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不确定性引导的交叉层融合网络用于视网膜CT的图像细分

Jiaxin Wang¹, Weifang Zhu¹, Dehui Xiang¹

¹MIPAV Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, China.

Medical physics

|September 2, 2025

概括

这项研究引入了以不确定性为导向的视网膜光学连贯断层扫描 (OCT) 分段网络,通过关注不确定区域来提高准确性. 这种新方法提高了医疗成像分析的细分可靠性和模型预测.

科学领域:

医学成像
深度学习
计算机视觉

背景情况:

深度学习在光学连贯断层扫描 (OCT) 细分方面表现出色,但由于数据分布和网络限制,它面临不确定性.
准确的不确定性估计对于可靠的信心评估和改进的OCT细分预测至关重要.

研究的目的:

提出一种新的以不确定性为导向的跨层融合网络 (UGCFNet),以加强视网膜CT的细分.
将不确定性量化整合到深度神经网络训练中,以提高细分精度.

主要方法:

使用编码器-解码器架构量化多个阶段的不确定性,以关注高不确定性区域.
实现跨层特征融合,以全面了解语义和形态细节.
纳入改进的贝叶斯神经网络和不确定性意识损失功能,以有效地模拟不确定性.

主要成果:

在AI-Challenger和OIMHS OCT细分数据集上取得了最先进的表现.
在相应的数据集中显示出高准确度,平均Dice相似系数为79.47%和93.22%.
广泛的实验证实了该模型在OCT大规模数据集上的有效性.

结论:

拟议的UGCFNet显著提升了视网膜CT的细分.
整合不确定性指导和跨级特征融合导致更可靠和更准确的细分结果.

关键词:

深度学习视网膜OCT图像细分不确定性量化不确定性导致的损失

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