Discontinuity Preserving Liver MR Registration With Three-Dimensional Active Contour Motion Segmentation

Abstract

Objective: The sliding motion of the liver during respiration violates the homogeneous motion smoothness assumption in conventional non-rigid image registration and commonly results in compromised registration accuracy. This paper presents a novel approach, registration with 3D active contour motion segmentation (RAMS), to improve registration accuracy with discontinuityaware motion regularization. Methods: A Markov random field-based discrete optimization with dense displacement sampling and self-similarity context metric is used for registration, while a graph cuts-based 3D active contour approach is applied to segment the sliding interface. In the first registration pass, a maskfree L1 regularization on an image-derived minimum spanning tree is performed to allow motion discontinuity. Based on the motion field estimates, a coarse segmentation finds the motion boundaries. Next, based on MR signal intensity, a fine segmentation aligns the motion boundaries with anatomical boundaries. In the second registration pass, smoothness constraints across the

Advances in Data Preprocessing for Biomedical Data Fusion: An Overview of the Methods, Challenges, and Prospects

“…Improvements in the classic 'snake' include balloon-based models to reduce dependency on contour initialization [207] and united snakes to combine B-spline functions, FEM functions, etc., [208]. More recent applications include the use of active contours, which tolerate discontinuities by replacing smoothness constraints with masked regularization [209], and the use of curve-based registration for time-series of intensity change in dynamic contrast-enhanced MRI [210].…”

“…Recent studies combined biomechanical prior with geometric shape prior for surface registration of MRI to transrectal ultrasound [214,215]. Other recent registration studies with segmentation include the 3D active contour segmentation of the liver from abdominal MRI for registration [209].…”

Sliding motion compensated low-rank plus sparse (SMC-LS) reconstruction for high spatiotemporal free-breathing liver 4D DCE-MRI

“…In fact, the respiratory motion of abdominal inner organs is more complicated than the oversimplified global sliding motion model, as presented in liver MR registration [43]. Besides the constraints on the maximal range of sliding motion, there are two other limitations for successful application of the proposed SMC-LS reconstruction to clinical liver DCE-MRI exams.…”

“…abdominal image registration. The respiratory sliding motion of abdominal internal organs violates the conventional homogeneous smoothness regularization and commonly makes the registration accuracy compromised [43]. By applying prior sliding motion compensation, the motion discontinuity between the image pair would be greatly reduced so that conventional nonrigid registration with homogeneous smoothness regularization could be used for abdominal image registration.…”

An Unsupervised Learning-Based Multi-Organ Registration Method for 3D Abdominal CT Images

“…To solve the above problems, many researchers pay attention to the segmented-based abdominal image registration methods. For example, Li et al [ 9 ] proposed a liver MR image registration method based on the respiratory sliding motion segmentation that achieves more accurate registration results. Xie et al [ 10 ] proposed a lung and liver 4D-CT image registration method based on tissue features and ROI segmentation, which can be implemented on clinical data.…”