching algorithm. We use as a similarity measure an extension of the classical correlation coefficient that improves the consistency of the field. A hierarchical clustering algorithm then automatically partitions the field into a number of classes from which we extract independent pairs of sub-images. Our clustering algorithm relies on the Earth mover’s distribution metric and is additionally guided by robust least-square estimation of the transformations associated with each cluster. Finally, the pairs of sub-images are, independently, affinely registered and a hybrid affine/non-linear interpolation scheme is used to compose the output registered image. We investigate the behavior of our approach on several batches of histological data and discuss its sensitivity to parameters and noise

Surface reconstruction using active contour models

Variational methods have been frequently used for surface reconstruction and contour extraction (snakes). We present a surface reconstruction method where we assume the surface composed of two regions of different types of smoothness. One region of the surface models a "lake" (constant height region with uphill borders). It is surrounded by the other background region which is reconstructed using classic surface regularization. The boundary between the two regions, represented by a closed curve is determined with the help of an active contour model. Then the surface is reconstructed by minimizing the energy terms in each region. Minimizing a global energy defined on the couple of unknowns - boundary curve and surface - permits to introduce other forces on the curve. The surface reconstruction and contour extraction tasks are then made together. We have applied this model for segmenting a synthetic Digital Terrain Model (DTM) image which represents a noisy mountain and lake

A parametric deformable model to fit unstructured 3D data

International audienceIn many computer vision and image understanding problems, it is important to find a smooth surface that fits a set of given unstructured 3D data. Although approaches based on general deformable models give satisfactory results, in particular a local description of the surface, they involve large linear systems to solve when dealing with high resolution 3D images. The advantage of parametric deformable templates like superquadrics is their small number of parameters to describe a shape. However, the set of shapes described by superquadrics is too limited to approximate precisely complex surfaces. This is why hybrid models have been introduced to refine the initial approximation. This article introduces a deformable superquadric model based on a superquadric fit followed by a free-form deformation (FFD) to fit unstructured 3D points. At the expense of a reasonable number of additional parameters, free-form deformations provide a much closer fit and a volumetric deformation field. We first present the mathematical and algorithmic details of the method. Then, since we are mainly concerned with applications for medical images, we present a medical application consisting in the reconstruction of the left ventricle of the heart from a number of various 3D cardiac images. The extension of the method to track anatomical structures in spatio-temporal images (4D data) is presented in a companion article

Intra-operative Registration for Deep Brain Stimulation Procedures based on a Full Physics Head Model

International audienceBrain deformation is a factor of inaccuracy during stereotactic neurosurgeries. If this phenomenon is not considered in the pre-operative planning or intra-operatively, it could lead to surgical complications, side effects or ineffectiveness. In this paper, we present a patient-specific method to update the pre-operative planning based on a physical simulation of the brain shift. A minimization process estimates parameters of the simulation in order to compute the brain tissue deformation matching the partial data taken from intra-operative modalities. The simulation is based on a patient-specific biomechanical model of the brain and the cerebro-spinal fluid. We validate the method on a patient with a post-operative MRI

Iconic-Geometric Nonlinear Registration of a Basal Ganglia Atlas for Deep Brain Stimulation Planning

International audienceThis paper evaluates a nonlinear registration method for warping a 3D histological atlas of the basal ganglia into patient data for deep brain stimulation (DBS) planning. The power of the method is the possibility to combine iconic registration with geometric constraints un-der a unified diffeomorphic framework. This combination aims to ensure robust and accurate atlas-to-patient warping and anatomy-preserving de-formations of stimulation target nuclei. A comparison of the method with a state-of-the-art diffeomorphic registration algorithm reveals how each approach deforms low-contrasted image regions where DBS target nuclei often lie. The technique is applied to T1-weighted magnetic resonance images from a cohort of Parkinsonian subjects, including subjects with standard-size and large ventricles. Results illustrate the effects of iconic or geometric registration alone, as well as how both constraints can be integrated in order to contribute for registration precision enhancement

Clinical impairment in premanifest and early Huntington's disease is associated with regionally specific atrophy.

TRACK-HD is a multicentre longitudinal observational study investigating the use of clinical assessments and 3-Tesla magnetic resonance imaging as potential biomarkers for future therapeutic trials in Huntington's disease (HD). The cross-sectional data from this large well-characterized dataset provide the opportunity to improve our knowledge of how the underlying neuropathology of HD may contribute to the clinical manifestations of the disease across the spectrum of premanifest (PreHD) and early HD. Two hundred and thirty nine gene-positive subjects (120 PreHD and 119 early HD) from the TRACK-HD study were included. Using voxel-based morphometry (VBM), grey and white matter volumes were correlated with performance in four domains: quantitative motor (tongue force, metronome tapping, and gait); oculomotor [anti-saccade error rate (ASE)]; cognition (negative emotion recognition, spot the change and the University of Pennsylvania smell identification test) and neuropsychiatric measures (apathy, affect and irritability). After adjusting for estimated disease severity, regionally specific associations between structural loss and task performance were found (familywise error corrected, P < 0.05); impairment in tongue force, metronome tapping and ASE were all associated with striatal loss. Additionally, tongue force deficits and ASE were associated with volume reduction in the occipital lobe. Impaired recognition of negative emotions was associated with volumetric reductions in the precuneus and cuneus. Our study reveals specific associations between atrophy and decline in a range of clinical modalities, demonstrating the utility of VBM correlation analysis for investigating these relationships in HD

Automated Analysis of Basal Ganglia Intensity Distribution in Multisequence MRI of the Brain - Application to Creutzfeldt-Jakob Disease

We present a method for the analysis of basal ganglia (including the thalamus) for accurate detection of human spongiform encephalopathy in multisequence MRI of the brain. One common feature of most forms of prion protein infections is the appearance of hyperintensities in the deep grey matter area of the brain in T2-weighted MR images. We employ T1, T2 and Flair-T2 MR sequences for the detection of intensity deviations in the internal nuclei. First, the MR data is registered to a probabilistic atlas and normalised in intensity. Then smoothing is applied with edge enhancement. The segmentation of hyperintensities is performed using a model of the human visual system. For more accurate results, a priori anatomical data from a segmented atlas is employed to refine the registration and remove false positives. The results are robust over the patient data and in accordance to the clinical ground truth. Our method further allows the quantification of intensity distributions in basal ganglia. The caudate nuclei are highlighted as main areas of diagnosis of sporadic Creutzfeldt-Jakob Disease (CJD), in agreement with the histological data. The algorithm permitted to classify the intensities of abnormal signals in sporadic CJD patient FLAIR images with a more significant hypersignal in caudate nuclei (10/10) and putamen (6/10) than in thalami. Using normalised measures of the intensity relations between the internal grey nuclei of patients, we robustly differentiate sporadic CJD and new-variant CJD patients, as a first attempt towards an automatic classification tool of human spongiform encephalopathies

What do brain endocasts tell us? A comparative analysis of the accuracy of sulcal identification by experts and perspectives in palaeoanthropology

Palaeoneurology is a complex field as the object of study, the brain, does not fossilize. Studies rely therefore on the (brain) endocranial cast (often named endocast), the only available and reliable proxy for brain shape, size and details of surface. However, researchers debate whether or not specific marks found on endocasts correspond reliably to particular sulci and/or gyri of the brain that were imprinted in the braincase. The aim of this study is to measure the accuracy of sulcal identification through an experiment that reproduces the conditions that palaeoneurologists face when working with hominin endocasts. We asked 14 experts to manually identify well-known foldings in a proxy endocast that was obtained from an MRI of an actual in vivo Homo sapiens head. We observe clear differences in the results when comparing the non-corrected labels (the original labels proposed by each expert) with the corrected labels. This result illustrates that trying to reconstruct a sulcus following the very general known shape/position in the literature or from a mean specimen may induce a bias when looking at an endocast and trying to follow the marks observed there. We also observe that the identification of sulci appears to be better in the lower part of the endocast compared to the upper part. The results concerning specific anatomical traits have implications for highly debated topics in palaeoanthropology. Endocranial description of fossil specimens should in the future consider the variation in position and shape of sulci in addition to using models of mean brain shape. Moreover, it is clear from this study that researchers can perceive sulcal imprints with reasonably high accuracy, but their correct identification and labelling remains a challenge, particularly when dealing with extinct species for which we lack direct knowledge of the brain