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End-to-end First Trimester Fetal Ultrasound Video Automated CRL and NT Segmentation.
Yasrab, Robail; Fu, Zeyu; Drukker, Lior; Lee, Lok Hin; Zhao, He; Papageorghiou, Aris T; Noble, Alison J.
Afiliación
  • Yasrab R; Institute of Biomedical Engineering, University of Oxford, Oxford, UK.
  • Fu Z; Institute of Biomedical Engineering, University of Oxford, Oxford, UK.
  • Drukker L; Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK.
  • Lee LH; Rabin Medical Center, Sackler Faculty of Medicine, Tel-Aviv University, Israel.
  • Zhao H; Institute of Biomedical Engineering, University of Oxford, Oxford, UK.
  • Papageorghiou AT; Institute of Biomedical Engineering, University of Oxford, Oxford, UK.
  • Noble AJ; Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK.
Proc IEEE Int Symp Biomed Imaging ; 20222022 Apr 28.
Article en En | MEDLINE | ID: mdl-36643819
ABSTRACT
This study presents a novel approach to automatic detection and segmentation of the Crown Rump Length (CRL) and Nuchal Translucency (NT), two essential measurements in the first trimester US scan. The proposed method automatically localises a standard plane within a video clip as defined by the UK Fetal Abnormality Screening Programme. A Nested Hourglass (NHG) based network performs semantic pixel-wise segmentation to extract NT and CRL structures. Our results show that the NHG network is faster (19.52% < GFlops than FCN32) and offers high pixel agreement (mean-IoU=80.74) with expert manual annotations.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Proc IEEE Int Symp Biomed Imaging Año: 2022 Tipo del documento: Article País de afiliación: Reino Unido
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Proc IEEE Int Symp Biomed Imaging Año: 2022 Tipo del documento: Article País de afiliación: Reino Unido