Publications

@article{Owen2024-gn,

title = {Computer Vision for substrate detection in high‐throughput  biomaterial screens using bright‐field microscopy},

author = {Robert Owen and Aishah Nasir and Mahetab H Amer and Chenxue Nie and Xuan Xue and Laurence Burroughs and Chris Denning and Ricky D Wildman and Faraz A Khan and Morgan R Alexander and Felicity R A J Rose},

doi = {10.1002/aisy.202400573},

year  = {2024},

date = {2024-08-01},

urldate = {2024-08-01},

journal = {Adv. Intell. Syst.},

publisher = {Wiley},

abstract = {High‐throughput screening (HTS) can be used when ab initio 

 information is unavailable for rational design of new materials, 

 generating data on properties such as chemistry and topography 

 that control cell behavior. Biomaterial screens are typically 

 fabricated as microarrays or ``chips,\'\' seeded with the cell 

 type of interest, then phenotyped using immunocytochemistry and 

 high‐content imaging, generating vast quantities of image data. 

 Typically, analysis is only performed on fluorescent cell images 

 as it is relatively simple to automate through intensity 

 thresholding of cellular features. Automated analysis of 

 bright‐field images is rarely performed as it presents an 

 automation challenge as segmentation thresholds that work in all 

 images cannot be defined. This limits the biological insight as 

 cell response cannot be correlated to specifics of the 

 biomaterial feature (e.g., shape, size) as these features are 

 not visible on fluorescence images. Computer Vision aims to 

 digitize tasks humans do by sight, such as identify objects by 

 their shape. Herein, two case studies demonstrate how 

 open‐source approaches, (region‐based convolutional neural 

 network and algorithmic [OpenCV]), can be integrated into 

 cell‐biomaterial HTS analysis to automate bright‐field 

 segmentation across thousands of images, allowing rapid, spatial 

 definition of biomaterial features during cell analysis for the 

 first time.},

keywords = {computer vision, microscopy},

pubstate = {published},

tppubtype = {article}

}