Automatic fetal body and amniotic fluid segmentation from fetal ultrasound images by encoder-decoder network with inner layers

Yan Li, Rong Xu, Jun Ohya, Hiroyasu Iwata

研究成果: Conference contribution

11 被引用数 (Scopus)

抄録

This paper explores the effectiveness of applying a deep learning based method to segment the amniotic fluid and fetal tissues in fetal ultrasound (US) images. The deeply learned model firstly encodes the input image into down scaled feature maps by convolution and pooling structures, then up-scale the feature maps to confidence maps by corresponded un-pooling and convolution layers. Additional convolution layers with 1×1 sized kernels are adopted to enhance the feature representations, which could be used to further improve the discriminative learning of our model. We effectively update the weights of the network by fine-tuning on part of the layers from a pre-trained model. By conducting experiments using clinical data, the feasibility of our proposed approach is compared and discussed. The result proves that this work achieves satisfied results for segmentation of specific anatomical structures from US images.

本文言語English
ホスト出版物のタイトル2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
ホスト出版物のサブタイトルSmarter Technology for a Healthier World, EMBC 2017 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1485-1488
ページ数4
ISBN(電子版)9781509028092
DOI
出版ステータスPublished - 2017 9 13
イベント39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - Jeju Island, Korea, Republic of
継続期間: 2017 7 112017 7 15

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

Other

Other39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
国/地域Korea, Republic of
CityJeju Island
Period17/7/1117/7/15

ASJC Scopus subject areas

  • 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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