TNTdetect.AI: A Deep Learning Model for Automated Detection and Counting of Tunneling Nanotubes in Microscopy Images.
Yasin CeranHamza ErgüderKatherine LadnerSophie KorenfeldKarina DenizSanyukta PadmanabhanPhillip WongMurat BadayThomas PengoEmil LouChirag B PatelPublished in: Cancers (2022)
Our automated approach labeled and detected TNTs and cells imaged in culture, resulting in comparable TCRs to those determined by human experts. Future studies will aim to improve on the accuracy, precision, and recall of the algorithm.
Keyphrases
- deep learning
- artificial intelligence
- convolutional neural network
- induced apoptosis
- machine learning
- endothelial cells
- label free
- cell cycle arrest
- high throughput
- high resolution
- single molecule
- current status
- induced pluripotent stem cells
- optical coherence tomography
- high speed
- endoplasmic reticulum stress
- pluripotent stem cells
- signaling pathway
- cell death
- mass spectrometry
- computed tomography
- positron emission tomography