14 November 2014 On the evaluation of segmentation editing tools
Frank Heckel, Jan H. Moltz, Hans Meine, Benjamin Geisler M.D., Andreas Kießling, Melvin D’Anastasi, Daniel Pinto dos Santos, Ashok Joseph Theruvath, Horst K. Hahn
Author Affiliations +
Abstract
Efficient segmentation editing tools are important components in the segmentation process, as no automatic methods exist that always generate sufficient results. Evaluating segmentation editing algorithms is challenging, because their quality depends on the user’s subjective impression. So far, no established methods for an objective, comprehensive evaluation of such tools exist and, particularly, intermediate segmentation results are not taken into account. We discuss the evaluation of editing algorithms in the context of tumor segmentation in computed tomography. We propose a rating scheme to qualitatively measure the accuracy and efficiency of editing tools in user studies. In order to objectively summarize the overall quality, we propose two scores based on the subjective rating and the quantified segmentation quality over time. Finally, a simulation-based evaluation approach is discussed, which allows a more reproducible evaluation without the need for human input. This automated evaluation complements user studies, allowing a more convincing evaluation, particularly during development, where frequent user studies are not possible. The proposed methods have been used to evaluate two dedicated editing algorithms on 131 representative tumor segmentations. We show how the comparison of editing algorithms benefits from the proposed methods. Our results also show the correlation of the suggested quality score with the qualitative ratings.
© 2014 Society of Photo-Optical Instrumentation Engineers (SPIE) 0091-3286/2014/$25.00 © 2014 SPIE
Frank Heckel, Jan H. Moltz, Hans Meine, Benjamin Geisler M.D., Andreas Kießling, Melvin D’Anastasi, Daniel Pinto dos Santos, Ashok Joseph Theruvath, and Horst K. Hahn "On the evaluation of segmentation editing tools," Journal of Medical Imaging 1(3), 034005 (14 November 2014). https://doi.org/10.1117/1.JMI.1.3.034005
Published: 14 November 2014
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CITATIONS
Cited by 8 scholarly publications.
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KEYWORDS
Image segmentation

Tumors

Computer simulations

Algorithm development

Quality measurement

Image processing algorithms and systems

Medical imaging

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