Ground truth delineation for medical image segmentation based on Local Consistency and Distribution Map analysis

Computer-aided detection (CAD) systems are being increasingly deployed for medical applications in recent years with the goal to speed up tedious tasks and improve precision. Among others, segmentation is an important component in CAD systems as a preprocessing step to help recognize patterns in medical images. In order to assess the accuracy of a CAD segmentation algorithm, comparison with ground truth data is necessary. To-date, ground truth delineation relies mainly on contours that are either manually defined by clinical experts or automatically generated by software. In this paper, we propose a systematic ground truth delineation method based on a Local Consistency Set Analysis approach, which can be used to establish an accurate ground truth representation, or if ground truth is available, to assess the accuracy of a CAD generated segmentation algorithm. We validate our computational model using medical data. Experimental results demonstrate the robustness of our approach. In contrast to current methods, our model also provides consistency information at distributed boundary pixel level, and thus is invariant to global compensation error.