Benchmarking off-the-shelf statistical shape modeling tools in clinical applications |
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| Institution: | 1. Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA;2. School of Computing, University of Utah, Salt Lake City, UT, USA;3. ARAMIS Lab, ICM, Inserm U1127, CNRS UMR 7225, Sorbonne University, Inria, Paris, France;4. Robert Stempel School of Public Health and Social Work, Florida International University, Miami, FL, USA;5. 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;1. Computer Aided Medical Procedures, Technische Universität München, Boltzmannstraße 3, Garching bei München 85748, Germany;2. Carl Zeiss Meditec AG, Rudolf-Eber-Str. 11, Oberkochen 73447, Germany;3. Computer Aided Medical Procedures, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA;4. German Center for Vertigo and Balance Disorders, Ludwig-Maximilians Universität München, Marchioninistr. 15, München 81377, Germany;5. Carl Zeiss Meditec AG, Göschwitzer Str. 51-52, Jena 07745, Germany;6. Vienna Institute for Research in Ocular Surgery, A Karl-Landsteiner Institute, Hanusch Hospital, Vienna, Austria;1. School of Computer Science, University of Sydney, NSW, Australia;2. Department of Molecular Imaging, Royal Prince Alfred Hospital, NSW, Australia;1. INRIA, Project-Team Aramis, Centre Paris-Rocquencourt, France;2. Sorbonne Universités, UPMC Université Paris 06, UMR S 1127, ICM, Paris, France;3. Inserm, U1127, ICM, Paris, France;4. CNRS, UMR 7225, ICM, Paris, France;5. Institut du Cerveau et de la Moëlle Épinière (ICM), Hôpital de la Pitié Salpêtrière, 75013 Paris, France;6. Scientific Computing and Imaging (SCI) Institute, University of Utah, Salt Lake City, UT 84112, USA;7. Centre de Mathématiques et Leurs Applications (CMLA), Ecole Normale Supérieure de Cachan, 94230 Cachan, France;8. Brain Institute, University of Utah, Salt Lake City, UT 84112, USA |
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| 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. |
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