This paper presents an ensemble learning algorithm for liver tumour segmentation from a CT volume in the form of U-Boost and extends the loss functions to improve performance. Five segmentation algorithms trained by the ensemble learning algorithm with different loss functions are compared in terms of error rate and Jaccard Index between the extracted regions and true ones.

This paper proposes the design of a physically accurate spine model and its application to estimate three dimensional spine posture from the frontal and lateral views of a human body taken by two conventional video cameras. The accurate spine model proposed here is composed of rigid body parts approximating vertebral bodies and elastic body parts representing intervertebral disks. In the estimation process, we obtain neck and waist positions by fitting the Connected Vertebra Spheres Model to frontal and lateral silhouette images. Then the virtual forces acting on the top and the bottom vertebrae of the accurate spine model are computed based on the obtained neck and waist positions. The accurate model is deformed by the virtual forces, the gravitational force, and the forces of repulsion. The model thus deformed is regarded as the current posture. According to the preliminary experiments based on one real MR image data set of only one subject person, we confirmed that our proposed deformation method estimates the positions of the vertebrae within positional shifts of 3.2 6.8 mm. 3D posture of the spine could be estimated reasonably by applying the estimation method to actual human images taken by video cameras.