TY  - JOUR
AU  - Frederic Sampedro
AU  - Sergio Escalera
AU  - Anna Domenech
AU  - Ignasi Carrio
PY  - 2015//
TI  - Automatic Tumor Volume Segmentation in Whole-Body PET/CT Scans: A Supervised Learning Approach Source
T2  - JMIHI
JO  - Journal of Medical Imaging and Health Informatics
SP  - 192
EP  - 201
VL  - 5
IS  - 2
KW  - CONTEXTUAL CLASSIFICATION
KW  - PET/CT
KW  - SUPERVISED LEARNING
KW  - TUMOR SEGMENTATION
KW  - WHOLE BODY
N2  - 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.
UR  - http://dx.doi.org/10.1166/jmihi.2015.1374
N1  - HuPBA;MILAB
ID  - Frederic Sampedro2015
ER  -