Evaluation of an atlas-based automatic segmentation software for the delineation of brain organs at risk in a radiation therapy clinical context - Inria - Institut national de recherche en sciences et technologies du numérique
Journal Articles Radiotherapy & Oncology Year : 2008

Evaluation of an atlas-based automatic segmentation software for the delineation of brain organs at risk in a radiation therapy clinical context

Abstract

BACKGROUND AND PURPOSE: Conformal radiation therapy techniques require the delineation of volumes of interest, a time-consuming and operator-dependent task. In this work, we aimed to evaluate the potential interest of an atlas-based automatic segmentation software (ABAS) of brain organs at risk (OAR), when used under our clinical conditions. MATERIALS AND METHODS: Automatic and manual segmentations of the eyes, optic nerves, optic chiasm, pituitary gland, brain stem and cerebellum of 11 patients on T1-weighted magnetic resonance, 3-mm thick slice images were compared using the Dice similarity coefficient (DSC). The sensitivity and specificity of the ABAS were also computed and analysed from a radiotherapy point of view by splitting the ROC (Receiver Operating Characteristic) space into four sub-regions. RESULTS: Automatic segmentation of OAR was achieved in 7-8 min. Excellent agreement was obtained between automatic and manual delineations for organs exceeding 7 cm3: the DSC was greater than 0.8. For smaller structures, the DSC was lower than 0.41. CONCLUSIONS: These tests demonstrated that this ABAS is a robust and reliable tool for automatic delineation of large structures under clinical conditions in our daily practice, even though the small structures must continue to be delineated manually by an expert.

Dates and versions

inria-00616079 , version 1 (19-08-2011)

Identifiers

Cite

Aurélie Isambert, Frédéric Dhermain, F. Bidault, Olivier Commowick, Pierre-Yves Bondiau, et al.. Evaluation of an atlas-based automatic segmentation software for the delineation of brain organs at risk in a radiation therapy clinical context. Radiotherapy & Oncology, 2008, 87 (1), pp.93-9. ⟨10.1016/j.radonc.2007.11.030⟩. ⟨inria-00616079⟩
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