Abstract: Wireless capsule endoscopy involves inspection of hours of video material by a highly qualified professional. Time episodes corresponding to intestinal contractions, which are of interest to the physician constitute about 1% of the video. The problem is to label automatically time episodes containing contractions so that only a fraction of the video needs inspection. As the classes of contraction and non-contraction images in the video are largely imbalanced, ROC curves are used to optimize the trade-off between false positive and false negative rates. Classifier ensemble methods and simple classifiers were examined. Our results reinforce the claims from recent literature that classifier ensemble methods specifically designed for imbalanced problems have substantial advantages over simple classifiers and standard classifier ensembles. By using ROC curves with the bagging ensemble method the inspection time can be drastically reduced at the expense of a small fraction of missed contractions.

Abstract: In this paper, we propose an automatic method for constructing an active shape model (ASM) to segment the complete cardiac left ventricle in 3D ultrasound (3DUS) images, which avoids costly manual landmarking. The automatic construction of the ASM has already been addressed in the literature; however, the direct application of these methods to 3DUS is hampered by a high level of noise and artefacts. Therefore, we propose to construct the ASM by fusing the multidetector computed tomography data, to learn the shape, with the artificially generated 3DUS, in order to learn the neighbourhood of the boundaries. Our artificial images were generated by two approaches: a faster one that does not take into account the geometry of the transducer, and a more comprehensive one, implemented in Field II toolbox. The segmentation accuracy of our ASM was evaluated on 20 patients with left-ventricular asynchrony, demonstrating plausibility of the approach.