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Found 4 papers, 1 papers with code
VoxelEmbed: 3D Instance Segmentation and Tracking with Voxel Embedding based Deep Learning
Recently, pixel embedding-based cell instance segmentation and tracking provided a neat and generalizable computing paradigm for understanding cellular dynamics.
ASIST: Annotation-free Synthetic Instance Segmentation and Tracking by Adversarial Simulations
Contribution: The contribution of this paper is three-fold: (1) the proposed method aggregates adversarial simulations and single-stage pixel-embedding based deep learning; (2) the method is assessed with both the cellular (i. e., HeLa cells) and subcellular (i. e., microvilli) objects; and (3) to the best of our knowledge, this is the first study to explore annotation-free instance segmentation and tracking study for microscope videos.
ASIST: Annotation-free synthetic instance segmentation and tracking for microscope video analysis
Instance object segmentation and tracking provide comprehensive quantification of objects across microscope videos.
Faster Mean-shift: GPU-accelerated clustering for cosine embedding-based cell segmentation and tracking
With both embedding simulation and empirical validation via the four cohorts from the ISBI cell tracking challenge, the proposed Faster Mean-shift algorithm achieved 7-10 times speedup compared to the state-of-the-art embedding based cell instance segmentation and tracking algorithm.
