In this paper we are concerned with multi-object segmentation. For each object we will train a level set function based shape prior from a sample set of outlines. The outlines are aligned in a multi-resolution scheme wrt. an Euclidean similarity transformation in order to maximize the overlap of the interior between all pairs of outlines. Then the outlines are converted to level set functions. A shape model is constructed from the mean level set and the first few principal variations. We combine the prior model with an observation model based on the Chan-Vese functional assuming constant intensity levels inside the outline as well as in a narrow band outside the outline. The maximum a posteriori estimate of the outline is found by gradient descent optimization. In order to segment a group of mutually dependent objects we propose 2 procedures, 1) the objects are found sequentially by conditioning the initialization of the next search from already found objects; 2) all objects are found simultaneously and a repelling force is introduced in order to avoid overlap between outlines in the solution. The methods are applied to segmentation of cross sections of muscles in slices of CT scans of pig backs for quality assessment of bacon slices.