Improved operator agreement and efficiency using the minimum area contour change method for delineation of hyperintense multiple sclerosis lesions on FLAIR MRI

BMC Medical Imaging

Robert Zivadinov⁴  Kevin Seals²  Christopher Magnano¹  Sara Hussein¹  Laura Ranza³  Carol Di Perri³  Deepa Ramasamy¹  Niels Bergsland¹  Michael G Dwyer¹  David S Wack⁵ 

[1] Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York at Buffalo, Buffalo, NY, USA;School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA;Department of Neuroradiology, IRCCS, C. Mondino, University of Pavia, Pavia, Italy;MR Imaging Clinical Translational Research Center, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA;Department of Nuclear Medicine, University at Buffalo, State University of New York at Buffalo, Buffalo, NY, USA

关键词: Minimum area contour change;    Assessment;    Lesion;    Kappa;    Similarity index;    Jaccard index;    Metric;    Operator agreement;    Rater agreement;    Outline error;    Detection error;    Multiple sclerosis;   

Others  :  1090644
DOI  :  10.1186/1471-2342-13-29

 received in 2013-03-20, accepted in 2013-08-19,  发布年份 2013

【 摘 要 】

Background

Activity of disease in patients with multiple sclerosis (MS) is monitored by detecting and delineating hyper-intense lesions on MRI scans. The Minimum Area Contour Change (MACC) algorithm has been created with two main goals: a) to improve inter-operator agreement on outlining regions of interest (ROIs) and b) to automatically propagate longitudinal ROIs from the baseline scan to a follow-up scan.

Methods

The MACC algorithm first identifies an outer bound for the solution path, forms a high number of iso-contour curves based on equally spaced contour values, and then selects the best contour value to outline the lesion. The MACC software was tested on a set of 17 FLAIR MRI images evaluated by a pair of human experts and a longitudinal dataset of 12 pairs of T2-weighted Fluid Attenuated Inversion Recovery (FLAIR) images that had lesion analysis ROIs drawn by a single expert operator.

Results

In the tests where two human experts evaluated the same MRI images, the MACC program demonstrated that it could markedly reduce inter-operator outline error. In the longitudinal part of the study, the MACC program created ROIs on follow-up scans that were in close agreement to the original expert’s ROIs. Finally, in a post-hoc analysis of 424 follow-up scans 91% of propagated MACC were accepted by an expert and only 9% of the final accepted ROIS had to be created or edited by the expert.

Conclusion

When used with an expert operator's verification of automatically created ROIs, MACC can be used to improve inter- operator agreement and decrease analysis time, which should improve data collected and analyzed in multicenter clinical trials.

【 授权许可】

   
2013 Wack et al.; licensee BioMed Central Ltd.

【 图 表 】

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