Knowledge of left atrial (LA) anatomy is important for atrial fibrillation ablation guidance, fibrosis quantification and biophysical modelling. Segmentation of the LA from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images is a complex problem. This manuscript presents a benchmark to evaluate algorithms that address LA segmentation. The datasets, ground truth and evaluation code have been made publicly available through the http://www.cardiacatlas.org website. This manuscript also reports the results of the Left Atrial Segmentation Challenge (LASC) carried out at the STACOM'13 workshop, in conjunction with MICCAI'13. Thirty CT and 30 MRI datasets were provided to participants for segmentation. Each participant segmented the LA including a short part of the LA appendage trunk and proximal sections of the pulmonary veins (PVs). We present results for nine algorithms for CT and eight algorithms for MRI. Results showed that methodologies combining statistical models with region growing approaches were the most appropriate to handle the proposed task. The ground truth and automatic segmentations were standardised to reduce the influence of inconsistently defined regions (e.g., mitral plane, PVs end points, LA appendage). This standardisation framework, which is a contribution of this work, can be used to label and further analyse anatomical regions of the LA. By performing the standardisation directly on the left atrial surface, we can process multiple input data, including meshes exported from different electroanatomical mapping systems.

International audienceMoments and moment invariants have become a powerful tool in pattern recognition and image analysis. Conventional methods to deal with color images are based on RGB decomposition or graying, which may lose some significant color information. In this paper, by using the algebra of quaternions, we introduce the quaternion Zernike moments (QZMs) to deal with the color images in a holistic manner. It is shown that the QZMs can be obtained from the conventional Zernike moments of each channel. We also provide the theoretical framework to construct a set of combined invariants with respect to rotation, scaling and translation (RST) transformation. Experimental results are provided to illustrate the efficiency of the proposed descriptors

This paper describes a fast and fully automatic method for liver vessel segmentation on computerized tomography scan preoperative images. The basis of this method is the introduction of a 3-D geometrical moment-based detector of cylindrical shapes within the minimum-cut/maximum-flow energy minimization framework. This method represents an original way to introduce a data term as a constraint into the widely used Boykov's graph cuts algorithm, and hence, to automate the segmentation. The method is evaluated and compared with others on a synthetic dataset. Finally, the relevancy of our method regarding the planning of a necessarily accurate percutaneous high-intensity focused ultrasound surgical operation is demonstrated with some examples.