Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation

Parmar, Chintan; Velazquez, Emmanuel Rios; Leijenaar, Ralph T. H.; Jermoumi, M; Carvalho, Sara; Mak, Raymond H.; Mitra, Sushmita; Shankar, B. Uma; Kikinis, Ron; Haibe‐Kains, Benjamin; Lambin, Philippe; Aerts, Hugo J.W.L.

doi:10.1371/journal.pone.0102107

articlePLoS ONEJul 15, 2014GOLD OA

Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation

CPChintan Parmar EREmmanuel Rios Velazquez RTRalph T. H. Leijenaar MJM Jermoumi SCSara Carvalho

Dana-Farber Cancer Institute · Indian Statistical Institute · +9 more institutions

PubMed

Indexed incrossrefdoajpubmed

Abstract

Due to advances in the acquisition and analysis of medical imaging, it is currently possible to quantify the tumor phenotype. The emerging field of Radiomics addresses this issue by converting medical images into minable data by extracting a large number of quantitative imaging features. One of the main challenges of Radiomics is tumor segmentation. Where manual delineation is time consuming and prone to inter-observer variability, it has been shown that semi-automated approaches are fast and reduce inter-observer variability. In this study, a semiautomatic region growing volumetric segmentation algorithm, implemented in the free and publicly available 3D-Slicer platform, was investigated in terms of its…

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Topics & keywords

Topics

Keywords

Radiomics
Contouring
Segmentation
Artificial intelligence
Computer science
Pattern recognition (psychology)
Robustness (evolution)
Medical imaging

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