Scribbles for Metric Learning in Histological Image Segmentation

Daisuke Harada, Ryoma Bise, Hiroki Tokunaga, Wataru Ohyama, Sanae Oka, Toshihiko Fujimori, Seiichi Uchida

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

1 被引用数 (Scopus)

抄録

Segmentation is a fundamental process in biomedical image analysis that enables various types of analysis. Segmenting organs in histological microscopy images is problematic because the boundaries between regions are ambiguous, the images have various appearances, and the amount of training data is limited. To address these difficulties, supervised learning methods (e.g., convolutional neural networking (CNN)) are insufficient to predict regions accurately because they usually require a large amount of training data to learn the various appearances. In this paper, we propose a semi-automatic segmentation method that effectively uses scribble annotations for metric learning. Deep discriminative metric learning re-trains the representation of the feature space so that the distances between the samples with the same class labels are reduced, while those between ones with different class labels are enlarged. It makes pixel classification easy. Evaluation of the proposed method in a heart region segmentation task demonstrated that it performed better than three other methods.

本文言語英語
ホスト出版物のタイトル2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1026-1030
ページ数5
ISBN(電子版)9781538613115
DOI
出版ステータス出版済み - 7 2019
イベント41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, ドイツ
継続期間: 7 23 20197 27 2019

出版物シリーズ

名前Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN(印刷版)1557-170X

会議

会議41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
国/地域ドイツ
CityBerlin
Period7/23/197/27/19

All Science Journal Classification (ASJC) codes

  • 信号処理
  • 生体医工学
  • コンピュータ ビジョンおよびパターン認識
  • 健康情報学

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