A semiautomatic tool for 3D medical image analysis using active contour models

Invariant surface alignment in the presence of affine and some nonlinear transformations

In this paper, we introduce a non-iterative geometric-based method to align 3D brain surfaces into standard coordinate system, which is based on a novel set of surface landmarks (e.g., inflection and/or zero torsion points residing on parabolic contours), which are intrinsic and are computed from the differential geometry of the surface. This is in contrast to existing methods that depend on anatomical landmarks that require expert intervention to locate--a very hard task. The landmarks are local and are preserved under affine transformations. To reduce the sensitivity of the landmarks to noise, we use a B-Spline surface representation that smoothes out the surface prior to the computation of the landmarks. The alignment is achieved by establishing correspondences between the landmarks after a sorting of the landmarks based on derived absolute invariants (volumes confined between parallelepipeds spanned by sets of the landmark point quadruplets). The method is tested for intra- and inter-brain alignments while entertaining affine, cubic and fourth-order polynomial nonlinear transformations using distance mapping as well as comparison with an expert alignment, and promising results are obtained. When comparing our automatic alignment with that of an expert we arrived at complete agreement for the more difficult case of partial alignment of sectional slab materials of five rats with an atlas (a whole brain of rat). This perfect alignment was only based on the surface structure for our procedure, whereas it was based on the staining and the external and internal structures for the expert.

Extracting VOIs from brain PET images

A semi-automatic system for determining volumes of interest (VOI) from positron emission tomography (PET) scans of brain is described. The VOIs surface extraction is based on user selectable threshold and three-dimensional target flood-fill. Contrast to anatomical volume detection approaches, volumes are determined from functional PET images and the obtained objects are checked against anatomical images. The developed VOI program was evaluated with brain FDOPA-PET studies where the striatum was the object. The results were comparable to entirely manual method and the target extraction time is reduced to about one third of manual method.

3D reconstruction method from biplanar radiography using non-stereocorresponding points and elastic deformable meshes

Standard 3D reconstruction of bones using stereoradiography is limited by the number of anatomical landmarks visible in more than one projection. The proposed technique enables the 3D reconstruction of additional landmarks that can be identified in only one of the radiographs. The principle of this method is the deformation of an elastic object that respects stereocorresponding and non-stereocorresponding observations available in different projections. This technique is based on the principle that any non-stereocorresponding point belongs to a line joining the X-ray source and the projection of the point in one view. The aim is to determine the 3D position of these points on their line of projection when submitted to geometrical and topological constraints. This technique is used to obtain the 3D geometry of 18 cadaveric upper cervical vertebrae. The reconstructed geometry obtained is compared with direct measurements using a magnetic digitiser. The order of precision determined with the point-to-surface distance between the reconstruction obtained with that technique and reference measurements is about 1 mm, depending on the vertebrae studied. Comparison results indicate that the obtained reconstruction is close to the actual vertebral geometry. This method can therefore be proposed to obtain the 3D geometry of vertebrae.

Digital 3D image reconstruction of ventriculocapillary communication as revealed in one case after transmyocardial laser revascularization

TMR (Transmyocardial Laserrevascularization) was performed on the partially dyskinetic left ventricular anterior wall with stenotic coronary blood supply in a 61 year old woman with a history of angina and myocardial infarction. As an ischemic aneurysm developed in the anteroapical region of the TMR treated area, it became clear that TMR did not provide a substitute for coronary blood supply in this very heart region. The aneurysm was removed surgically 7 months after TMR and showed histopathologic features of an acute aneurysm. Three-dimensional image analysis helped prove the presence of linear tracks through several serial sections which were not easily visible in routine histology sections. Also, three-dimensional vessel reconstruction showed a connection between a small endocardial pit on one serial section with the capillary network in the adjacent serial sections. The results should not be generalized, as currently aneurysmectomy is an end point not reached by the majority of TMR-treated patients.

The use of active shape models for making thickness measurements of articular cartilage from MR images

Previously reported studied to quantify articular cartilage have used labor-intensive manual or semi-automatic data-driven techniques, demonstrating high accuracy and precision. However, none has been able to automate the segmentation process. This paper describes a fast, automatic, model-based approach to segmentation and thickness measurement of the femoral cartilage in 3D T1-weighted images using active shape models (ASMs). Systematic experiments were performed to assess the accuracy and precision of the technique with in vivo images of both normal and abnormal knees. Segmentation accuracy was determined by comparing the results of the segmentation with the boundaries delineated by a radiologist. The mean error in locating the boundary was 0.57 pixels. To assess the precision of the measurement technique, the mean thickness of the femoral cartilage was calculated for repeated scans of five healthy volunteers. A mean coefficient of variation (CV) of 2.8% was obtained for the thickness measurements.

A semiautomatic tool for 3D medical image analysis using active contour models

We describe a computerized tool designed to improve the efficiency with which clinicians can delineate volumes of interest in multi slice medical data such as NMR and CT images. The tool allows a trained user to create deformable models that can be used to search for edges or regions. Models can also be copied from previously analysed images into fresh slices, and frozen so they can only translate, rotate and scale, without deforming. In this way an accurate model can be used to extract an object from several adjacent slices. Surfaces are then reconstructed from the models using conic splines. By applying these techniques to objects such as tumours, it should be possible to generate three-dimensional images that are accurate enough to use when planning surgery and radiotherapy.