@Article{FredericSampedro2015,
author="Frederic Sampedro
and Sergio Escalera
and Anna Domenech
and Ignasi Carrio",
title="Automatic Tumor Volume Segmentation in Whole-Body PET/CT Scans: A Supervised Learning Approach Source",
journal="Journal of Medical Imaging and Health Informatics",
year="2015",
volume="5",
number="2",
pages="192--201",
optkeywords="CONTEXTUAL CLASSIFICATION",
optkeywords="PET/CT",
optkeywords="SUPERVISED LEARNING",
optkeywords="TUMOR SEGMENTATION",
optkeywords="WHOLE BODY",
abstract="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.",
optnote="HuPBA;MILAB",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2584), last updated on Thu, 17 Nov 2016 11:38:53 +0100",
doi="10.1166/jmihi.2015.1374"
}