Designing image segmentation studies: Statistical power, sample size and reference standard quality.

ABSTRACT: Segmentation algorithms are typically evaluated by comparison to an accepted reference standard. The cost of generating accurate reference standards for medical image segmentation can be substantial. Since the study cost and the likelihood of detecting a clinically meaningful difference in accuracy both depend on the size and on the quality of the study reference standard, balancing these trade-offs supports the efficient use of research resources. In this work, we derive a statistical power calculation that enables researchers to estimate the appropriate sample size to detect clinically meaningful differences in segmentation accuracy (i.e. the proportion of voxels matching the reference standard) between two algorithms. Furthermore, we derive a formula to relate reference standard errors

SUBMITTER: Gibson E 

PROVIDER: S-EPMC5666910 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature