School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada.
School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada; Department of Internal Medicine, Section of Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA; Center for Biomedical Informatics, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA; Alzheimer's Disease Research Center, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
Comput Biol Med. 2023 Jun;159:106595. doi: 10.1016/j.compbiomed.2023.106595. Epub 2023 Mar 2.
Medical images such as Optical Coherence Tomography (OCT) images acquired from different devices may show significantly different intensity profiles. An automatic segmentation model trained on images from one device may perform poorly when applied to images acquired using another device, resulting in a lack of generalizability. This study addresses this issue using domain adaptation methods improved by Cycle-Consistent Generative Adversarial Networks (CycleGAN), especially when the ground-truth labels are only available in the source domain.
A two-stage pipeline is proposed to generate segmentation in the target domain. The first stage involves the training of a state-of-the-art segmentation model in the source domain. The second stage aims to adapt the images from the target domain to the source domain. The adapted target domain images are segmented using the model in the first stage. Ablation tests were performed with integration of different loss functions, and the statistical significance of these models is reported. Both the segmentation performance and the adapted image quality metrics were evaluated.
Regarding the segmentation Dice score, the proposed model ssppg achieves a significant improvement of 46.24% compared to without adaptation and reaches 87.4% of the upper limit of the segmentation performance. Furthermore, image quality metrics, including FID and KID scores, indicate that adapted images with better segmentation also have better image qualities.
The proposed method demonstrates the effectiveness of segmentation-driven domain adaptation in retinal imaging processing. It reduces the labor cost of manual labeling, incorporates prior anatomic information to regulate and guide domain adaptation, and provides insights into improving segmentation qualities in image domains without labels.
来自不同设备的医学图像（如光学相干断层扫描（OCT）图像）可能显示出明显不同的强度分布。在一个设备上的图像上训练的自动分割模型在应用于另一个设备获取的图像时可能表现不佳，导致缺乏泛化能力。本研究使用由循环一致性生成对抗网络（CycleGAN）改进的域自适应方法解决了这个问题，特别是在目标域中只有源域标签可用的情况下。
提出了一种两阶段流水线来生成目标域的分割。第一阶段涉及在源域中训练最先进的分割模型。第二阶段旨在将目标域的图像适应到源域。使用第一阶段的模型对适应后的目标域图像进行分割。进行了消融测试，整合了不同的损失函数，并报告了这些模型的统计显著性。评估了分割性能和适应图像质量指标。
在分割 Dice 分数方面，与没有自适应相比，所提出的模型 ssppg 显著提高了 46.24%，达到了分割性能上限的 87.4%。此外，图像质量指标，包括 FID 和 KID 评分，表明具有更好分割的适应图像也具有更好的图像质量。
所提出的方法证明了分割驱动的域自适应在视网膜成像处理中的有效性。它减少了手动标记的劳动成本，结合了先验解剖信息来调节和指导域自适应，并为改善无标签图像域中的分割质量提供了思路。
