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Goparaju A, Iyer K, Bône A, et al. Benchmarking off-the-shelf statistical shape modeling tools in clinical applications. Med Image Anal. 2021;76:102271doi: 10.1016/j.media.2021.102271.
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. (2021). Benchmarking off-the-shelf statistical shape modeling tools in clinical applications. Medical image analysis, 76102271. https://doi.org/10.1016/j.media.2021.102271
Goparaju, Anupama, et al. "Benchmarking off-the-shelf statistical shape modeling tools in clinical applications." Medical image analysis vol. 76 (2021): 102271. doi: https://doi.org/10.1016/j.media.2021.102271
Goparaju A, Iyer K, Bône A, Hu N, Henninger HB, Anderson AE, Durrleman S, Jacxsens M, Morris A, Csecs I, Marrouche N, Elhabian SY. Benchmarking off-the-shelf statistical shape modeling tools in clinical applications. Med Image Anal. 2021 Oct 26;76:102271. doi: 10.1016/j.media.2021.102271. Epub 2021 Oct 26. PMID: 34974213.
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Med Image Anal. 2021 Oct 26;76:102271. doi: 10.1016/j.media.2021.102271. Epub 2021 Oct 26.

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

Medical image analysis

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

Affiliations

  1. Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA; School of Computing, University of Utah, Salt Lake City, UT, USA.
  2. ARAMIS Lab, ICM, Inserm U1127, CNRS UMR 7225, Sorbonne University, Inria, Paris, France.
  3. Robert Stempel School of Public Health and Social Work, Florida International University, Miami, FL, USA.
  4. Department of Orthopaedics, School of Medicine, University of Utah, Salt Lake City, UT, USA.
  5. Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA; Department of Orthopaedics, School of Medicine, University of Utah, Salt Lake City, UT, USA.
  6. Division of Cardiovascular Medicine, School of Medicine, University of Utah, Salt Lake City, UT, USA.
  7. Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA; School of Computing, University of Utah, Salt Lake City, UT, USA. Electronic address: [email protected].

PMID: 34974213 DOI: 10.1016/j.media.2021.102271

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.

Copyright © 2021 Elsevier B.V. All rights reserved.

Keywords: Algorithm evaluation and validation; Correspondence optimization; Landmark inference; Lesion screening; Population analysis; Statistical shape models; Surface parameterization

Conflict of interest statement

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this pa

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