Pascal Cathier

Symmetric curvature patterns for colonic polyp detection

A novel approach for generating a set of features derived from properties of patterns of curvature is introduced as a part of a computer aided colonic polyp detection system. The resulting sensitivity was 84% with 4.8 false positives per volume on an independent test set of 72 patients (56 polyps). When used in conjunction with other features, it allowed the detection system to reach an overall sensitivity of 94% with a false positive rate of 4.3 per volume.

A new method for spherical object detection and its application to computer aided detection of pulmonary nodules in CT images

A novel method called local shape controlled voting has been developed for spherical object detection in 3D voxel images. By introducing local shape properties into the voting procedure of normal overlap, the proposed method improves the capability of differentiating spherical objects from other structures, as the normal overlap technique only measures the density of normal overlapping, while how the normals are distributed in 3D is not discovered. The proposed method was applied to computer aided detection of pulmonary nodules based on helical CT images. Experiments showed that this method attained a better performance compared to the original normal overlap technique.

Efficient detection of polyps in CT colonography

Colon cancer is a widespread disease and, according to the American Cancer Society, it is estimated that in 2006 more than 55,000 people will die of colon cancer in the US. However, early detection of colorectal polyps helps to drastically reduces mortality. Computer-Aided Detection (CAD) of colorectal polyps is a tool that could help physicians finding such lesions in CT scans of the colon. In this paper, we present the first phase, candidate generation (CG), of our technique for the detection of colonic polyp candidate locations in CT colonoscopy. Since polyps typically appear as protrusions on the surface of the colon, our cutting-plane algorithm identifies all those areas that can be cut-off using a plane. The key observation is that for any protruding lesion there is at least one plane that cuts a fragment off. Furthermore, the intersection between the plane and the polyp will typically be small and circular. On the other hand, a plane cannot cut a small circular cross-section from a wall or a fold, due to their concave or elongated paraboloid morphology, because these structures yield cross-sections that are much larger or non-circular. The algorithm has been incorporated as part of a prototype CAD system. An analysis on a test set of more than 400 patients yielded a high per-patient sensitivity of 95% and 90% in clean and tagged preparation respectively for polyps ranging from 6mm to 20mm in size.

Iconic feature registration with sparse wavelet coefficients

With the growing acceptance of nonrigid registration as a useful tool to perform clinical research, and in particular group studies, the storage space needed to hold the resulting transforms is deemed to become a concern for vector field based approaches, on top of the traditional computation time issue. In a recent study we lead, which involved the registration of more than 22,000 pairs of T1 MR volumes, this constrain appeared critical indeed. In this paper, we propose to decompose the vector field on a wavelet basis, and let the registration algorithm minimize the number of non-zero coefficients by introducing an L1 penalty. This enables a sparse representation of the vector field which, unlike parametric representations, does not confine the estimated transform into a small parametric space with a fixed uniform smoothness : nonzero wavelet coefficients are optimally distributed depending on the data. Furthermore, we show that the iconic feature registration framework allows to embed the non-differentiable L1 penalty into a C1 energy that can be efficiently minimized by standard optimization techniques.