Automated Segmentation of Cells in Phase Contrast Optical Microscopy Time Series Images

Binici R. C. , Sahin U., Ayanzadeh A., Töreyin B. U. , Onal S., PESEN OKVUR D., ...More

Medical Technologies Congress (TIPTEKNO), İzmir, Turkey, 3 - 05 October 2019, pp.200-203 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/tiptekno.2019.8895080
  • City: İzmir
  • Country: Turkey
  • Page Numbers: pp.200-203
  • Keywords: phase contrast optical microscopy, time series, cell segmentation, deep learning, SegNet, TRACKING
  • Istanbul Technical University Affiliated: Yes


Phase contrast optical microscopy is a preferred imaging technique for live-cell, temporal analysis. Segmentation of cells from time series data acquired with this technique is a labor-intensive and time-consuming task that cell biology researchers need solution for. In this study traditional image processing and deep learning based approaches for automated cell segmentation from phase contrast optical microscopy time series are presented, and their performances are evaluated against manually annotated datasets.