Deformable Organisms and Error Learning for Brain Segmentation
Year: 2011
Type: article
Abstract: . Segmentation methods for medical images may not gener-alize 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 bor-rowing ideas from artificial life to govern a set of deformable models that use control processes such as sensing, proactive planning, reactive be-havior, 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, register-ing brain data, and creating cortical surface models. Our plan dictates how deformable organisms find features in head images and coopera-tively work to segment the brain. In addition, we use a method based on
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2946654524