对于共聚焦激光内分显微镜图像消噪的上下文感知动态过网络
Jingjun Zhou1, Xiangjiang Dong2, Qian Liu1,3
1School of Biomedical Engineering, Hainan University, 570228 Haikou, People's Republic of China.
Physics in medicine and biology
|August 30, 2023
概括
聚焦激光内分显微镜 (CLE) 图像往往很,阻碍了胃肠道疾病的诊断. 我们开发了一种新的自我监督的除方法,CLENet,以提高图像质量并减少误诊率.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 胃肠病学 胃肠病学
背景情况:
- 聚焦激光内微镜 (CLE) 是诊断胃肠道疾病的关键技术.
- 图像噪声源于光纤捆的物理结构,显著降低了CLE图像质量.
- 现有的无声化技术在CLE图像中难以实现现实的噪声模式.
研究的目的:
- 在CLE图像中开发先进的无声化方法,以实现现实的噪声.
- 提高使用CLE检测胃肠道疾病的诊断准确度.
- 创建一个不需要清洁参考图像的自主监督的无雾化模型.
主要方法:
- 构建了一个针对CLE数据的现实噪声模型.
- 开发了情境感知内核估计 (CLENet) 来处理不均的噪音.
- 在U型网络 (U-CLENet) 中集成了一个多尺度的动态融合模块.
主要成果:
- 在CLE图像数据集上实现了与现有方法相比或优于现有方法的性能.
- 对于拟议的无线化网络来说,已经证明了低计算复杂度.
- 成功地从CLE图像中删除了乘法和加法白噪声.
结论:
- 拟议的U-CLENet有效地提高了CLE图像的视觉质量.
- 提高图像清晰度有助于诊断胃肠道瘤的各个阶段.
- 该方法支持计算机辅助诊断,可能减少临床误诊率.
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