Agreement and reliability statistics for shapes.

We describe a methodology for assessing agreement and reliability among a set of shapes. Motivated by recent studies of the reliability of manually segmented medical images, we focus on shapes composed of rasterized, binary-valued data representing closed geometric regions of interest. The methodology naturally generalizes to N dimensions and other data types, though. We formulate the shape variance, shape correlation and shape intraclass correlation coefficient (ICC) in terms of a simple distance metric, the Manhattan norm, which quantifies the absolute difference between any two shapes. We demonstrate applications of this methodology by working through example shape variance calculations in 1-D, for the analysis of overlapping line segments, and 2-D, for the analysis of overlapping regions. We also report the results of a simulated reliability analysis of manually delineated shape boundaries, and we compare the shape ICC with the more conventional and commonly used area ICC. The proposed shape-sensitive methodology captures all of the variation in the shape measurements, and it provides a more accurate estimate of the measurement reliability than an analysis of only the measured areas.