Enhanced rotation-equivariant U-net for nuclear segmentation

Benjamin Chidester, That Vinh Ton, Minh Triet Tran, Jian Ma, Minh N. Do

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Despite recent advances in deep learning, the crucial task of nuclear segmentation for computational pathology remains challenging. Recently, deep learning, and specifically U-Nets, have shown significant improvements for this task, but there is still room for improvement by further enhancing the design and training of U-Nets for nuclear segmentation. Specifically, we consider enforcing rotation equivariance in the network, the placement of residual blocks, and applying novel data augmentation designed specifically for histopathology images, and show the relative improvement and merit of each. Incorporating all of these enhancements in the design and training of a U-Net yields significantly improved segmentation results while still maintaining a speed of inference that is sufficient for real-world applications, in particular, analyzing whole-slide images (WSIs). Code for our enhanced U-Net is available at https://github.com/thatvinhton/G-U-Net.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
PublisherIEEE Computer Society
Pages1097-1104
Number of pages8
ISBN (Electronic)9781728125060
DOIs
StatePublished - Jun 2019
Externally publishedYes
Event32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 - Long Beach, United States
Duration: Jun 16 2019Jun 20 2019

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2019-June
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
CountryUnited States
CityLong Beach
Period6/16/196/20/19

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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  • Cite this

    Chidester, B., Ton, T. V., Tran, M. T., Ma, J., & Do, M. N. (2019). Enhanced rotation-equivariant U-net for nuclear segmentation. In Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 (pp. 1097-1104). [9025349] (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2019-June). IEEE Computer Society. https://doi.org/10.1109/CVPRW.2019.00143