Heatmap illustrating which multi-scale feature pixels were classified as “cell” by query selection. This data was collected by processing the full test set containing 29 movies from the bacterial mother machine dataset with Cell-TRACTR. Note that for these movies, the model preferentially uses pixels from the lowest resolution multi-scale features. The black pixel at the top corresponds to the top of the mother machine image where there are not usually cells.

<p>Heatmap illustrating which multi-scale feature pixels were classified as “cell” by query selection. This data was collected by processing the full test set containing 29 movies from the bacterial mother machine dataset with Cell-TRACTR. Note that for these movies, the model preferentially u...

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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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