@inproceedings{f03085578a014cc5b6aafeb6470b94fd,
title = "CNSeg-GAN: A Lightweight Generative Adversarial Network For Segmentation of CRL and NT From First-Trimester Fetal Ultrasound",
abstract = "This paper presents a novel, low-compute and efficient generative adversarial network (GAN) design for automatic segmentation called CNSeg-GAN, which combines 1-D kernel factorized networks, spatial and channel attention, and multi-scale aggregation mechanisms in a conditional GAN (cGAN) fashion. The proposed CNSeg-GAN architecture is trained and tested on a first-trimester ultrasound (US) scan video dataset for automatic detection and segmentation of anatomical structures in the midsagittal plane to enable Crown Rump Length (CRL) and Nuchal Translucency (NT) measurement. Experimental results shows that the proposed CNSeg-GAN is x15 faster than U-Net and yields mIoU of 78.20\% on the CRL and 89.03\% on the NT dataset, respectively with only 2.19 millions in parameters. The accuracy of this lightweight design makes it well-suited for real-time deployment in future work.",
keywords = "First trimester, generative adversarial network, midsagittal plane, ultrasound, video segmentation",
author = "Sarker, \{Md Mostafa Kamal\} and Robail Yasrab and Mohammad Alsharid 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/ISBI53787.2023.10230781",
language = "British English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
booktitle = "2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023",
address = "United States",
}