Shape Morphing Technique Can Accurately Predict Pelvic Bone Landmarks

Diffeomorphic shape registration allows for the seamless geometric alignment of shapes. In this study, we demonstrated the use of a registration algorithm to automatically seed anthropological landmarks on the CT images of the pelvis. We found a high correlation between manually and automatically seeded landmarks. The registration algorithm makes it possible to achieve a high degree of automation with the potential to reduce operator errors in the seeding of anthropological landmarks. The results of this study represent a promising step forward in effectively defining the anthropological measures of the human skeleton.HighlightsThe clinical CT scan is a feasible alternative to skeletal collections and body donor programs.Pelvic morphology is complex, sexually dimorphic and is proven to being a good demonstration model for the performance analysis of registration algorithm for automatic landmark seeding.The landmark seeding using registration algorithm can save time and effort in anthropological analysis.

Modelling and manufacturing of 3D-printed, patient-specific, and anthropomorphic gastric phantoms: a pilot study

Interventional devices including intragastric balloons are widely used to treat obesity. This study aims to develop 3D-printed, patient-specific, and anthropomorphic gastric phantoms with mechanical properties similar to those of human stomach. Using computed tomography gastrography (CTG) images of three patients, gastric phantoms were modelled through shape registration to align the stomach shapes of three different phases. Shape accuracies of the original gastric models versus the 3D-printed phantoms were compared using landmark distances. The mechanical properties (elongation and tensile strength), number of silicone coatings (0, 2, and 8 times), and specimen hardness (50, 60, and 70 Shore A) of three materials (Agilus, Elastic, and Flexa) were evaluated. Registration accuracy was significantly lower between the arterial and portal phases (3.16 ± 0.80 mm) than that between the portal and delayed phases (8.92 ± 0.96 mm). The mean shape accuracy difference was less than 10 mm. The mean elongations and tensile strengths of the Agilus, Elastic, and Flexa were 264%, 145%, and 146% and 1.14, 1.59, and 2.15 MPa, respectively, and their mechanical properties differed significantly (all p < 0.05). Elongation and tensile strength assessments, CTG image registration and 3D printing resulted in highly realistic and patient-specific gastric phantoms with reasonable shape accuracies.

Rigid Shape Registration Based on Extended Hamiltonian Learning

Shape registration, finding the correct alignment of two sets of data, plays a significant role in computer vision such as objection recognition and image analysis. The iterative closest point (ICP) algorithm is one of well known and widely used algorithms in this area. The main purpose of this paper is to incorporate ICP with the fast convergent extended Hamiltonian learning (EHL), so called EHL-ICP algorithm, to perform planar and spatial rigid shape registration. By treating the registration error as the potential for the extended Hamiltonian system, the rigid shape registration is modelled as an optimization problem on the special Euclidean group S E ( n ) ( n = 2 , 3 ) . Our method is robust to initial values and parameters. Compared with some state-of-art methods, our approach shows better efficiency and accuracy by simulation experiments.