Results from Cell-TRACTR and model overview for object detection.

<p>(A) Representative output from Cell-TRACTR for a single chamber from a time-lapse microscopy movie of <i>E. coli</i> bacteria growing in the mother machine microfluidic device. The output is displayed as a kymograph where the same chamber is shown over time. Each color represent...

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Bibliographic Details
Main Author:	Owen M. O’Connor (11965460) (author)
Other Authors:	Mary J. Dunlop (463579) (author)
Published:	2025
Subjects:	Biophysics Genetics Cancer Biological Sciences not elsewhere classified Information Systems not elsewhere classified track many objects present unique challenges global contextual dependencies existing architectures developed easily interpretable assessment defined microfluidic geometry convolutional neural networks bacteria growing within div >< p tr </ u r </ u c </ u division accuracy compared tracking cells without assess cell division cells </ p tractr (< u cell tracking based </ u division accuracy tracking cells cell tracking based models based architecture cell segmentation work establishes two dimensions tractr operates simultaneously segmenting results demonstrate new framework medical data fall short ecognition ), deep learning data analysis art algorithms analyzing biological
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Results from Cell-TRACTR and model overview for object detection.

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