HyFormer:用于视网膜OCT图像分割的混合变压器-CNN架构
Qingxin Jiang1, Ying Fan2, Menghan Li2
1MIPAV Lab, School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China.
Biomedical optics express
|November 18, 2024
概括
一个新的混合网络HyFormer通过结合变压器和卷积特征准确地分割视网膜光学连贯性断层扫描 (OCT) 图像. 这种高效的方法可以改善视网膜疾病的诊断,如近视力引性黄斑病和与年龄相关的退化.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 光学连贯断层扫描 (OCT) 对于诊断和规划视网膜疾病的治疗至关重要.
- 视网膜OCT图像细分对于识别病变和组织结构至关重要,有助于眼科医生的决定.
- 精确的细分需要捕捉全球背景和细微的局部细节的网络,这是由于视网膜特征的强度不同和近距离的挑战.
研究的目的:
- 提出HyFormer,一个高效,轻量级和强大的混合网络架构,用于多类视网膜OCT图像细分.
- 为了应对在视网膜OCT图像中同时捕捉全球和本地特征的挑战.
- 增强特征提取和集成,以提高细分精度.
主要方法:
- HyFormer使用并行变压器和卷积编码器进行独立的特征捕获.
- 一个多尺度封闭的注意力块和组定位嵌入增强了变压器编码器的特征提取.
- 解码器中的三路融合模块集成了功能,并补充了基于类激活图的交叉损失函数.
主要成果:
- HyFormer在私人近视力引性黑眼病和公共AROI数据集上展示了卓越的细分性能和稳定性.
- 该网络有效地细分了与年龄相关的退行相关的视网膜层和病变.
- 评估证实了HyFormer在捕获全球和本地特征方面的能力,以便精确地对海外国家和地区的图像进行细分.
结论:
- HyFormer提供了一个有前途的解决方案,用于准确有效地细分视网膜OCT图像.
- 混合架构有效地解决了复杂视网膜图像中全球和本地特征提取的需求.
- 这种方法有可能在各种视网膜疾病的诊断和治疗规划中显著帮助.
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