Cell segmentation proposal network for microscopy image analysis

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

*Corresponding author for this work

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

49 Citations (Scopus)


Accurate cell segmentation is vital for the development of reliable microscopy image analysis methods. It is a very challenging problem due to low contrast, weak boundaries, and conjoined and overlapping cells; producing many ambiguous regions, which lower the performance of automated segmentation methods. Cell proposals provide an efficient way of exploiting both spatial and temporal context, which can be very helpful in many of these ambiguous regions. However, most proposal based microscopy image analysis methods rely on fairly simple proposal generation stage, limiting their performance. In this paper, we propose a convolutional neural network based method which provides cell segmentation proposals, which can be used for cell detection, segmentation and tracking. We evaluate our method on datasets from histology, fluorescence and phase contrast microscopy and show that it outperforms state of the art cell detection and segmentation methods.

Original languageEnglish
Title of host publicationDeep Learning and Data Labeling for Medical Applications - 1st International Workshop, LABELS 2016, and 2nd International Workshop, DLMIA 2016 Held in Conjunction with MICCAI 2016, Proceedings
PublisherSpringer Verlag
Number of pages9
Volume10008 LNCS
ISBN (Electronic)978-3-319-46976-8
ISBN (Print)9783319469751
Publication statusPublished - 2016
MoE publication typeA4 Article in a conference publication
EventInternational Workshop on Deep Learning and Data Labeling for Medical Applications - Athens, Greece
Duration: 21 Oct 201621 Oct 2016
Conference number: 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10008 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349


WorkshopInternational Workshop on Deep Learning and Data Labeling for Medical Applications


  • Cell detection
  • Cell proposals
  • Cell segmentation
  • Convolutional neural network
  • Deep learning


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