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Manual or computer-assisted tumor segmentation in FDG-PET images is a common way to assess tumor burden, such approaches are both labor intensive and may suffer from high inter-reader variability. We propose an end-to-end method leveraging 2D and 3D convolutional neural networks to rapidly identify and segment tumors and to extract metabolic information in eyes to thighs (whole body) FDG-PET\/CT scans. The developed architecture is computationally efficient and devised to accommodate the size of whole-body scans, the extreme imbalance between tumor burden and the volume of healthy tissue, and the heterogeneous nature of the input images. Our dataset consists of a total of 3664 eyes to thighs FDG-PET\/CT scans, from multi-site clinical trials in patients with non-Hodgkin\u2019s lymphoma (NHL) and advanced non-small cell lung cancer (NSCLC). Tumors were segmented and reviewed by board-certified radiologists. We report a mean 3D Dice score of 88.6% on an NHL hold-out set of 1124 scans and a 93% sensitivity on 274 NSCLC hold-out scans. The method is a potential tool for radiologists to rapidly assess eyes to thighs FDG-avid tumor burden.<\/jats:p>","DOI":"10.1007\/s10278-020-00341-1","type":"journal-article","created":{"date-parts":[[2020,5,6]],"date-time":"2020-05-06T21:02:46Z","timestamp":1588798966000},"page":"888-894","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["Tumor Segmentation and Feature Extraction from Whole-Body FDG-PET\/CT Using Cascaded 2D and 3D Convolutional Neural Networks"],"prefix":"10.1007","volume":"33","author":[{"given":"Skander","family":"Jemaa","sequence":"first","affiliation":[]},{"given":"Jill","family":"Fredrickson","sequence":"additional","affiliation":[]},{"given":"Richard A. 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Hoffman-La Roche; Tina Nielsen being an employee of and owning equity in F. Hoffman-La Roche.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}