The problem of extracting anatomical structures from medical images is both very important and difficult. In this paper we are motivated by a new paradigm in medical image segmentation, termed Citizen Science, which involves a volunteer effort from multiple, possibly non-expert, human participants. These contributors observe 2D images and generate their estimates of anatomical boundaries in the form of planar closed curves. The challenge, of course, is to combine these different estimates in a coherent fashion and to develop an overall estimate of the underlying structure. Treating these curves as random samples, we use statistical shape theory to generate joint inferences and analyze this data generated by the citizen scientists. The specific goals in this analysis are: (1) to find a robust estimate of the representative curve that provides an overall segmentation, (2) to quantify the level of agreement between segmentations, both globally (full contours) and locally (parts of contours), and (3) to automatically detect outliers and help reduce their influence in the estimation. We demonstrate these ideas using a number of artificial examples and real applications in medical imaging, and summarize their potential use in future scenarios.
- Medical imaging
- Non-expert image segmentation
- Segmentation uncertainty
- Statistical analysis of planar curves