Inverse Consistency by Construction for Multistep Deep Registration

Code

• github.com/uncbiag/ByConstructionICON
  Officialpytorch★ 4
• github.com/timH6502/MultiStepConsistent-LUMIRReg
  pytorch★ 0

Abstract

Inverse consistency is a desirable property for image registration. We propose a simple technique to make a neural registration network inverse consistent by construction, as a consequence of its structure, as long as it parameterizes its output transform by a Lie group. We extend this technique to multi-step neural registration by composing many such networks in a way that preserves inverse consistency. This multi-step approach also allows for inverse-consistent coarse to fine registration. We evaluate our technique on synthetic 2-D data and four 3-D medical image registration tasks and obtain excellent registration accuracy while assuring inverse consistency.

Tasks

Reproductions