分割
人工智能
计算机科学
适应(眼睛)
域适应
模式识别(心理学)
领域(数学分析)
自然语言处理
神经科学
心理学
数学
数学分析
分类器(UML)
作者
Yixin Chen,Yajun Gao,Lei Zhu,Jianan Li,Yan Wang,Jiakui Hu,Hongbin Han,Yanye Lu,Zhaoheng Xie
出处
期刊:IEEE transactions on radiation and plasma medical sciences
[Institute of Electrical and Electronics Engineers]
日期:2024-04-19
卷期号:8 (8): 926-938
被引量:1
标识
DOI:10.1109/trpms.2024.3391285
摘要
Early and accurate identification of intracranial hemorrhage (ICH) is crucial for treatment, but the inherently low contrast resolution of CT imaging poses challenges in identification of specific cerebral regions, impacting effective and timely clinical decision-making. We propose BraSEDA, a CT-based unsupervised domain adaptation (UDA) model designed to assist in the identification of brain regions. BraSEDA framework utilizes a cross-modal instance normalization (CMIN) module for enhancing CT image structural features and creating high-quality pseudo MR images. A multi-level CMIN architecture is also introduced for further improvement. The BraSEDA framework improved the quality of pseudo MR images in head CT to MR domain adaptation task, as reflected by the lowest FID scores 95.0± 12.1 (p-value<0.001) with and highest BC scores 0.915± 0.396 (p-value <0.01), compared to other UDA methods. It also demonstrated superior segmentation performance in other four traditional medical UDA tasks. The BraSEDA framework effectively addresses the challenge of low-contrast areas in CT brain images for ICH cases. It enables precise identification of low-contrast brain regions, making it a potential tool for assisting in the accurate and prompt initial diagnosis of ICH in emergency medical settings. The code and well-trained model has been publicly available: https://github.com/YixinChen-AI/BraSEDA.
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