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BackgroundSegmentation methods for medical images may not generalize well to new data sets or new tasks, hampering their utility. We attempt to remedy these issues using deformable organisms to create an easily customizable segmentation plan. We validate our framework by creating a plan to locate the brain in 3D magnetic resonance images of the head (skull-stripping).New methodOur method borrows ideas from artificial life to govern a set of deformable models. We 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. In addition, we use a machine learning method to make our segmentation more accurate.ResultsOur method had the least Hausdorff distance error, but included slightly less brain voxels (false negatives). It also had the lowest false positive error and performed on par to skull-stripping specific method on other metrics.Comparison with existing method(s)We tested our method on 838 T1-weighted images, evaluating results using distance and overlap error metrics based on expert gold standard segmentations. We evaluated the results before and after the learning step to quantify its benefit; we also compare our results to three other widely used methods: BSE, BET, and the Hybrid Watershed algorithm.ConclusionsOur framework captures diverse categories of information needed for brain segmentation and will provide a foundation for tackling a wealth of segmentation problems.

Sound communication plays a vital role in frog reproduction (1,2), in which vocal advertisement is generally the domain of males. Females are typically silent, but in a few anuran species [...]

A study was conducted on the temporal features of Batrachyla antartandica's evoked vocal responses. Eleven male leptodactylid frogs were subjected to a recorded imitation of advertisement calls. The subjects did not exhibit significant differences in evoked vocal responses to stimuli. Results stress the importance of sound temporal processing in specific vocal behavior patterns in frogs.