Cell proposal network for microscopy image analysis

Saad Ullah Akram, Juho Kannala, Lauri Eklund, Janne Heikkilä

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

10 Citations (Scopus)


Robust cell detection plays a key role in the development of reliable methods for automated analysis of microscopy images. It is a challenging problem due to low contrast, variable fluorescence, weak boundaries, conjoined and overlapping cells, causing most cell detection methods to fail in difficult situations. One approach for overcoming these challenges is to use cell proposals, which enable the use of more advanced features from ambiguous regions and/or information from adjacent frames to make better decisions. However, most current methods rely on simple proposal generation and scoring methods, which limits the performance they can reach. In this paper, we propose a convolutional neural network based method which generates cell proposals to facilitate cell detection, segmentation and tracking. We compare our method against commonly used proposal generation and scoring methods and show that our method generates significantly better proposals, and achieves higher final recall and average precision.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
Number of pages5
ISBN (Electronic)9781467399616
Publication statusPublished - 3 Aug 2016
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Image Processing - Phoenix, United States
Duration: 25 Sep 201628 Sep 2016
Conference number: 23


ConferenceIEEE International Conference on Image Processing
Abbreviated titleICIP
CountryUnited States


  • Cell detection
  • Cell proposals
  • Cell tracking
  • Deep learning
  • Fully convolutional network


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