The Image-to-Physical Liver Registration Sparse Data Challenge: comparison of state-of-the-art using a common dataset

Computational methods for image-to-physical registration during surgical guidance frequently rely on sparse point clouds obtained over a limited region of the organ surface. However, soft tissue deformations complicate the ability to accurately infer anatomical alignments from sparse descriptors of the organ surface. The Image-to-Physical Liver Registration Sparse Data Challenge introduced at SPIE Medical Imaging 2019 seeks to characterize the performance of sparse data registration methods on a common dataset to benchmark and identify effective tactics and limitations that will continue to inform the evolution of image-to-physical registration algorithms.

Three rigid and five deformable registration methods were contributed to the challenge. The deformable approaches consisted of two deep learning and three biomechanical boundary condition reconstruction methods. These algorithms were compared on a common dataset of 112 registration scenarios derived from a tissue-mimicking phantom with 159 subsurface validation targets. Target registration errors (TRE) were evaluated under varying conditions of data extent, target location, and measurement noise. Jacobian determinants and strain magnitudes were compared to assess displacement field consistency.

Rigid registration algorithms produced significant differences in TRE ranging from $$ to $$, depending on the choice of technique. Two biomechanical methods yielded TRE of $$ and $$, which outperformed optimal rigid registration of targets. These methods demonstrated good performance under varying degrees of surface data coverage and across all anatomical segments of the liver. Deep learning methods exhibited TRE ranging from $$ to $$ but are likely to improve with continued development. TRE was weakly correlated among methods, with greatest agreement and field consistency observed among the biomechanical approaches.

The choice of registration algorithm significantly impacts registration accuracy and variability of deformation fields. Among current sparse data driven image-to-physical registration algorithms, biomechanical simulations that incorporate task-specific insight into boundary conditions seem to offer best performance.