PURPOSE: Assessing the morphologic properties of cells in microscopy images is an important task to evaluate cell health, identity, and purity. Typically, subjective visual assessments are accomplished by an experienced researcher. This subjective human step makes transfer of the evaluation process from the laboratory to the cell manufacturing facility difficult and time consuming. METHODS: Automated image analysis can provide rapid, objective measurements of cultured cells, greatly aiding manufacturing, regulatory, and research goals. Automated algorithms for classifying images based on appearance characteristics typically either extract features from the image and use those features for classification or use the images directly as input to the classification algorithm. In this study we have developed both feature and nonfeature extraction methods for automatically measuring "cobblestone" structure in human retinal pigment epithelial (RPE) cell cultures. RESULTS: A new approach using image compression combined with a Kolmogorov complexity-based distance metric enables robust classification of microscopy images of RPE cell cultures. The automated measurements corroborate determinations made by experienced cell biologists. We have also developed an approach for using steerable wavelet filters for extracting features to characterize the individual cellular junctions. CONCLUSIONS: Two image analysis techniques enable robust and accurate characterization of the cobblestone morphology that is indicative of viable RPE cultures for therapeutic applications.
PURPOSE: Assessing the morphologic properties of cells in microscopy images is an important task to evaluate cell health, identity, and purity. Typically, subjective visual assessments are accomplished by an experienced researcher. This subjective human step makes transfer of the evaluation process from the laboratory to the cell manufacturing facility difficult and time consuming. METHODS: Automated image analysis can provide rapid, objective measurements of cultured cells, greatly aiding manufacturing, regulatory, and research goals. Automated algorithms for classifying images based on appearance characteristics typically either extract features from the image and use those features for classification or use the images directly as input to the classification algorithm. In this study we have developed both feature and nonfeature extraction methods for automatically measuring "cobblestone" structure in human retinal pigment epithelial (RPE) cell cultures. RESULTS: A new approach using image compression combined with a Kolmogorov complexity-based distance metric enables robust classification of microscopy images of RPE cell cultures. The automated measurements corroborate determinations made by experienced cell biologists. We have also developed an approach for using steerable wavelet filters for extracting features to characterize the individual cellular junctions. CONCLUSIONS: Two image analysis techniques enable robust and accurate characterization of the cobblestone morphology that is indicative of viable RPE cultures for therapeutic applications.
Authors: Rodrigo Cilla; Vinodh Mechery; Beatriz Hernandez de Madrid; Steven Del Signore; Ivan Dotu; Victor Hatini Journal: PLoS Comput Biol Date: 2015-04-17 Impact factor: 4.475
Authors: Peter Bajcsy; Antonio Cardone; Joe Chalfoun; Michael Halter; Derek Juba; Marcin Kociolek; Michael Majurski; Adele Peskin; Carl Simon; Mylene Simon; Antoine Vandecreme; Mary Brady Journal: BMC Bioinformatics Date: 2015-10-15 Impact factor: 3.169
Authors: Eric Wait; Mark Winter; Chris Bjornsson; Erzsebet Kokovay; Yue Wang; Susan Goderie; Sally Temple; Andrew R Cohen Journal: BMC Bioinformatics Date: 2014-10-03 Impact factor: 3.169
Authors: Mark R Winter; Mo Liu; David Monteleone; Justin Melunis; Uri Hershberg; Susan K Goderie; Sally Temple; Andrew R Cohen Journal: Stem Cell Reports Date: 2015-09-03 Impact factor: 7.765
Authors: Leon von der Emde; Marc Vaisband; Jan Hasenauer; Leonie Bourauel; Katharina Bermond; Marlene Saßmannshausen; Rainer Heintzmann; Frank G Holz; Christine A Curcio; Kenneth R Sloan; Thomas Ach Journal: Transl Vis Sci Technol Date: 2022-08-01 Impact factor: 3.048