13 March 2013 Automatic cell segmentation in fluorescence images of confluent cell monolayers using multi-object geometric deformable model
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Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 866904 (2013) https://doi.org/10.1117/12.2006603
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
With the rapid development of microscopy for cell imaging, there is a strong and growing demand for image analysis software to quantitatively study cell morphology. Automatic cell segmentation is an important step in image analysis. Despite substantial progress, there is still a need to improve the accuracy, efficiency, and adaptability to different cell morphologies. In this paper, we propose a fully automatic method for segmenting cells in uorescence images of conuent cell monolayers. This method addresses several challenges through a combination of ideas. 1) It realizes a fully automatic segmentation process by first detecting the cell nuclei as initial seeds and then using a multi-object geometric deformable model (MGDM) for final segmentation. 2) To deal with different defects in the uorescence images, the cell junctions are enhanced by applying an orderstatistic filter and principal curvature based image operator. 3) The final segmentation using MGDM promotes robust and accurate segmentation results, and guarantees no overlaps and gaps between neighboring cells. The automatic segmentation results are compared with manually delineated cells, and the average Dice coefficient over all distinguishable cells is 0:88.
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Zhen Yang, Zhen Yang, John A. Bogovic, John A. Bogovic, Aaron Carass, Aaron Carass, Mao Ye, Mao Ye, Peter C. Searson, Peter C. Searson, Jerry L. Prince, Jerry L. Prince, } "Automatic cell segmentation in fluorescence images of confluent cell monolayers using multi-object geometric deformable model", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866904 (13 March 2013); doi: 10.1117/12.2006603; https://doi.org/10.1117/12.2006603
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