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Collaborative Labeling of Malignant Glioma

Posted by on Friday, May 4, 2012 in Neuroimaging.

Zhoubing Xu, Andrew J. Asman, Eesha Singh, Lola Chambless, Reid Thompson, and Bennett A. Landman, “Collaborative Labeling of Malignant Glioma”, In Proceedings of the 2012 International Symposium on Biomedical Imaging (ISBI). Barcelona, Spain

Full text: http://ieeexplore.ieee.org/abstract/document/6235763/

Abstract

Malignant gliomas represent an aggressive class of central nervous system neoplasms which are often treated by maximal surgical resection. Herein, we seek to improve the methods available to quantify the extent of tumors as seen on magnetic resonance imaging using Internet-based, collaborative labeling. In a study of clinically acquired images, we demonstrate that teams of minimally trained human raters are able to reliably characterize the gadolinium-enhancing core and edema tumor regions (Dice ≈ 0.9). The collaborative approach is highly parallel and efficient in terms of time (the total time spent by the collective is equivalent to that of a single expert) and resources (only minimal training and no hardware is provided to the participants). Hence, collaborative labeling is a very promising new technique with potentially wide applicability to facilitate cost-effective manual labeling of medical imaging data.

The gamut of observed slices. The labeling objective was to a) delineate the gadolinium enhancing tumor core (T1 images, 1st row) and b) label all abnormally bright tissue (T2 images, 2nd row). The experienced labeler (“truth”, 2nd column) accurately found all structures, while the millers' work spanned a wide range of accuracies.
The gamut of observed slices. The labeling objective was to a) delineate the gadolinium enhancing tumor core (T1 images, 1st row) and b) label all abnormally bright tissue (T2 images, 2nd row). The experienced labeler (“truth”, 2nd column) accurately found all structures, while the millers’ work spanned a wide range of accuracies.