Browsing by Author "Paccini, Martina"

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    Feature-based Characterisation of Patient-specific 3D Anatomical Models
    (The Eurographics Association, 2019) Banerjee, Imon; Paccini, Martina; Ferrari, Enrico; CATALANO, CHIARA EVA; Biasotti, Silvia; Spagnuolo, Michela; Agus, Marco and Corsini, Massimiliano and Pintus, Ruggero
    This 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.
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    Mapping Grey-Levels on 3D Segmented Anatomical districts
    (The Eurographics Association, 2019) Paccini, Martina; Patané, Giuseppe; Spagnuolo, Michela; Agus, Marco and Corsini, Massimiliano and Pintus, Ruggero
    The study aims to perform a simple but effective integration of geometric information of segmented 3D bones' surface and density information provided by volume MRI (Magnetic Resonance Imaging). Such a representation method could support diagnosis process, biomedical simulation, computed assisted surgery and prosthesis fitting. The input consists of a volume MRI of a carpal district and the corresponding 3D surface model. The algorithm superimposes image and surface, and, once found the image voxel correspondent to each surface point, maps the grey level of the voxels identified on the segmented surface. The output is a surface mesh on which the texture, induced by the MRI, has been mapped. The approach is effective, general and applicable to different anatomical districts. Further elaboration of the results can be used to perform landmark identification or segmentation correction.