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We propose a novel weakly supervised method to improve the boundary of the 3Dsegmented nuclei utilizing an over-segmented image. This is motivated by theobservation that current state-of-the-art deep learning methods do not resultin accurate boundaries when the training data is weakly annotated. Towardsthis, a 3D U-Net is trained to get the centroid of the nuclei and integratedwith a simple linear iterative clustering (SLIC) supervoxel algorithm thatprovides better adherence to cluster boundaries. To track these segmentednuclei, our algorithm utilizes the relative nuclei location depicting theprocesses of nuclei division and apoptosis. The proposed algorithmic pipelineachieves better segmentation performance compared to the state-of-the-artmethod in Cell Tracking Challenge (CTC) 2019 and comparable performance tostate-of-the-art methods in IEEE ISBI CTC2020 while utilizing very fewpixel-wise annotated data. Detailed experimental results are provided, and thesource code is available on GitHub.

Semi Supervised Segmentation And Graph-Based Tracking Of 3d Nuclei In Time-Lapse Microscopy