Gérard Subsol

Reconstructing a 3D structure from serial histological sections

Abstract We consider the problem of aligning histological sections for 3D reconstruction and analysis. The method we propose is based on a block-matching strategy that allows us to compute local displacements between the sections. We then collect these local measures to estimate a rigid transformation. Our emphasis is on the necessity to use a robust approach for this estimation step. The process is integrated within a multi-scale scheme to improve both accuracy and computation time. We prove experimentally that we can reach sub-pixel accuracy and we show some results of aligning histological sections from a rats brain and a rhesus monkeys brain.

Automatic detection and segmentation of evolving processes in 3D medical images: Application to multiple sclerosis

The study of temporal series of medical images can be helpful for physicians to perform pertinent diagnoses and to help them in the follow-up of a patient: in some diseases, lesions, tumors or anatomical structures vary over time in size, position, composition, etc., either because of a natural pathological process or under the effect of a drug or a therapy. It is a laborious and subjective task to visually and manually analyze such images. Thus the objective of this work was to automatically detect regions with apparent local volume variation with a vector field operator applied to the local displacement field obtained after a non-rigid registration between two successive temporal images. On the other hand, quantitative measurements, such as the volume variation of lesions or segmentation of evolving lesions, are important. By studying the information of apparent shrinking areas in the direct and reverse displacement fields between images, we are able to segment evolving lesions. Then we propose a method to segment lesions in a whole temporal series of images. In this article we apply this approach to automatically detect and segment multiple sclerosis lesions that evolve in time series of MRI scans of the brain. At this stage, we have only applied the approach to a few experimental cases to demonstrate its potential. A clinical validation remains to be done, which will require important additional work.

A scheme for automatically building three-dimensional morphometric anatomical atlases: Application to a skull atlas

We present a general scheme for automatically building a morphometric anatomical atlas. We detail each stage of the method, including the non-rigid registration algorithm, three-dimensional line averaging and statistical processes. We apply the method to obtain a quantitative atlas of skull crest lines. Finally, we use the resulting atlas to study a craniofacial disease; we show how we can obtain qualitative and quantitative results by contrasting a skull affected by a mandible deformation with the atlas.

Automatic Retrieval of Anatomical Structures in 3D Medical Images

This paper describes a method to automatically generate the mapping between a completely labeled reference image and 3D medical images of patients. To achieve this, we combined three techniques: the extraction of 3D feature lines, their non-rigid registration and the extension of the deformation to the whole image space using warping techniques. As experimental results, we present the retrieval of the cortical and ventricles structures in MRI images of the brain.

Automatic analysis of cerebral atrophy

3D MR data obtained for 10 healthy control subjects have been used to build a brain atlas. The atlas is built in four stages. First, a set of features that are unambiguously definable and anatomically relevant need to be computed for each item in the database. The chosen features are crest lines along which the maximal principal curvature of the surface of the brain is maximal in its associated principal direction. Second, a nonrigid registration algorithm is used to determine the common crest lines among the subjects in the database. These crest lines form the structure of the atlas. Third, a set of crest lines is taken as a reference set and a modal analysis is performed to determine the fundamental deformations that are necessary to bring the individual data in line with the reference set. The deformations are averaged and the set of mean crest lines becomes the atlas. Finally, the standard deviation of the deformations between the atlas and the items in the database defines the normal variation in the relative positions of the crest lines in a healthy population. In a fully automatic procedure, the crest lines on the surface of the brain adjacent to the cerebral ventricles in a patient with primary progressive aphasia were compared to the atlas; confirmation that the brain of this patient demonstrates atrophy was provided by stereological analysis that showed that the volume of the left cerebral hemisphere is 48.8 ml (CE 2.8%) less than the volume of the right cerebral hemisphere in the region of the temporal and frontal lobes. When the amplitude of the deformations necessary to register the crest lines obtained for the patient with the atlas were greater than three standard deviations beyond the variability inherent in the atlas, the deformation was considered significant. Four of the main deformation modes of the longest crest line of the surface of the brain adjacent to the cerebral ventricles were significantly different in the patient with primary progressive aphasia compared to the atlas. The ventricles are preferentially enlarged in the left cerebral hemisphere. Furthermore, they are closer together posteriorly and further apart anteriorly than in the atlas. These observations may be indicative of the atrophy of the temporal and frontal lobes of the left cerebral hemisphere noted in the patient. Ultimately, the approach may provide a useful screening technique for identifying brain diseases involving cerebral atrophy. Serial studies of individual patients may provide insights into the processes controlling or affected by particular disease.

Craniofacial surgery simulation testbed

We present a craniofacial surgery simulation testbed that makes extensive use of virtual reality techniques. The skull, skin and fat tissues are represented with simplex meshes, that are characterized with a constant vertex to vertex connectivity. Surfaces and volumes are respectively described as three and four connected meshes. This representation is well suited for the implementation of surface deformations such as those exerted on the face skin under the action of fat tissues. Furthermore, cutting surface regions may be easily achieved due to the local nature of simplex meshes. The user proceeds by cutting skull fragments and reorganizing them with the help of a virtual hand. Fat tissue attached to both skin and skull adjusts the face shape to the reconstructed skull.

Automatic Detection and Segmentation of Evolving Processes in 3D Medical Images: Application to Multiple Sclerosis

Physicians often perform diagnoses based on the evolution of lesions, tumors or anatomical structures through time. The objective of this paper is to automatically detect regions with apparent local volume variation with a vector field operator applied to the local displacement field obtained after a non-rigid registration between successive temporal images. In studying the information of apparent shrinking areas in the direct and reverse displacement fields between images, we are able to segment evolving lesions. Then we propose a method to segment lesions in a whole temporal series of images. In this paper we apply this approach to the automatic detection and segmentation of multiple sclerosis lesions in time series of MRI images of the brain.

Application of an Automatically Built 3D Morphometric Brain Atlas: Study of Cerebral Ventricle Shape

In this paper we present new results on the automatic building of a 3D morphometric brain atlas from volumetric MRI images and its application to the study of the shape of cerebral structures. In particular, we show how it is possible to define “abnormal” deformations of the cerebral ventricles with a small set of parameters.

Automatic MRI Database Exploration and Applications

The design of representative models of the human body is of great interest to medical doctors. Qualitative information about the characteristics of the brain is widely available, but due to the volume of information that needs to be analyzed and the complexity of its structure, rarely is there quantification according to a standard model. To address this problem, we propose in this paper an automatic method to retrieve corresponding structures from a database of medical images. This procedure being local and fast, will permit navigation through large databases in a practical amount of time. We present as examples of applications the building of an average volume of interest and preliminary results of classification according to morphology.

Crest Lines for Curve-Based Warping

Publisher Summary This chapter presents an automatic method to warp three-dimensional medical images, which is based on crest line landmarks that are automatically extracted. It describes several applications to warp an atlas to retrieve anatomical structures in an image. Digital topology methods not only give the tracks of the sulci on the cortical surface but also extract the whole sulcal fold surface. Such methods are very useful to study the structure of the cortex. On the contrary, differential geometry methods give only characteristic lines, but they integrate quantitative information on the local shape of the surface (elliptic, parabolic, hyperbolic, convex, and concave). These lines, especially the crest lines, are very appropriate landmarks of the cortical structures for registration, warping, or morphometrical applications. Crest lines have a very complex geometry as they characterize the chaotic shape of the brains surface. So, except in the case of the cerebral ventricles where the shape is smooth, it is difficult to present several hundred lines to an expert to obtain a qualitative evaluation of these cortical landmarks.