@inproceedings{b0fc1ed8967043dfaa3ed840849728bd,
title = "D2ANET: Densely Attentional-Aware Network for First Trimester Ultrasound CRL and NT Segmentation",
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.",
keywords = "Channel Attention, First Trimester, Spatial Attention, Ultrasound, Video Segmentation",
author = "Mourad Gridach and Robail Yasrab and Lior Drukker and Papageorghiou, {Aris T.} and Noble, {J. Alison}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 ; Conference date: 18-04-2023 Through 21-04-2023",
year = "2023",
doi = "10.1109/ISBI53787.2023.10230727",
language = "אנגלית",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
booktitle = "2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023",
address = "ארצות הברית",
}