Manual labeling strategy for ground truth estimation in MRI glial tumor segmentation

Valentina Pedoia, Alessandro De Benedictis, Giuseppe Renis, Emanuele Monti, Sergio Balbi, Elisabetta Binaghi

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

Abstract

In this paper we focused our attention on the problem of determining reliable ground truth for validating unsupervised, fully automatic MRI brain tumor segmentation procedures in the clinical context of Glial Tumor treatment. The goal was achieved by proposing an integrated "visual knowledge elicitation strategy" centered on the use of GliMAn(Glial Tumor Manual Annotator), a 3D MRI navigator that allows to view and manually labeling MRI volumes. As seen in our experimental context, the manual labeling process benefits from the insertion of a software tool taylored on the experts visual and usability requirements.

Original languageEnglish
Title of host publicationACM International Conference Proceeding Series
DOIs
Publication statusPublished - 2012
Event1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications, VIGTA 2012 - Capri, Italy
Duration: May 21 2012May 21 2012

Other

Other1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications, VIGTA 2012
CountryItaly
CityCapri
Period5/21/125/21/12

Keywords

  • D.2.8 [Software Engineering]: Metrics-complexity measures
  • Design
  • performance measures
  • Verifacation

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

Fingerprint Dive into the research topics of 'Manual labeling strategy for ground truth estimation in MRI glial tumor segmentation'. Together they form a unique fingerprint.

Cite this