An Active Contour-based Atlas Registration Model for Automatic Subthalamic Nucleus Targeting on MRI: Method and Validation

This paper presents a new non parametric atlas registration framework, derived from the optical flow model and the active contour theory, applied to automatic subthalamic nucleus (STN) targeting in deep brain stimulation (DBS) surgery. In a previous work, we demonstrated that the STN position can be predicted based on the position of surrounding visible structures, namely the lateral and third ventricles. A STN targeting process can thus be obtained by registering these structures of interest between a brain atlas and the patient image. Here we aim to improve the results of the state of the art targeting methods and at the same time to reduce the computational time. Our simultaneous segmentation and registration model shows mean STN localization errors statistically similar to the most performing registration algorithms tested so far and to the targeting expert’s variability. Moreover, the computational time of our registration method is much lower, which is a worthwhile improvement from a clinical point of view.

Dense Deformation Field Estimation for Atlas Registration using the Active Contour Framework

Meritxell Bach Cuadra, Xavier Bresson, Valérie Duay, Jean-Philippe Thiran

In this paper, we propose a new paradigm to carry out the registration task with a dense deformation field derived from the optical flow model and the active contour method. The proposed framework merges different tasks such as segmentation, regularization, incorporation of prior knowledge and registration into a single framework. The active contour model is at the core of our framework even if it is used in a different way than the standard approaches. Indeed, active contours are a well-known technique for image segmentation. This technique consists in finding the curve which minimizes an energy functional designed to be minimal when the curve has reached the object contours. That way, we get accurate and smooth segmentation results. So far, the active contour model has been used to segment objects lying in images from boundary-based, region-based or shape-based information. Our registration technique will profit of all these families of active contours to determine a dense deformation field defined on the whole image. A well-suited application of our model is the atlas registration in medical imaging which consists in automatically delineating anatomical structures. We present results on 2D synthetic images to show the performances of our non rigid deformation field based on a natural registration term. We also present registration results on real 3D medical data with a large space occupying tumor substantially deforming surrounding structures, which constitutes a high challenging problem.

INTEL2006

A Cross Validation Study of Deep Brain Stimulation Targeting: From Experts to Atlas-Based, Segmentation-Based and Automatic Registration Algorithms

Meritxell Bach Cuadra, Olivier Cuisenaire, Reto Meuli, Francisco Javier Sanchez Castro, Jean-Philippe Thiran

Validation of image registration algorithms is a difficult task and open-ended problem, usually application-dependent. In this paper we focus on deep brain stimulation (DBS) targeting for the treatment of movement disorders like Parkinson's disease and essential tremor. DBS involves implantation of an electrode deep inside the brain to electrically stimulate specific areas shutting down the disease's symptoms. The subthalamic nucleus (STN) has turned out to be the optimal target for this kind of surgery. Unfortunately, the STN is in general not clearly distinguishable in common medical imaging modalities. Usual techniques to infer its location are the use of anatomical atlases and visible surrounding landmarks. Surgeons have to adjust the electrode intra-operatively using electrophysiological recordings and macrostimulation tests. We constructed a ground truth derived from specific patients whose STNs are clearly visible on MR T2-weighted images. A patient is chosen as atlas both for the right and left sides. Then, by registering each patient with the atlas using different methods, several estimations of the STN location are obtained. Two studies are driven using our proposed validation scheme. First, a comparison between different atlas-based and non-rigid registration algorithms with a evaluation of their performance and usability to locate the STN automatically. Second, a study of which visible surrounding structures influence the STN location. The two studies are cross-validated between them and against expert's variability. Using this scheme we evaluated the expert's ability against the estimation error provided by the tested algorithms and we demonstrated that automatic STN targeting is possible and as accurate as the expert-driven techniques currently used. We also show which structures have to be taken into account to accurately estimate the STN location.

2006

Hierarchical Image Registration with an Active Contour-Based Atlas Registration Model

Valérie Duay, Nawal Houhou, Jean-Philippe Thiran, Subrahmanyam Venkata Ravi Mohana Sai Gorthi

This paper proposes to apply the non parametric atlas registration framework we have recently developed in [6]. This technique derived from the optical flow model and the active contour framework allows to base the registration of an anatomical atlas on selected structures. A well-suited application of our model is the non rigid registration of medical images based on a hierarchical atlas. This hierarchical registration approach that we have previously introduced in [7], aims to better exploit the spatial dependencies that exist between anatomical structures in an image matching process. Its basic idea is to first register the structures the most relevant to estimate the deformation in order to help the registration of secondary structures. This aims to reduce the risks of mismatching. Here, we propose to test our novel simultaneous registration and segmentation model on different types of medical image registration problems. Results show the advantages to combine our active contour-based registration framework with the structure-based hierarchical approach and highlight the importance of the registration order of the anatomical structures.

2008

A Cross Validation Study of Deep Brain Stimulation Targeting: From Experts to Atlas-Based, Segmentation-Based and Automatic Registration Algorithms

Meritxell Bach Cuadra, Olivier Cuisenaire, Reto Meuli, Francisco Javier Sanchez Castro, Jean-Philippe Thiran

2006