Collaborative labeling of malignant glioma with WebMILL: a first look

Eesha Singh; Andrew J. Asman; Zhoubing Xu; Lola Chambless; Reid Thompson; Bennett A. Landman

doi:10.1117/12.910802

22 February 2012 Collaborative labeling of malignant glioma with WebMILL: a first look

Eesha Singh, Andrew J. Asman, Zhoubing Xu, Lola Chambless, Reid Thompson, Bennett A. Landman

Proceedings Volume 8318, Medical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment; 831813 (2012) https://doi.org/10.1117/12.910802
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

Malignant gliomas are the most common form of primary neoplasm in the central nervous system, and one of the most rapidly fatal of all human malignancies. They are treated by maximal surgical resection followed by radiation and chemotherapy. Herein, we seek to improve the methods available to quantify the extent of tumors using newly presented, collaborative labeling techniques on magnetic resonance imaging. Traditionally, labeling medical images has entailed that expert raters operate on one image at a time, which is resource intensive and not practical for very large datasets. Using many, minimally trained raters to label images has the possibility of minimizing laboratory requirements and allowing high degrees of parallelism. A successful effort also has the possibility of reducing overall cost. This potentially transformative technology presents a new set of problems, because one must pose the labeling challenge in a manner accessible to people with little or no background in labeling medical images and raters cannot be expected to read detailed instructions. Hence, a different training method has to be employed. The training must appeal to all types of learners and have the same concepts presented in multiple ways to ensure that all the subjects understand the basics of labeling. Our overall objective is to demonstrate the feasibility of studying malignant glioma morphometry through statistical analysis of the collaborative efforts of many, minimally-trained raters. This study presents preliminary results on optimization of the WebMILL framework for neoplasm labeling and investigates the initial contributions of 78 raters labeling 98 whole-brain datasets.

Citation Download Citation

Eesha Singh, Andrew J. Asman, Zhoubing Xu, Lola Chambless, Reid Thompson, and Bennett A. Landman "Collaborative labeling of malignant glioma with WebMILL: a first look", Proc. SPIE 8318, Medical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment, 831813 (22 February 2012); https://doi.org/10.1117/12.910802

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KEYWORDS

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Medical imaging

Brain

Magnetic resonance imaging

Image fusion

Image enhancement

Nervous system

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