TESD: a novel ground truth estimation method

Knowledge of the exact shape of a lesion, or ground truth (GT), is necessary for algorithm validation, measurement metric analysis, accurate size estimation. When multiple readers provide their documentations of a lesion that can ultimately be described with occupancy regions, estimating the unknown GT is achieved by aptly merging those occupancy regions into a single outcome. Several methods are already available but, even when they consider the spatial location of pixels, e.g. thresholded probability-map (TPM) or STAPLE, pixels are assumed spatially independent (even when STAPLE proposes a hidden-Markov-random-field fix). In this paper we propose Truth Estimate from Self Distances (TESD): a new voting scheme, for all the voxels inside and outside the occupancy region, in order to take in account three key characteristics: (a) critical shape conformations, like holes or spikes, that are defined by the reciprocally surrounding pixels, (b) marking co-locations, meaning the closeness without intersection of one reader's marking to other readers' ones and c) the three-dimensionality of lesions as imaged by CT scanners. In TESD each voxel is labeled into four categories according to its signed distance transform and then the labeled images are combined with a center of gravity method to provide the GT estimation. This theoretical approach was validated on a subset of the publicly available Lung Image Database Consortium archive, where a total of 35 nodules documented on 26 scans by all four radiologists were available. The results obtained are reasonable estimates, with GT obtained close to TPM and STAPLE; at the same time this method is not limited to the intersections of readers' marked regions.