Papers Cell Tracking
“Cell Tracking” 태그가 달린 논문 74편 · 필터 해제
Cell Tracking-by-detection using Elliptical Bounding Boxes
Cell detection and tracking are paramount for bio-analysis. Recent approaches rely on the tracking-by-model evolution paradigm, which usually consists of training end-to-end deep learning models to detect and track the c…
Cell DetectionCell TrackingLearning Deformable 3D Graph Similarity to Track Plant Cells in Unregistered Time Lapse Images
Tracking of plant cells in images obtained by microscope is a challenging problem due to biological phenomena such as large number of cells, non-uniform growth of different layers of the tightly packed plant cells and ce…
Cell TrackingGraph SimilarityLarge-Scale Multi-Hypotheses Cell Tracking Using Ultrametric Contours Maps
In this work, we describe a method for large-scale 3D cell-tracking through a segmentation selection approach. The proposed method is effective at tracking cells across large microscopy datasets on two fronts: (i) It can…
Cell SegmentationCell TrackingSegmentationEnhancing Cell Tracking with a Time-Symmetric Deep Learning Approach
The accurate tracking of live cells using video microscopy recordings remains a challenging task for popular state-of-the-art image processing based object tracking methods. In recent years, several existing and new appl…
Cell TrackingDeep LearningObject TrackingGenerative modeling of living cells with SO(3)-equivariant implicit neural representations
Data-driven cell tracking and segmentation methods in biomedical imaging require diverse and information-rich training data. In cases where the number of training samples is limited, synthetic computer-generated data set…
Cell TrackingMigraR: an open-source, R-based application for analysis and quantification of cell migration parameters
Background and objective: Cell migration is essential for many biological phenomena with direct impact on human health and disease. One conventional approach to study cell migration involves the quantitative analysis of …
Cell TrackingSegmentation based tracking of cells in 2D+time microscopy images of macrophages
The automated segmentation and tracking of macrophages during their migration are challenging tasks due to their dynamically changing shapes and motions. This paper proposes a new algorithm to achieve automatic cell trac…
Cell TrackingSegmentationCell tracking for live-cell microscopy using an activity-prioritized assignment strategy
Cell tracking is an essential tool in live-cell imaging to determine single-cell features, such as division patterns or elongation rates. Unlike in common multiple object tracking, in microbial live-cell experiments cell…
Cell TrackingInstance SegmentationMultiple Object TrackingObject Tracking+1Analysis of the performance of U-Net neural networks for the segmentation of living cells
The automated analysis of microscopy images is a challenge in the context of single-cell tracking and quantification. This work has as goals the study of the performance of deep learning for segmenting microscopy images …
Cell SegmentationCell TrackingImage SegmentationSegmentation+1Tracking by weakly-supervised learning and graph optimization for whole-embryo C. elegans lineages
Tracking all nuclei of an embryo in noisy and dense fluorescence microscopy data is a challenging task. We build upon a recent method for nuclei tracking that combines weakly-supervised learning from a small set of nucle…
Cell TrackingEvent DetectionWeakly-supervised LearningTraining a universal instance segmentation network for live cell images of various cell types and imaging modalities
We share our recent findings in an attempt to train a universal segmentation network for various cell types and imaging modalities. Our method was built on the generalized U-Net architecture, which allows the evaluation …
Cell TrackingInstance SegmentationSegmentationSemantic Segmentation+1A Semi-automatic Cell Tracking Process Towards Completing the 4D Atlas of C. elegans Development
The nematode Caenorhabditis elegans (C. elegans) is used as a model organism to better understand developmental biology and neurobiology. C. elegans features an invariant cell lineage, which has been catalogued and obser…
Cell TrackingImplicit Neural Representations for Generative Modeling of Living Cell Shapes
Methods allowing the synthesis of realistic cell shapes could help generate training data sets to improve cell tracking and segmentation in biomedical images. Deep generative models for cell shape synthesis require a lig…
Cell TrackingMedical Image SegmentationEmbedTrack -- Simultaneous Cell Segmentation and Tracking Through Learning Offsets and Clustering Bandwidths
A systematic analysis of the cell behavior requires automated approaches for cell segmentation and tracking. While deep learning has been successfully applied for the task of cell segmentation, there are few approaches f…
Cell SegmentationCell TrackingClusteringDeep Learning+1EfficientCellSeg: Efficient Volumetric Cell Segmentation Using Context Aware Pseudocoloring
Volumetric cell segmentation in fluorescence microscopy images is important to study a wide variety of cellular processes. Applications range from the analysis of cancer cells to behavioral studies of cells in the embryo…
Cell SegmentationCell TrackingDecoderSegmentation+1Graph Neural Network for Cell Tracking in Microscopy Videos
We present a novel graph neural network (GNN) approach for cell tracking in high-throughput microscopy videos. By modeling the entire time-lapse sequence as a direct graph where cell instances are represented by its node…
3D Multi-Object TrackingCell TrackingEdge ClassificationGraph Neural Network+1FastDOG: Fast Discrete Optimization on GPU
We present a massively parallel Lagrange decomposition method for solving 0--1 integer linear programs occurring in structured prediction. We propose a new iterative update scheme for solving the Lagrangean dual and a pe…
Cell TrackingGPUStructured PredictionMultiple Hypothesis Hypergraph Tracking for Posture Identification in Embryonic Caenorhabditis elegans
Current methods in multiple object tracking (MOT) rely on independent object trajectories undergoing predictable motion to effectively track large numbers of objects. Adversarial conditions such as volatile object motion…
Cell TrackingMultiple Object TrackingObjectObject TrackingOn Improving an Already Competitive Segmentation Algorithm for the Cell Tracking Challenge - Lessons Learned
The virtually error-free segmentation and tracking of densely packed cells and cell nuclei is still a challenging task. Especially in low-resolution and low signal-to-noise-ratio microscopy images erroneously merged and …
Cell TrackingData AugmentationSegmentationVoxelEmbed: 3D Instance Segmentation and Tracking with Voxel Embedding based Deep Learning
Recent advances in bioimaging have provided scientists a superior high spatial-temporal resolution to observe dynamics of living cells as 3D volumetric videos. Unfortunately, the 3D biomedical video analysis is lagging, …
3D Instance SegmentationCell TrackingGPUInstance Segmentation+2