A size adaptive neural network for nucleus segmentation

Jie Shu, Qiyang Gao, Yanjie Guan, Qian Zhang

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


The analysis of nucleus shape is useful for the pathological diagnosis or prognosis. The shape of the nucleus in the digital pathological images from different sources may vary greatly. Although, Convolutional Neural Network (CNN) has proven its success in automatic nucleus segmentation, segmentation performance may be reduced due to varies in nucleus size. In this paper, we proposed a CNN based size adaptive nucleus segmentation method. This method adapts the CNN model to image clusters with similar estimated nucleus size to increase its nucleus segmentation performance. Furthermore our method can automatically segment nuclei and the only parameter that needs to be set is the number of clusters. We compared this method to several existing methods on two datasets from the nucleus segmentation challenge. The proposed method achieved satisfactory results with mean Intersection-over-Union (IoU) 0.718 and 0.798 and mean F1-score 0.780 and 0.864. In addition, compared with the method without size adaptation, the proposed method improves the segmentation performance and is easy to implement.

Original languageEnglish
Title of host publication2021 5th International Conference on Digital Signal Processing, ICDSP 2021
PublisherAssociation for Computing Machinery
Number of pages5
ISBN (Electronic)9781450389365
Publication statusPublished - 26 Feb 2021
Event5th International Conference on Digital Signal Processing, ICDSP 2021 - Virtual, Online, China
Duration: 26 Feb 202128 Feb 2021

Publication series

NameACM International Conference Proceeding Series


Conference5th International Conference on Digital Signal Processing, ICDSP 2021
CityVirtual, Online


  • Mask R-CNN
  • convolutional neural networks
  • deep learning
  • nucleus segmentation

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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