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.

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The work aims to develop a new image-processing method to improve the guidance of transesophageal high intensity focused ultrasound (HIFU) atrial fibrillation therapy. Our proposal is a novel registration approach that aligns intraoperative 2D ultrasound with preoperative 3D-CT information. This approach takes advantage of the anatomical constraints imposed at the transesophageal HIFU probe to simplify the registration process. Our proposed method has been evaluated on a physical phantom and on real clinical data.

A machine vision-based classification system to sort coffee fruits (cherries) according their ripeness stage is presented. Eight categories were defined and they include the entire coffee-cherry ripening process, from the initial stage (early green) to over-ripe and dry stages. A Bayesian classifier was implemented using a set of nine features which include color, shape and texture computed on an image of the cherry, with a 96.88% of performance using the cross-validation approach.