Automatic tumor volume segmentation in whole-body PET/CT scans: A supervised learning approach

Frederic Sampedro, Sergio Escalera, Anna Domenech, Ignasi Carrio

Research output: Contribution to journalArticleResearchpeer-review

4 Citations (Scopus)


Copyright © 2015 American Scientific Publishers Whole-body 3D PET/CT tumoral volume segmentation provides relevant diagnostic and prognostic information in clinical oncology and nuclear medicine. Carrying out this procedure manually by a medical expert is time consuming and suffers from inter- and intra-observer variabilities. In this paper, a completely automatic approach to this task is presented. First, the problem is stated and described both in clinical and technological terms. Then, a novel supervised learning segmentation framework is introduced. The segmentation by learning approach is defined within a Cascade of Adaboost classifiers and a 3D contextual proposal of Multiscale Stacked Sequential Learning. Segmentation accuracy results on 200 Breast Cancer whole body PET/CT volumes show mean 49% sensitivity, 99.993% specificity and 39% Jaccard overlap Index, which represent good performance results both at the clinical and technological level.
Original languageEnglish
Pages (from-to)192-201
JournalJournal of Medical Imaging and Health Informatics
Issue number2
Publication statusPublished - 1 Jan 2015


  • Contextual classification
  • PET/CT
  • Supervised learning
  • Tumor segmentation
  • Whole body


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