Liu Xiaoming, Zhou Kejie, Yao Junping, Wang Man, Zhang Ying
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, People's Republic of China.
Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan, 430065, People's Republic of China.
Phys Med Biol. 2022 Dec 9;67(24). doi: 10.1088/1361-6560/aca376.
Retinal biomarker in optical coherence tomography (OCT) images plays a key guiding role in the follow-up diagnosis and clinical treatment of eye diseases. Although there have been many deep learning methods to automatically process retinal biomarker, the detection of retinal biomarkers is still a great challenge due to the similar characteristics to normal tissue, large changes in size and shape and fuzzy boundary of different types of biomarkers. To overcome these challenges, a novel contrastive uncertainty network (CUNet) is proposed for retinal biomarkers detection in OCT images.In CUNet, proposal contrastive learning is designed to enhance the feature representation of retinal biomarkers, aiming at boosting the discrimination ability of network between different types of retinal biomarkers. Furthermore, we proposed bounding box uncertainty and combined it with the traditional bounding box regression, thereby improving the sensitivity of the network to the fuzzy boundaries of retinal biomarkers, and to obtain a better localization result.Comprehensive experiments are conducted to evaluate the performance of the proposed CUNet. The experimental results on two datasets show that our proposed method achieves good detection performance compared with other detection methods.We propose a method for retinal biomarker detection trained by bounding box labels. The proposal contrastive learning and bounding box uncertainty are used to improve the detection of retinal biomarkers. The method is designed to help reduce the amount of work doctors have to do to detect retinal diseases.
光学相干断层扫描（OCT）图像中的视网膜生物标志物在眼科疾病的后续诊断和临床治疗中起着关键的指导作用。尽管已经有许多深度学习方法用于自动处理视网膜生物标志物，但由于其与正常组织特征相似、大小和形状变化大以及不同类型生物标志物边界模糊，视网膜生物标志物的检测仍然是一个巨大的挑战。为了克服这些挑战，提出了一种用于在OCT图像中检测视网膜生物标志物的新型对比不确定性网络（CUNet）。在CUNet中，提议对比学习旨在增强视网膜生物标志物的特征表示，旨在提高网络对不同类型视网膜生物标志物的辨别能力。此外，我们提出了边界框不确定性，并将其与传统的边界框回归相结合，从而提高网络对视网膜生物标志物模糊边界的敏感性，并获得更好的定位结果。进行了综合实验以评估所提出的CUNet的性能。在两个数据集上的实验结果表明，与其他检测方法相比，我们提出的方法实现了良好的检测性能。我们提出了一种通过边界框标签训练的视网膜生物标志物检测方法。提议对比学习和边界框不确定性用于改进视网膜生物标志物的检测。该方法旨在帮助减少医生检测视网膜疾病所需的工作量。
