Kantonsspital St.Gallen
login

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

Anupama Goparaju, Krithika Iyer, Alexandre Bône, Nan Hu, Heath B Henninger, Andrew E Anderson, Stanley Durrleman, Matthijs Jacxsens, Alan Morris, Ibolya Csecs, Nassir Marrouche & Shireen Y Elhabian

abstract 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.g., implant design and lesion screening). Here, we systematically assess the outcome of widely used, state-of-the-art SSM tools, namely ShapeWorks, Deformetrica, and SPHARM-PDM. We use both quantitative and qualitative metrics to evaluate shape models from different tools. We propose validation frameworks for anatomical landmark/measurement inference and lesion screening. We also present a lesion screening method to objectively characterize subtle abnormal shape changes with respect to learned population-level statistics of controls. Results demonstrate that SSM tools display different levels of consistencies, where ShapeWorks and Deformetrica models are more consistent compared to models from SPHARM-PDM due to the groupwise approach of estimating surface correspondences. Furthermore, ShapeWorks and Deformetrica shape models are found to capture clinically relevant population-level variability compared to SPHARM-PDM models.
   
citation Goparaju A, Iyer K, Bône A, Hu N, Henninger H B, Anderson A E, Durrleman S, Jacxsens M, Morris A, Csecs I, Marrouche N, Elhabian S Y. Benchmarking off-the-shelf statistical shape modeling tools in clinical applications. Med Image Anal 2021; 76:102271.
   
type journal paper/review (English)
date of publishing 26-10-2021
journal title Med Image Anal (76)
ISSN electronic 1361-8423
pages 102271
PubMed 34974213
DOI 10.1016/j.media.2021.102271