Ground Truth Estimation by Maximizing Topological Agreements in Electron Microscopy Data
Manual editing can correct segmentation errors produced by automated segmentation algorithms, but it also introduces a practical challenge: the combination of multiple users’ annotations of an image to obtain an estimation of the true, unknown labeling. Current estimation methods are not suited for electron microscopy (EM) images because they typically do not take into account topological correctness of a segmentation that can be critical in EM analysis. This paper presents a ground truth estimation method for EM images. Taking a collection of alternative segmentations, the algorithm seeks an estimated segmentation that is topologically equivalent and geometrically similar to the true, unknown segmentation. To this end, utilizing warping error as the evaluation metric, which measures topological disagreements between segmentations, the algorithm iteratively modifies the topology of an estimated segmentation to minimize the topological disagreements between this estimated segmentation and the given segmentations. Our experimental results obtained using EM images with densely packed cells demonstrate that the proposed method is superior to majority voting and STAPLE commonly used for combining multiple segmentation results.
KeywordsGround Truth Majority Vote Electron Microscopy Image True Segmentation Initial Segmentation
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