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

Demonstrating an improvement in segmentation algorithm accuracy typically involves comparison with an accepted reference standard, such as manual expert segmentations or other imaging modalities (e.g. histology). In many medical image segmentation problems, such segmentations are challenging due to the variable appearance of anatomical/pathological features, ambiguous anatomical definitions, clinical constraints, and interobserver variability. The resulting errors in the reference standards introduce errors in the performance measures used to compare segmentation algorithms, and can impact the probability of detecting a significant difference between algorithms, referred to as the statistical power  (Beiden et al., 2000).

Source: Medical Image Analysis - July 22, 2017 Category: Radiology Authors: Eli Gibson, Yipeng Hu, Henkjan J. Huisman, Dean C. Barratt Source Type: research