Automatic identification and tracking of retraction fibers in time-lapse microscopy

Digital image processing in the field of time-lapse microscopy and biological research has been an interesting research area to provide statistical data of cellular dynamics to the field of cell biology. Digital image processing enables rapid and consistent quantification of qualitative observations. The image processing model examined here provides a tool for the biologists to identify structures called retraction fibers (RF) that are formed during epithelial-mesenchyme transition (EMT), an important developmental process which also occurs during cancer metastasis. Quantifying RF formation is an important tool for biologists studying cellular regulation of EMT. The dynamic EMT process is captured using time-lapse microscopy. We use computer vision algorithms to detect and track the RF in image sequences of cells undergoing EMT to generate statistical information such as the number of RF formed during a window, lifetime of the RF, and their geometric dimension. This information can in turn be used by the biologist to quantitatively measure the extent of EMT under different test conditions.