A Two-stage Refinement Network for Nuclei Segmentation in Histopathology Images

Peiyi Jian*, Sei Ichiro Kamata

*この研究の対応する著者

研究成果: Conference contribution

抄録

Histopathology images are used to assess the status of certain biological structures and to diagnose diseases such as cancer. In computer-assisted diagnosis (CAD), nuclear segmentation for histopathology images is an essential prerequisite. In recent years, deep-learning technology has been gaining popularity in the field of nuclei segmentation. However, nuclei segmentation is still faced with challenging a lot of difficulties due to (1) staining intensity inhomogeneity, (2) background noise caused by preprocessing, (3) blurred boundaries due to a large number of overlapping cells. Furthermore, in histopathology imaging, the number of data samples in the dataset is relatively low, preventing deep convolutional neural networks (CNNs) from segmenting nuclei images with high accuracy like in other vision applications. To overcome the above difficulties, we propose a two-stage deep learning network for nuclei segmentation tasks. It is the first stage network responsible for coarse segmentation, and the second stage network for refined segmentation. In comparison with traditional network architectures, our method achieves near SOTA performance in the nuclei segmentation task.

本文言語English
ホスト出版物のタイトルIVSP 2022 - 2022 4th International Conference on Image, Video and Signal Processing
出版社Association for Computing Machinery
ページ8-13
ページ数6
ISBN(電子版)9781450387415
DOI
出版ステータスPublished - 2022 3月 18
イベント4th International Conference on Image, Video and Signal Processing, IVSP 2022 - Virtual, Online, Singapore
継続期間: 2022 3月 182022 3月 20

出版物シリーズ

名前ACM International Conference Proceeding Series

Conference

Conference4th International Conference on Image, Video and Signal Processing, IVSP 2022
国/地域Singapore
CityVirtual, Online
Period22/3/1822/3/20

ASJC Scopus subject areas

  • 人間とコンピュータの相互作用
  • コンピュータ ネットワークおよび通信
  • コンピュータ ビジョンおよびパターン認識
  • ソフトウェア

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