Liu Xiaoming, Pan Jingling, Zhang Ying, Li Xiao, Tang Jinshan
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. 2023 Dec 8;68(24). doi: 10.1088/1361-6560/ad0d42.
Choroidal vessels account for 85% of all blood vessels in the eye, and the accurate segmentation of choroidal vessels from optical coherence tomography (OCT) images provides important support for the quantitative analysis of choroid-related diseases and the development of treatment plans. Although deep learning-based methods have great potential for segmentation, these methods rely on large amounts of well-labeled data, and the data collection process is both time-consuming and laborious.In this paper, we propose a novel asymmetric semi-supervised segmentation framework called SSCR, based on a student-teacher model, to segment choroidal vessels in OCT images. The proposed framework enhances the segmentation results with uncertainty-aware self-integration and transformation consistency techniques. Meanwhile, we designed an asymmetric encoder-decoder network called Pyramid Pooling SegFormer (APP-SFR) for choroidal vascular segmentation. The network combines local attention and global attention information to improve the model's ability to learn complex vascular features. Additionally, we proposed a boundary repair module that enhances boundary confidence by utilizing a repair head to re-predict selected fuzzy points and further refines the segmentation boundary.We conducted extensive experiments on three different datasets: the ChorVessel dataset with 400 OCT images, the Meibomian Glands (MG) dataset with 400 images, and the U2OS Cell Nucleus Dataset with 200 images. The proposed method achieved an average Dice score of 74.23% on the ChorVessel dataset, which is 2.95% higher than the fully supervised network (U-Net) and outperformed other comparison methods. In both the MG dataset and the U2OS cell nucleus dataset, our proposed SSCR method achieved average Dice scores of 80.10% and 87.26%, respectively.The experimental results show that our proposed methods achieve better segmentation accuracy than other state-of-the-art methods. The method is designed to help clinicians make rapid diagnoses of ophthalmic diseases and has potential for clinical application.
脉络膜血管占眼部所有血管的85%，从光学相干断层扫描（OCT）图像中准确分割脉络膜血管，为脉络膜相关疾病的定量分析和治疗方案的制定提供了重要支持。尽管基于深度学习的方法在分割方面具有很大潜力，但这些方法依赖大量标注良好的数据，且数据收集过程既耗时又费力。在本文中，我们提出了一种基于学生-教师模型的新型非对称半监督分割框架SSCR，用于分割OCT图像中的脉络膜血管。所提出的框架通过不确定性感知自集成和变换一致性技术增强分割结果。同时，我们设计了一种用于脉络膜血管分割的非对称编码器-解码器网络，称为金字塔池化分割Transformer（APP-SFR）。该网络结合局部注意力和全局注意力信息，以提高模型学习复杂血管特征的能力。此外，我们提出了一个边界修复模块，通过利用修复头重新预测选定的模糊点来增强边界置信度，并进一步细化分割边界。我们在三个不同的数据集上进行了广泛实验：包含400张OCT图像的脉络膜血管数据集、包含400张图像的睑板腺（MG）数据集以及包含200张图像的U2OS细胞核数据集。所提出的方法在脉络膜血管数据集上的平均Dice分数达到74.23%，比全监督网络（U-Net）高2.95%，并优于其他比较方法。在MG数据集和U2OS细胞核数据集中，我们提出的SSCR方法的平均Dice分数分别达到80.10%和87.26%。实验结果表明，我们提出的方法比其他现有方法具有更好的分割精度。该方法旨在帮助临床医生快速诊断眼科疾病，并具有临床应用潜力。
