A detector-independent quality score for cell segmentation without ground truth in 3D live fluorescence microscopy

Deep-learning techniques have enabled a breakthrough in robustness and execution time in automated cell detection in live fluorescence microscopy datasets. However, the heterogeneity, dimensionality and ever-growing size of 3D+time datasets challenge the evaluation of measurements. Here we propose a quality score for the accuracy of cell segmentation maps that is detector-independent and does not need any groundtruth nor priors on object appearance. Our method learns the dynamic parameters of each cell to detect inconsistencies in local displacements induced by segmentation errors. Using simulations that approximate the dynamics of cellular aggregates, we demonstrate the score ability to rank the performance of detectors up to 40% of false positives. On live volumetric imaging of organoids, our score is able to appropriately rank two stateof-the-art pre-trained deep-learning detectors (Stardist3D and Cellpose)