Frederic Sampedro author Sergio Escalera author Anna Domenech author Ignasi Carrio author 2015 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. CONTEXTUAL CLASSIFICATION PET/CT SUPERVISED LEARNING TUMOR SEGMENTATION WHOLE BODY HuPBA;MILAB exported from refbase (http://refbase.cvc.uab.es/show.php?record=2584), last updated on Thu, 17 Nov 2016 11:38:53 +0100 text 10.1166/jmihi.2015.1374 Admin @ si @ SED2015 2015 continuing periodical academic journal 5 2 192 201