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    题名: W-Net: two-stage segmentation for multi-center kidney ultrasound
    作者: 羅崇銘
    Lo, Chung-Ming;Chang, Yu-Chi;Chen, Yi-Kong;Wu, Ping-Hsun;Luh, Hsing
    贡献者: 圖檔所
    关键词: W-Net;kidney;ultrasound;segmentation;multicenter
    日期: 2024-06
    上传时间: 2024-11-15
    摘要: The global death rate of chronic kidney disease (CKD) continues to increase and becomes a serious health issue. Ultrasound imaging is significant in the evaluation of CKD. However, there is a challenge posed by quality differences in multi-center datasets for kidney ultrasound image segmentation. Confronting the problem, this study applied the W-Net based on the double U-Net architecture which was respectively trained in two stages. In the first stage, the pixel-wise nnU-Net was pretrained by 4586 images and fine-tuned by 534 images. In the second stage, the region-wise nnU-Net was trained from the inference of the first stage by 72 images and achieved a 6.95% improvement from the first stage. It can bring more evidence about the practical application of deep learning-based segmentation in kidney ultrasound and its potential use in clinics.
    關聯: 2024 IEEE Conference on Artificial Intelligence, IEEE
    数据类型: conference
    DOI 連結: https://doi.org/10.1109/CAI59869.2024.00274
    DOI: 10.1109/CAI59869.2024.00274
    显示于类别:[圖書資訊與檔案學研究所] 會議論文

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