Papers Cell Tracking
“Cell Tracking” 태그가 달린 논문 74편 · 필터 해제
VideoMolmo: Spatio-Temporal Grounding Meets Pointing
Spatio-temporal localization is vital for precise interactions across diverse domains, from biological research to autonomous navigation and interactive interfaces. Current video-based approaches, while proficient in tra…
Autonomous DrivingAutonomous NavigationCell TrackingReferring Video Object Segmentation+4Accurate Tracking of Arabidopsis Root Cortex Cell Nuclei in 3D Time-Lapse Microscopy Images Based on Genetic Algorithm
Arabidopsis is a widely used model plant to gain basic knowledge on plant physiology and development. Live imaging is an important technique to visualize and quantify elemental processes in plant development. To uncover …
Cell TrackingPyUAT: Open-source Python framework for efficient and scalable cell tracking
Tracking individual cells in live-cell imaging provides fundamental insights, inevitable for studying causes and consequences of phenotypic heterogeneity, responses to changing environmental conditions or stressors. Micr…
Cell TrackingcounterfactualHow To Make Your Cell Tracker Say "I dunno!"
Cell tracking is a key computational task in live-cell microscopy, but fully automated analysis of high-throughput imaging requires reliable and, thus, uncertainty-aware data analysis tools, as the amount of data recorde…
Bayesian InferenceCell TrackingUncertainty QuantificationCell as Point: One-Stage Framework for Efficient Cell Tracking
Conventional multi-stage cell tracking approaches rely heavily on detection or segmentation in each frame as a prerequisite, requiring substantial resources for high-quality segmentation masks and increasing the overall …
Cell TrackingDiagnosticSegmentationSINETRA: a Versatile Framework for Evaluating Single Neuron Tracking in Behaving Animals
Accurately tracking neuronal activity in behaving animals presents significant challenges due to complex motions and background noise. The lack of annotated datasets limits the evaluation and improvement of such tracking…
Cell TrackingTracking one-in-a-million: Large-scale benchmark for microbial single-cell tracking with experiment-aware robustness metrics
Tracking the development of living cells in live-cell time-lapses reveals crucial insights into single-cell behavior and presents tremendous potential for biomedical and biotechnological applications. In microbial live-c…
Cell TrackingContrastive learning of cell state dynamics in response to perturbations
We introduce DynaCLR, a self-supervised framework for modeling cell dynamics via contrastive learning of representations of time-lapse datasets. Live cell imaging of cells and organelles is widely used to analyze cellula…
Cell TrackingContrastive LearningCHOTA: A Higher Order Accuracy Metric for Cell Tracking
The evaluation of cell tracking results steers the development of tracking methods, significantly impacting biomedical research. This is quantitatively achieved by means of evaluation metrics. Unfortunately, current metr…
Cell TrackingMultiple Object TrackingObject TrackingRobust Approximate Characterization of Single-Cell Heterogeneity in Microbial Growth
Live-cell microscopy allows to go beyond measuring average features of cellular populations to observe, quantify and explain biological heterogeneity. Deep Learning-based instance segmentation and cell tracking form the …
Cell TrackingInstance SegmentationSemantic SegmentationBora: Biomedical Generalist Video Generation Model
Generative models hold promise for revolutionizing medical education, robot-assisted surgery, and data augmentation for medical AI development. Diffusion models can now generate realistic images from text prompts, while …
Cell TrackingData AugmentationmodelVideo GenerationDeep Temporal Sequence Classification and Mathematical Modeling for Cell Tracking in Dense 3D Microscopy Videos of Bacterial Biofilms
Automatic cell tracking in dense environments is plagued by inaccurate correspondences and misidentification of parent-offspring relationships. In this paper, we introduce a novel cell tracking algorithm named DenseTrack…
Cell TrackingTrackastra: Transformer-based cell tracking for live-cell microscopy
Cell tracking is a ubiquitous image analysis task in live-cell microscopy. Unlike multiple object tracking (MOT) for natural images, cell tracking typically involves hundreds of similar-looking objects that can divide in…
Cell TrackingMultiple Object TrackingObject TrackingSynCellFactory: Generative Data Augmentation for Cell Tracking
Cell tracking remains a pivotal yet challenging task in biomedical research. The full potential of deep learning for this purpose is often untapped due to the limited availability of comprehensive and varied training dat…
Cell TrackingData AugmentationDeep LearningCell Tracking according to Biological Needs -- Strong Mitosis-aware Multi-Hypothesis Tracker with Aleatoric Uncertainty
Cell tracking and segmentation assist biologists in extracting insights from large-scale microscopy time-lapse data. Driven by local accuracy metrics, current tracking approaches often suffer from a lack of long-term con…
Cell TrackingMotion EstimationregressionCell Tracking in C. elegans with Cell Position Heatmap-Based Alignment and Pairwise Detection
3D cell tracking in a living organism has a crucial role in live cell image analysis. Cell tracking in C. elegans has two difficulties. First, cell migration in a consecutive frame is large since they move their head dur…
Cell DetectionCell TrackingPositionA Novel Deep Learning Approach Featuring Graph-Based Algorithm for Cell Segmentation and Tracking
The precise segmentation and tracking of cells in microscopy image sequences play a pivotal role in biomedical research, facilitating the study of tissue, organ, and organism development. However, manual segmentation and…
Cell DetectionCell SegmentationCell TrackingGraph Neural Network+4Prediction of Cellular Identities from Trajectory and Cell Fate Information
Determining cell identities in imaging sequences is an important yet challenging task. The conventional method for cell identification is via cell tracking, which is complex and can be time-consuming. In this study, we p…
Cell TrackingGravitational cell detection and tracking in fluorescence microscopy data
Automatic detection and tracking of cells in microscopy images are major applications of computer vision technologies in both biomedical research and clinical practice. Though machine learning methods are increasingly co…
Cell DetectionCell TrackingA Weakly Supervised Learning Method for Cell Detection and Tracking Using Incomplete Initial Annotations
The automatic detection of cells in microscopy image sequences is a significant task in biomedical research. However, routine microscopy images with cells, which are taken during the process whereby constant division and…
Cell DetectionCell TrackingWeakly-supervised LearningWeakly Supervised Object Detection