title: Multi-object spring level sets (MUSCLE).
abstract: A new data structure is presented for geometrically modeling multi-objects. The model can exhibit elastic and fluid-like behavior to enable interpretability between tasks that require both deformable registration and active contour segmentation. The data structure consists of a label mask, distance field, and springls (a constellation of disconnected triangles). The representation has sub-voxel precision, is parametric, re-meshes, tracks point correspondences, and guarantees no self-intersections, air-gaps, or overlaps between adjacent structures. In this work, we show how to apply existing registration algorithms and active contour segmentation to the data structure; and as a demonstration, the data structure is used to segment cortical and subcortical structures (74 total) in the human brain.
authors.affiliation: Johns Hopkins Applied Physics Laboratory, Laurel, MD, USA. blake@cs.jhu.edu
authors.forename: Blake C
authors.initials: BC
authors.lastname: Lucas
authors.forename: Michael
authors.initials: M
authors.lastname: Kazhdan
authors.forename: Russell H
authors.initials: RH
authors.lastname: Taylor
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods; D003198:Computer Simulation; D016208:Databases, Factual; D004548:Elasticity; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D011381:Programming Languages; D015203:Reproducibility of Results; D012984:Software; D013499:Surface Properties