Feature-based characterisation of patient-specific 3d anatomical models

Imon Banerjee, Martina Paccini, Enrico Ferrari, Chiara Eva Catalano, Silvia Biasotti, Michela Spagnuolo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationItalian Chapter Conference 2019 - Smart Tools and Apps in computer Graphics, STAG 2019
EditorsMarco Agus, Massimiliano Corsini, Ruggero Pintus
PublisherEurographics Association
Pages41-50
Number of pages10
ISBN (Electronic)9783038681007
DOIs
StatePublished - 2019
Event2019 Italian Chapter Conference - Smart Tools and Apps in Computer Graphics, STAG 2019 - Cagliari, Italy
Duration: Nov 14 2019Nov 15 2019

Publication series

NameItalian Chapter Conference 2019 - Smart Tools and Apps in computer Graphics, STAG 2019

Conference

Conference2019 Italian Chapter Conference - Smart Tools and Apps in Computer Graphics, STAG 2019
Country/TerritoryItaly
CityCagliari
Period11/14/1911/15/19

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications

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