Optimization of mesh generation for geometric accuracy, robustness, and efficiency of biomechanical‐model‐based deformable image registration

Abstract

Background: Successful generation of biomechanical-model-based deformable image registration (BM-DIR) relies on user-defined parameters that dictate surface mesh quality. The trial-and-error process to determine the optimal parameters can be labor-intensive and hinder DIR efficiency and clinical workflow. Purpose: To identify optimal parameters in surface mesh generation as boundary conditions for a BM-DIR in longitudinal liver and lung CT images to facilitate streamlined image registration processes. Methods: Contrast-enhanced CT images of 29 colorectal liver cancer patients and end-exhale four-dimensional CT images of 26 locally advanced non-small cell lung cancer patients were collected. Different combinations of parameters that determine the triangle mesh quality (voxel side length and triangle edge length) were investigated. The quality of DIRs generated using these parameters was evaluated with metrics for geometric accuracy, robustness, and efficiency. Metrics for geometric accuracy included target registration error (TRE) of internal vessel bifurcations, dice similar coefficient (DSC), mean distance to agreement (MDA), Hausdorff distance (HD) for organ contours, and number of vertices in the triangle mesh. American Association of Physicists in Medicine Task Group 132 was used to ensure parameters met TRE, DSC, MDA recommendations before the comparison among the parameters. Robustness was evaluated as the success rate of DIR generation, and efficiency was evaluated as the total time to generate boundary conditions and compute finite element analysis. Results: Voxel side length of 0.2 cm and triangle edge length of 3 were found to be the optimal parameters for both liver and lung, with success rate of 1.00 and 0.98 and average DIR computation time of 100 and 143 s, respectively. For this combination, the average TRE, DSC, MDA, and HD were 0.38-0.40, 0.96-0.97, 0.09-0.12, and 0.87-1.17 mm, respectively. Conclusion:The optimal parameters were found for the analyzed patients. The decision-making process described in this study serves as a recommendation for BM-DIR algorithms to be used for liver and lung. These parameters can facilitate consistence in the evaluation of published studies and more widespread utilization of BM-DIR in clinical practice.

Fully automated deep learning based auto-contouring of liver segments and spleen on contrast-enhanced CT images

“…We randomly selected CECT images of 20 patients used in training and then obtained their corresponding pre-contrast CT (i.e., non-CECT) images from the same four-phase liver CT protocol examination. To generate the ground-truth contours of liver segments, we first contoured the whole liver on the both CECT and non-CECT using our deep-learning based model 24 , and then performed whole liver based biomechanical deformable image registration using an algorithm previously validated 25 , 26 . We used models and to predict the liver segments and spleen.…”

Leveraging deep learning-based segmentation and contours-driven deformable registration for dose accumulation in abdominal structures

“…The second method was the same intensity-based method ANACONDA but also driven by boundary conditions on the liver and combined stomach and duodenum contours ("Anaconda_ROIs"). The third DIR method was a biomechanical model-based one, Morfeus (11,12), driven only by the liver and combined stomach and duodenum contours ("Morfeus"), which has been extensively validated demonstrating voxel-based accuracy within the liver (12)(13)(14).…”