Validation of Experts versus Atlas-based and Automatic Registration Methods for Subthalamic Nucleus Targeting on MRI

Objects In functional stereotactic neurosurgery, one of the cornerstones upon which the success and the operating time depends is an accurate targeting. The subthalamic nucleus (STN) is the usual target involved when applying deep brain stimulation for Parkinson's disease (PD). Unfortunately, STN is usually not clearly visible in common medical imaging modalities, which justifies the use of atlas-based segmentation techniques to infer the STN location. Materials and methods Eight bilaterally implanted PD patients were included in this study. A three-dimensional T1-weighted sequence and inversion recovery T2-weighted coronal slices were acquired pre-operatively. We propose a methodology for the construction of a ground truth of the STN location and a scheme that allows both, to perform a comparison between different non-rigid registration algorithms and to evaluate their usability to locate the STN automatically. Results The intra-expert variability in identifying the STN location is 1.06±0.61mm while the best non-rigid registration method gives an error of 1.80±0.62mm. On the other hand, statistical tests show that an affine registration with only 12 degrees of freedom is not enough for this application. Conclusions Using our validation-evaluation scheme, we demonstrate that automatic STN localization is possible and accurate with non-rigid registration algorithm

Validation of Experts versus Atlas-based and Automatic Registration Methods for Subthalamic Nucleus Targeting on MRI

Objects In functional stereotactic neurosurgery, one of the cornerstones upon which the success and the operating time depends is an accurate targeting. The subthalamic nucleus (STN) is the usual target involved when applying deep brain stimulation for Parkinson's disease (PD). Unfortunately, STN is usually not clearly visible in common medical imaging modalities, which justifies the use of atlas-based segmentation techniques to infer the STN location. Materials and methods Eight bilaterally implanted PD patients were included in this study. A three-dimensional T1-weighted sequence and inversion recovery T2-weighted coronal slices were acquired pre-operatively. We propose a methodology for the construction of a ground truth of the STN location and a scheme that allows both, to perform a comparison between different non-rigid registration algorithms and to evaluate their usability to locate the STN automatically. Results The intra-expert variability in identifying the STN location is 1.06±0.61mm while the best non-rigid registration method gives an error of 1.80±0.62mm. On the other hand, statistical tests show that an affine registration with only 12 degrees of freedom is not enough for this application. Conclusions Using our validation-evaluation scheme, we demonstrate that automatic STN localization is possible and accurate with non-rigid registration algorithm

Semi-supervised segmentation of ultrasound images based on patch representation and continuous min cut.

Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye). We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average) and the proposed algorithm performs favorably with the literature

using

Locally adaptable mathematical morpholog

Multi-object segmentation of brain structures in 3D MRI using a computerized atlas

We present a hierarchical multi-object surface-based deformable atlas for the automatic localization and identification of brain structures in MR images. The atlas is built as a multi-object set of 3D triangulated closed surfaces, each representing a given brain structure, and sharing its faces with neighboring structures. To support such a topology unambiguously, the multi-object mesh is build upon a Face Centered Cubic grid to maintain a unique kind of shared boundary elements. Hence, the voronoi neighborhoods of grid points are rhombic dodecahedra so that neighboring grid points always share a common face of a given size (cubic grid points can also share an edge or a corner). The registration of the atlas to a patient's MR image is done in two steps: a global registration based on the matching of the cortical surface and the ventricles followed by a multi-object active surface deformation to account for the local shape deformations. First, the cortical surface and the ventricular sy..