Benchmarking off-the-shelf statistical shape modeling tools in clinical applications.

Statistical shape modeling (SSM) is widely used in biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes. Technological advancements of in vivo imaging have led to the development of open-source computational tools that automate the modeling of anatomical shapes and their population-level variability. However, little work has been done on the evaluation and validation of such tools in clinical applications that rely on morphometric quantifications(e.

Interatrial Septum and Appendage Ostium in Atrial Fibrillation Patients: A Population Study.

Left atrial appendage (LAA) closure is performed in atrial fibrillation (AF) patients to help prevent stroke. LAA closure using an occlusion implant is performed under imaging guidance. However, occlusion can be a complicated process due to the highly variable and heterogeneous LAA shapes across patients.

Efficient Segmentation Pipeline Using Diffeomorphic Image Registration: A Validation Study.

Functional measurements of the left atrium (LA) in atrial fibrillation (AF) patients is limited to a single CINE slice midway through the LA. Nonetheless, a full 3D characterization of atrial functional measurements would provide more insights into LA function. But this improved modeling capacity comes at a price of requiring LA segmentation of each 3D time point,a time-consuming and expensive task that requires anatomy-specific expertise.

On the Evaluation and Validation of Off-the-shelf Statistical Shape Modeling Tools: A Clinical Application.

Statistical shape modeling (SSM) has proven useful in many areas of biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes. Recently, the increased availability of high-resolution in vivo images of anatomy has led to the development and distribution of open-source computational tools to model anatomical shapes and their variability within populations with unprecedented detail and statistical power. Nonetheless, there is little work on the evaluation and validation of such tools as related to clinical applications that rely on morphometric quantifications for treatment planning.