AIMC Topic: Cell Tracking

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Label-free quality control and identification of human keratinocyte stem cells by deep learning-based automated cell tracking.

Stem cells (Dayton, Ohio)
Stem cell-based products have clinical and industrial applications. Thus, there is a need to develop quality control methods to standardize stem cell manufacturing. Here, we report a deep learning-based automated cell tracking (DeepACT) technology fo...

3DeeCellTracker, a deep learning-based pipeline for segmenting and tracking cells in 3D time lapse images.

eLife
Despite recent improvements in microscope technologies, segmenting and tracking cells in three-dimensional time-lapse images (3D + T images) to extract their dynamic positions and activities remains a considerable bottleneck in the field. We develope...

Cell segmentation and tracking using CNN-based distance predictions and a graph-based matching strategy.

PloS one
The accurate segmentation and tracking of cells in microscopy image sequences is an important task in biomedical research, e.g., for studying the development of tissues, organs or entire organisms. However, the segmentation of touching cells in image...

OrganoidTracker: Efficient cell tracking using machine learning and manual error correction.

PloS one
Time-lapse microscopy is routinely used to follow cells within organoids, allowing direct study of division and differentiation patterns. There is an increasing interest in cell tracking in organoids, which makes it possible to study their growth and...

Discovering the hidden messages within cell trajectories using a deep learning approach for in vitro evaluation of cancer drug treatments.

Scientific reports
We describe a novel method to achieve a universal, massive, and fully automated analysis of cell motility behaviours, starting from time-lapse microscopy images. The approach was inspired by the recent successes in application of machine learning for...

DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning.

PLoS computational biology
Microscopy image analysis is a major bottleneck in quantification of single-cell microscopy data, typically requiring human oversight and curation, which limit both accuracy and throughput. To address this, we developed a deep learning-based image an...

Detection of Rare Objects by Flow Cytometry: Imaging, Cell Sorting, and Deep Learning Approaches.

International journal of molecular sciences
Flow cytometry nowadays is among the main working instruments in modern biology paving the way for clinics to provide early, quick, and reliable diagnostics of many blood-related diseases. The major problem for clinical applications is the detection ...

Ontology patterns for the representation of quality changes of cells in time.

Journal of biomedical semantics
BACKGROUND: Cell tracking experiments, based on time-lapse microscopy, have become an important tool in biomedical research. The goal is the reconstruction of cell migration patterns, shape and state changes, and, comprehensive genealogical informati...

Cell Type Classification and Unsupervised Morphological Phenotyping From Low-Resolution Images Using Deep Learning.

Scientific reports
Convolutional neural networks (ConvNets) have proven to be successful in both the classification and semantic segmentation of cell images. Here we establish a method for cell type classification utilizing images taken with a benchtop microscope direc...

Segmenting and tracking cell instances with cosine embeddings and recurrent hourglass networks.

Medical image analysis
Differently to semantic segmentation, instance segmentation assigns unique labels to each individual instance of the same object class. In this work, we propose a novel recurrent fully convolutional network architecture for tracking such instance seg...