Mask-RCNN and u-net ensembled for nuclei segmentation

Aarno Oskar Vuola*, Saad Ullah Akram, Juho Kannala

*Corresponding author for this work

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

38 Citations (Scopus)

Abstract

Nuclei segmentation is both an important and in some ways ideal task for modern computer vision methods, e.g. convolutional neural networks. While recent developments in theory and open-source software have made these tools easier to implement, expert knowledge is still required to choose the Sight model architecture and training setup. We compare two popular segmentation frameworks, U-Net and Mask-RCNN in the nuclei segmentation task and find that they have different strengths and failures. To get the best of both worlds, we develop an ensemble model to combine their predictions that can outperform both models by a significant margin and should be considered when aiming for best nuclei segmentation performance.

Original languageEnglish
Title of host publicationISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
PublisherIEEE
Pages208-212
Number of pages5
ISBN (Electronic)9781538636411
DOIs
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventIEEE International Symposium on Biomedical Imaging - Venice, Italy
Duration: 8 Apr 201911 Apr 2019
Conference number: 16

Publication series

NameIEEE International Symposium on Biomedical Imaging
PublisherIEEE
ISSN (Print)1945-7928

Conference

ConferenceIEEE International Symposium on Biomedical Imaging
Abbreviated titleISBI
CountryItaly
CityVenice
Period08/04/201911/04/2019

Keywords

  • nuclei segmentation
  • microscopy image analysis
  • convolutional neural networks

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