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Caliban: Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning

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Outgoing claims: the papers this work cites, builds on, or argues with.

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Instance Segmentation and Tracking with Cosine Embeddings and Recurrent Hourglass Networks
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U-Net: Convolutional Networks for Biomedical Image Segmentation
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Medical Image Computing and Computer-Assisted Intervention — MICCAI’98
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Morphological segmentation
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Optical Pooled Screens in Human Cells
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Measuring Signaling and RNA-Seq in the Same Cell Links Gene Expression to Dynamic Patterns of NF-κB Activation
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Interpretable deep learning uncovers cellular properties in label-free live cell images that are predictive of highly metastatic melanoma
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Frequency-Modulated Pulses of ERK Activity Transmit Quantitative Proliferation Signals
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Nucleus segmentation: towards automated solutions
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Construction of Escherichia coli K‐12 in‐frame, single‐gene knockout mutants: the Keio collection
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Mechanism of shape determination in motile cells
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Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes
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Dynamics of the p53-Mdm2 feedback loop in individual cells
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Visual barcodes for clonal-multiplexing of live microscopy-based assays
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Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning
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Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations
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Quanti.us: a tool for rapid, flexible, crowd-based annotation of images
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CDeep3M—Plug-and-Play cloud-based deep learning for image segmentation
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Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy
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Content-aware image restoration: pushing the limits of fluorescence microscopy
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U-Net: deep learning for cell counting, detection, and morphometry
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Deep learning for cellular image analysis
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ilastik: interactive machine learning for (bio)image analysis
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Cell region fingerprints enable highly precise single-cell tracking and lineage reconstruction
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Cellpose 2.0: how to train your own model
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The Cell Tracking Challenge: 10 years of objective benchmarking
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Segment Anything for Microscopy
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Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations
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Cell shape and cell-wall organization in Gram-negative bacteria
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The light-sheet microscopy revolution
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Single-cell variation leads to population invariance in NF-κB signaling dynamics
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DynaMorph: self-supervised learning of morphodynamic states of live cells
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A benchmark for comparison of cell tracking algorithms
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Quantitative comparison of principal component analysis and unsupervised deep learning using variational autoencoders for shape analysis of motile cells
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EmbedTrack—Simultaneous Cell Segmentation and Tracking Through Learning Offsets and Clustering Bandwidths
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Feature Pyramid Networks for Object Detection
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Panoptic Feature Pyramid Networks
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MOTS: Multi-Object Tracking and Segmentation
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MPM: Joint Representation of Motion and Position Map for Cell Tracking
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Masked-attention Mask Transformer for Universal Image Segmentation
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CTMC: Cell Tracking with Mitosis Detection Dataset Challenge
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Focal Loss for Dense Object Detection
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Tracking the Untrackable: Learning to Track Multiple Cues with Long-Term Dependencies
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Stochastic Gene Expression in a Single Cell
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p53 Dynamics Control Cell Fate
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Accurate information transmission through dynamic biochemical signaling networks
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Dynamics of epigenetic regulation at the single-cell level
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Long Short-Term Memory
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Interactive biomedical segmentation tool powered by deep learning and ImJoy
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Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments
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DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning
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Cell Tracking Accuracy Measurement Based on Comparison of Acyclic Oriented Graphs
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Automated analysis of high‐content microscopy data with deep learning
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Mammalian gene expression variability is explained by underlying cell state
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Pooled genetic screens with image‐based profiling
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Automated Deep Lineage Tree Analysis Using a Bayesian Single Cell Tracking Approach
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AutoResearch Lite: a reproducible paper-inspired experiment runner (Adam mechanism reproduction and convergence-speed comparison)
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Cell Tracking via Proposal Generation and Selection
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Accurate and versatile 3D segmentation of plant tissues at cellular resolution
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scikit-image: image processing in Python

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning
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Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations
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DeepCell Kiosk: scaling deep learning–enabled cellular image analysis with Kubernetes
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Segment Anything for Microscopy
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Cell tracking with accurate error prediction
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Automated Deep Lineage Tree Analysis Using a Bayesian Single Cell Tracking Approach
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Tools and methods for high-throughput single-cell imaging with the mother machine
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Tools and methods for high-throughput single-cell imaging with the mother machine
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Tools and methods for high-throughput single-cell imaging with the mother machine
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Tools and methods for high-throughput single-cell imaging with the mother machine
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A review of open-source image analysis tools for mammalian cell culture: algorithms, features and implementations
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.