A flexible and robust approach for segmenting cell nuclei from 2D microscopy images using supervised learning and template matching

Cytometry A. 2013 May;83(5):495-507. doi: 10.1002/cyto.a.22280. Epub 2013 Apr 8.


We describe a new supervised learning-based template matching approach for segmenting cell nuclei from microscopy images. The method uses examples selected by a user for building a statistical model that captures the texture and shape variations of the nuclear structures from a given dataset to be segmented. Segmentation of subsequent, unlabeled, images is then performed by finding the model instance that best matches (in the normalized cross correlation sense) local neighborhood in the input image. We demonstrate the application of our method to segmenting nuclei from a variety of imaging modalities, and quantitatively compare our results to several other methods. Quantitative results using both simulated and real image data show that, while certain methods may work well for certain imaging modalities, our software is able to obtain high accuracy across several imaging modalities studied. Results also demonstrate that, relative to several existing methods, the template-based method we propose presents increased robustness in the sense of better handling variations in illumination, variations in texture from different imaging modalities, providing more smooth and accurate segmentation borders, as well as handling better cluttered nuclei.

Publication types

  • Comparative Study
  • Evaluation Study
  • Research Support, N.I.H., Extramural

MeSH terms

  • Animals
  • Bone Neoplasms / pathology
  • Cell Line
  • Cell Line, Tumor
  • Cell Nucleus / pathology*
  • Female
  • Fibroblasts / pathology
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Mice
  • Microscopy / methods*
  • Models, Statistical*
  • NIH 3T3 Cells / pathology
  • Osteosarcoma / pathology
  • Pattern Recognition, Automated / methods
  • Software*