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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2025
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| _version_ | 1852019910835699712 |
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| author | Owen M. O’Connor (11965460) |
| author2 | Mary J. Dunlop (463579) |
| author2_role | author |
| author_facet | Owen M. O’Connor (11965460) Mary J. Dunlop (463579) |
| author_role | author |
| dc.creator.none.fl_str_mv | Owen M. O’Connor (11965460) Mary J. Dunlop (463579) |
| dc.date.none.fl_str_mv | 2025-05-29T12:19:51Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pcbi.1013071.g001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Results_from_Cell-TRACTR_and_model_overview_for_object_detection_/29181965 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | 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 |
| dc.title.none.fl_str_mv | Results from Cell-TRACTR and model overview for object detection. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <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 represents a unique cell being tracked and black arrows signify cell divisions. This movie is from the test set. (B) Overview of Cell-TRACTR tracking mammalian nuclei across multiple frames. Each color represents a unique cell being tracked. These frames are from the DeepCell dataset using data from the test set. (C) Cell-TRACTR is a DETR-based model that first uses a CNN backbone to extract image features, then uses a transformer encoder-decoder framework that leverages attention mechanisms. Encoded multi-scale features are used for query selection to initialize the object queries. Object queries attend to the encoded multi-scale features to generate the output embeddings needed to predict class labels, bounding boxes, and segmentation masks. If the model predicts an object query to be a cell, then the output embedding is shown as a colored box in the schematic. Object queries predicted to be of class “no object” are colored black and are discarded. The squares represent content embeddings and the circles represent positional embeddings.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_dcaa81a1dedfa65a897cdb054f2e8002 |
| identifier_str_mv | 10.1371/journal.pcbi.1013071.g001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29181965 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Results from Cell-TRACTR and model overview for object detection.Owen M. O’Connor (11965460)Mary J. Dunlop (463579)BiophysicsGeneticsCancerBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedtrack many objectspresent unique challengesglobal contextual dependenciesexisting architectures developedeasily interpretable assessmentdefined microfluidic geometryconvolutional neural networksbacteria growing withindiv >< ptr </ ur </ uc </ udivision accuracy comparedtracking cells withoutassess cell divisioncells </ ptractr (< ucell tracking based</ udivision accuracytracking cellscell trackingbased modelsbased architecturecell segmentationwork establishestwo dimensionstractr operatessimultaneously segmentingresults demonstratenew frameworkmedical datafall shortecognition ),deep learningdata analysisart algorithmsanalyzing biological<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 represents a unique cell being tracked and black arrows signify cell divisions. This movie is from the test set. (B) Overview of Cell-TRACTR tracking mammalian nuclei across multiple frames. Each color represents a unique cell being tracked. These frames are from the DeepCell dataset using data from the test set. (C) Cell-TRACTR is a DETR-based model that first uses a CNN backbone to extract image features, then uses a transformer encoder-decoder framework that leverages attention mechanisms. Encoded multi-scale features are used for query selection to initialize the object queries. Object queries attend to the encoded multi-scale features to generate the output embeddings needed to predict class labels, bounding boxes, and segmentation masks. If the model predicts an object query to be a cell, then the output embedding is shown as a colored box in the schematic. Object queries predicted to be of class “no object” are colored black and are discarded. The squares represent content embeddings and the circles represent positional embeddings.</p>2025-05-29T12:19:51ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pcbi.1013071.g001https://figshare.com/articles/figure/Results_from_Cell-TRACTR_and_model_overview_for_object_detection_/29181965CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/291819652025-05-29T12:19:51Z |
| spellingShingle | Results from Cell-TRACTR and model overview for object detection. Owen M. O’Connor (11965460) 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 |
| status_str | publishedVersion |
| title | Results from Cell-TRACTR and model overview for object detection. |
| title_full | Results from Cell-TRACTR and model overview for object detection. |
| title_fullStr | Results from Cell-TRACTR and model overview for object detection. |
| title_full_unstemmed | Results from Cell-TRACTR and model overview for object detection. |
| title_short | Results from Cell-TRACTR and model overview for object detection. |
| title_sort | Results from Cell-TRACTR and model overview for object detection. |
| topic | 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 |