TY - JOUR
T1 - Statistical analysis of manual segmentations of structures in medical images
AU - Su, Jingyong
AU - Kurtek, Sebastian
AU - Grimm, Cindy
AU - Vaughan, Michelle
AU - Sowell, Ross
AU - Srivastava, Anuj
N1 - Funding Information:
This research was supported in part by: NSF DMS 0915003, NSF IIS 1217515, NSF DMS 1208959, NSF DBI 1053554 and NSF DBI 1313810.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Medical imaging
KW - Non-expert image segmentation
KW - Segmentation uncertainty
KW - Statistical analysis of planar curves
UR - http://www.scopus.com/inward/record.url?scp=84885382892&partnerID=8YFLogxK
U2 - 10.1016/j.cviu.2012.11.014
DO - 10.1016/j.cviu.2012.11.014
M3 - Article
VL - 117
SP - 1036
EP - 1050
JO - Computer Vision and Image Understanding
JF - Computer Vision and Image Understanding
IS - 9
ER -