Microscopy cell segmentation via convolutional LSTM networks

Assaf Arbelle, Tamar Riklin Raviv

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

48 Scopus citations


Live cell microscopy sequences exhibit complex spatial structures and complicated temporal behaviour, making their analysis a challenging task. Considering cell segmentation problem, which plays a significant role in the analysis, the spatial properties of the data can be captured using Convolutional Neural Networks (CNNs). Recent approaches show promising segmentation results using convolutional encoder-decoders such as the U-Net. Nevertheless, these methods are limited by their inability to incorporate temporal information, that can facilitate segmentation of individual touching cells or of cells that are partially visible. In order to exploit cell dynamics we propose a novel segmentation architecture which integrates Convolutional Long Short Term Memory (C-LSTM) with the U-Net. The network's unique architecture allows it to capture multi-scale, compact, spatio-temporal encoding in the C-LSTMs memory units. The method was evaluated on the Cell Tracking Challenge and achieved state-of-the-art results (1st on Fluo-N2DH-SIM + and 2nd on DIC-C2DLHeLa datasets) The code is freely available at: https://github.com/arbellea/LSTM-UNet.git.
Original languageEnglish GB
Title of host publication2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)
PublisherInstitute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781538636411
StatePublished - 2019
Event16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy
Duration: 8 Apr 201911 Apr 2019

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Conference16th IEEE International Symposium on Biomedical Imaging, ISBI 2019

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging


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