%0 Journal Article
%T Automatic Tumor Volume Segmentation in Whole-Body PET/CT Scans: A Supervised Learning Approach Source
%A Frederic Sampedro
%A Sergio Escalera
%A Anna Domenech
%A Ignasi Carrio
%J Journal of Medical Imaging and Health Informatics
%D 2015
%V 5
%N 2
%F Frederic Sampedro2015
%O HuPBA;MILAB
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2584), last updated on Thu, 17 Nov 2016 11:38:53 +0100
%X 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.
%K CONTEXTUAL CLASSIFICATION
%K PET/CT
%K SUPERVISED LEARNING
%K TUMOR SEGMENTATION
%K WHOLE BODY
%U http://dx.doi.org/10.1166/jmihi.2015.1374
%P 192-201