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., ...Daha Fazla

Medical Technologies Congress (TIPTEKNO), İzmir, Türkiye, 3 - 05 Ekim 2019, ss.200-203 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Doi Numarası: 10.1109/tiptekno.2019.8895080
  • Basıldığı Şehir: İzmir
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.200-203
  • Anahtar Kelimeler: phase contrast optical microscopy, time series, cell segmentation, deep learning, SegNet, TRACKING
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

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.