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Manual Segmentation Errors in Medical Imaging. Proposing a Reliable Gold Standard

  • Fernando Yepes-CalderonEmail author
  • J. Gordon McComb
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1051)

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.

Keywords

Segmentation standards Medical image quantification Repeatable segmentations 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Neurosurgery DivisionChildren’s Hospital Los AngelesLos AngelesUSA
  2. 2.Keck School of MedicineUniversity of Southern CaliforniaLos AngelesUSA

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