Segmentation methods for medical images may not generalize well to different data sets or tasks, hampering their utility. We attempt to remedy these issues using deformable organisms to create an easily customizable segmentation plan. This plan is developed by borrowing ideas from artificial life to govern a set of deformable models that use control processes such as sensing, proactive planning, reactive behavior, and knowledge representation to segment an image. The image may have landmarks and features specific to that dataset; these may be easily incorporated into the plan. We validate this framework by creating a plan to locate the brain in 3D magnetic resonance images of the head (skull-stripping). This is important for surgical planning, understanding how diseases affect the brain, conducting longitudinal studies, registering brain data, and creating cortical surface models. Our plan dictates how deformable organisms find features in head images and cooperatively work to segment the brain. In addition, we use a method based on Adaboost to learn and correct errors in our segmentation. We tested our method on 630 T1-weighted images from healthy young adults, evaluating results using distance and overlap error metrics based on expert gold standard segmentations. We compare our segmentations with and without the error correction step; we also compare our results to three other widely used methods: BSE, BET, and the Hybrid Watershed algorithm. Our method had the least Hausdorff distance to expert segmentations on this dataset, but included slightly more non-brain voxels (false positives). Our framework captures diverse categories of information needed for skull-stripping, and produces competitive segmentations.