Fast modified Self-organizing Deformable Model: Geometrical feature-preserving mapping of organ models onto target surfaces with various shapes and topologies

Abstract

Background and objective: This paper proposes a new method for mapping surface models of human organs onto target surfaces with the same genus as the organs.

Methods: In the proposed method, called modified Self-organizing Deformable Model (mSDM), the mapping problem is formulated as the minimization of an objective function which is defined as the weighted linear combination of four energy functions: model fitness, foldover-free, landmark mapping accuracy, and geometrical feature preservation. Further, we extend mSDM to speed up its processes, and call it Fast mSDM.

Results: From the mapping results of various organ models with different number of holes, it is observed that Fast mSDM can map the organ models onto their target surfaces efficiently and stably without foldovers while preserving geometrical features.

Conclusions: Fast mSDM can map the organ model onto the target surface efficiently and stably, and is applicable to medical applications including Statistical Shape Model.

Keywords: Angle- and/or area-preserving mapping; Fast modified Self-organizing Deformable Model; Model correspondence; Organ surface model; Statistical Shape Model.