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.
- Contextual classification
- Supervised learning
- Tumor segmentation
- Whole body
Sampedro, F., Escalera, S., Domenech, A., & Carrio, I. (2015). Automatic tumor volume segmentation in whole-body PET/CT scans: A supervised learning approach. Journal of Medical Imaging and Health Informatics, 5(2), 192-201. https://doi.org/10.1166/jmihi.2015.1374