Banerjee, ImonPaccini, MartinaFerrari, EnricoCATALANO, CHIARA EVABiasotti, SilviaSpagnuolo, MichelaAgus, Marco and Corsini, Massimiliano and Pintus, Ruggero2019-11-202019-11-202019978-3-03868-100-72617-4855https://doi.org/10.2312/stag.20191362https://diglib.eg.org:443/handle/10.2312/stag20191362This paper aims to examine the potential of 3D shape analysis integrated to machine learning techniques in supporting medical investigation. In particular, we introduce an approach specially designed for the characterisation of anatomical landmarks on patient-specific 3D carpal bone models represented as triangular meshes. Furthermore, to identify functional articulation regions, two novel district-based properties are defined. The performance of both state of the art and novel features has been evaluated in a machine learning setting to identify a set of significant anatomical landmarks on patient data. Experiments have been performed on a carpal dataset of 56 patient-specific 3D models that are segmented from T1 weighed magnetic resonance (MR) scans of healthy male subjects. Despite the typical large inter-patient shape variation within the training samples, our framework has achieved promising results.Computing methodologiesShape modelingMachine learning approaches10.2312/stag.2019136241-50