D2ANET: Densely Attentional-Aware Network for First Trimester Ultrasound CRL and NT Segmentation

Mourad Gridach*, Robail Yasrab, Lior Drukker, Aris T. Papageorghiou, J. Alison Noble

*Corresponding author for this work

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

Abstract

Manual annotation of medical images is time consuming for clinical experts; therefore, reliable automatic segmentation would be the ideal way to handle large medical datasets. In this paper, we are interested in detection and segmentation of two fundamental measurements in the first trimester ultrasound (US) scan: Nuchal Translucency (NT) and Crown Rump Length (CRL). There can be a significant variation in the shape, location or size of the anatomical structures in the fetal US scans. We propose a new approach, namely Densely Attentional-Aware Network for First Trimester Ultrasound CRL and NT Segmentation (DA2Net), to encode variation in feature size by relying on the powerful attention mechanism and densely connected networks. Our results show that the proposed D2ANet offers high pixel agreement (mean JSC = 84.21) with expert manual annotations.

Original languageEnglish
Title of host publication2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023
PublisherIEEE Computer Society
ISBN (Electronic)9781665473583
DOIs
StatePublished - 2023
Externally publishedYes
Event20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 - Cartagena, Colombia
Duration: 18 Apr 202321 Apr 2023

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2023-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference20th IEEE International Symposium on Biomedical Imaging, ISBI 2023
Country/TerritoryColombia
CityCartagena
Period18/04/2321/04/23

Keywords

  • Channel Attention
  • First Trimester
  • Spatial Attention
  • Ultrasound
  • Video Segmentation

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