Abstract
Manual segmentation is ubiquitous in modern medical imaging. It is a tedious and time-consuming process that is also operator-dependent and due to its low reproducibility, presents to specialist a challenge to reach consensus when diagnosing from an image. In the diagnosis of several abnormalities, geometrical features such as distances, curvatures, volumes, areas, and shapes are used to derive verdicts. These features are only quantifiable if the measuring structures can be separated from other elements in the image. The process of manual segmentation provides the analysis with a response to the question of the limits, and those limits are not easy to identify. Despite all the mentioned drawbacks, manual segmentation is still used in medical imaging analysis or employed to validate automatic or semi-automatic methods. Intending to quantify the operator variability of the process, we have created a controlled environment and run segmentations on known volumes scanned with Magnetic Resonance. The strategy proposed here suggests a mechanism to establish gold standards for geometrical readings in medical imaging; thus measuring instruments can be analyzed and certified for the task.
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References
Bae, K.T., Giger, M.L., Chen, C.-T., Kahn Jr., C.E.: Automatic segmentation of liver structure in ct images. Med. Phys. 20, 71–78 (1993)
Dyjack, D.T., Levine, S.P.: Development of an ISO 9000 compatible occupational health standard: defining the issues. Am. Ind. Hyg. Assoc. J. 56, 599–609 (1995)
Admiraal-Behloul, F., et al.: Fully automatic segmentation of white matter hyperintensities in MR images of the elderly. NeuroImage 28, 607–617 (2005)
Fenster, A., Chiu, B.: Evaluation of segmentation algorithms for medical imaging. IEEE EMB, January 2006
Duca, D., Riva, G., Foppa, E., Toscano, P.G.: Wood pellet quality with respect to EN 14961–2 standard and certifications. Fuel 135, 9–14 (2014)
Fukuda, Y., Terada, Y., Yoshioka, H., Yu, H.J., Chang, A.: Comparative study of intra-operator variability in manual and semi-automatic segmentation of knee cartilage. Abstr./Osteoarthr. Cartil. 24, 296–297 (2016)
Jovanović, B., Filipović, J.: ISO 50001 standard-based energy management maturity model-proposal and validation in industry. J. Clean. Prod. 112, 2744–2755 (2016)
Kim, H., et al.: Quantitative evaluation of image segmentation incorporating medical consideration functions. Med. Phys. 42(6), 3013–3023 (2015)
Moeskops, P., Viergever, M.A., Mendrik, A.M., de Vries, L.S., Benders, M.J.N.L., Išgum, I.: Automatic segmentation of MR brain images with a convolutional neural network. Fuel 9–14 (2014)
Pohle, R., Toennies, K.D.: Segmentation of medical images using adaptive region growing. In: Proceedings of Medical Imaging, vol. 4322, July 2001
Rana, M., et al.: Development and evaluation of an automatic tumor segmentation tool: a comparison between automatic, semi-automatic and manual segmentation of mandibular odontogenic cysts and tumors. J. Cranio-Maxillo-Fac. Surg. 43, 355–359 (2015)
Wei, W., Ramalho, O., Mandin, C.: Indoor air quality requirements in green building certifications. Build. Environ. 92, 10–19 (2015)
Yan, P., Kassim, A.A.: Medical image segmentation using minimal path deformable models with implicit shape priors. In: IEEE EMB, October 2006
Yao, J., Warren, S.: Applying the ISO/IEEE 11073 standards to wearable home health monitoring systems. J. Clin. Monit. Comput. 19, 427–436 (2005)
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Yepes-Calderon, F., Gordon McComb, J. (2019). Manual Segmentation Errors in Medical Imaging. Proposing a Reliable Gold Standard. In: Florez, H., Leon, M., Diaz-Nafria, J., Belli, S. (eds) Applied Informatics. ICAI 2019. Communications in Computer and Information Science, vol 1051. Springer, Cham. https://doi.org/10.1007/978-3-030-32475-9_17
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DOI: https://doi.org/10.1007/978-3-030-32475-9_17
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