Microscopy Cell Segmentation via Adversarial Neural Networks

Assaf Arbelle, Tamar Riklin Raviv

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

32 Scopus citations

Abstract

We present a novel method for cell segmentation in microscopy images which is inspired by the Generative Adversarial Neural Network (GAN) approach. Our framework is built on a pair of two competitive artificial neural networks, with a unique architecture, termed Rib Cage, which are trained simultaneously and together define a min-max game resulting in an accurate segmentation of a given image. Our approach has two main strengths, similar to the GAN, the method does not require a formulation of a loss function for the optimization process. This allows training on a limited amount of annotated data in a weakly supervised manner. Promising segmentation results on real fluorescent microscopy data are presented. The code is freely available at: https://github.com/arbellea/DeepCellSeg.git.

Original languageEnglish
Title of host publication2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
PublisherInstitute of Electrical and Electronics Engineers
Pages645-648
Number of pages4
ISBN (Electronic)9781538636367
DOIs
StatePublished - 23 May 2018
Event15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States
Duration: 4 Apr 20187 Apr 2018

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2018-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
Country/TerritoryUnited States
CityWashington
Period4/04/187/04/18

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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