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Interactive cell segmentation based on correction propagation

Automatic cell segmentation can hardly be flawless due to the complexity of image data particularly when time-lapse experiments last for a long time without biomarkers. To address this issue, we propose an interactive cell segmentation method that actively selects uncertain regions and requests human validation on them. Once erroneous segmentation is detected and subsequently corrected, the information is propagated over affinity graphs in order to fix analogous errors. We present a systematical method for correction propagation based on active and semi-supervised learning. Experimental results performed on three types of cell populations validate that our interactive cell segmentation quickly reaches high quality results with minimal human interventions, and thus is significantly more efficient than alternative methods.