Improving nuclei/gland instance segmentation in histopathology images by full resolution neural network and spatial constrained loss

Hui Qu, Zhennan Yan, Gregory M. Riedlinger, Subhajyoti De, Dimitris N. Metaxas

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

1 Scopus citations

Abstract

Image segmentation plays an important role in pathology image analysis as the accurate separation of nuclei or glands is crucial for cancer diagnosis and other clinical analyses. The networks and cross entropy loss in current deep learning-based segmentation methods originate from image classification tasks and have drawbacks for segmentation. In this paper, we propose a full resolution convolutional neural network (FullNet) that maintains full resolution feature maps to improve the localization accuracy. We also propose a variance constrained cross entropy (varCE) loss that encourages the network to learn the spatial relationship between pixels in the same instance. Experiments on a nuclei segmentation dataset and the 2015 MICCAI Gland Segmentation Challenge dataset show that the proposed FullNet with the varCE loss achieves state-of-the-art performance. The code is publicly available (https://github.com/huiqu18/FullNet-varCE).

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
EditorsDinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
PublisherSpringer
Pages378-386
Number of pages9
ISBN (Print)9783030322380
DOIs
StatePublished - 2019
Event22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: Oct 13 2019Oct 17 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11764 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period10/13/1910/17/19

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Deep learning
  • Gland segmentation
  • Nuclei segmentation

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  • Cite this

    Qu, H., Yan, Z., Riedlinger, G. M., De, S., & Metaxas, D. N. (2019). Improving nuclei/gland instance segmentation in histopathology images by full resolution neural network and spatial constrained loss. In D. Shen, P-T. Yap, T. Liu, T. M. Peters, A. Khan, L. H. Staib, C. Essert, & S. Zhou (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings (pp. 378-386). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11764 LNCS). Springer. https://doi.org/10.1007/978-3-030-32239-7_42