论文标题
OMSN和FAROS：八八微结构分割网络和完全注释的视网膜八片分段数据集
OMSN and FAROS: OCTA Microstructure Segmentation Network and Fully Annotated Retinal OCTA Segmentation Dataset
论文作者
论文摘要
缺乏有效的分割方法和完全标记的数据集限制了对视网膜血管网络（RVN）（RVN）和Foveal Asvascular Zone（FAZ）（FAZ）的光学相干层析成像造影术（八章）的全面评估，它们在眼科和系统疾病评估中具有很高的价值。在这里，我们通过将基于编码器的架构与多尺度的跳过连接和基于分裂的基于分裂的剩余网络重新发出结合，从而介绍了一个创新的八章微观结构细分网络（OMSN），在促进更好的模型融合和特征表现上，将基于分裂的剩余网络重新发出。所提出的OMSN实现了RVN或/和FAZ分割的出色单一/多任务性能。特别是，多任务模型上的评估指标优于同一数据集上的单个任务模型。在此基础上，完全注释的视网膜八片分段（FAROS）数据集是半自动构造的，填充了像素级全标记的八颗数据集的空缺。 OMSN多任务分段模型对FAROS进行了重新训练，进一步证明了其同时RVN和FAZ细分的出色准确性。
The lack of efficient segmentation methods and fully-labeled datasets limits the comprehensive assessment of optical coherence tomography angiography (OCTA) microstructures like retinal vessel network (RVN) and foveal avascular zone (FAZ), which are of great value in ophthalmic and systematic diseases evaluation. Here, we introduce an innovative OCTA microstructure segmentation network (OMSN) by combining an encoder-decoder-based architecture with multi-scale skip connections and the split-attention-based residual network ResNeSt, paying specific attention to OCTA microstructural features while facilitating better model convergence and feature representations. The proposed OMSN achieves excellent single/multi-task performances for RVN or/and FAZ segmentation. Especially, the evaluation metrics on multi-task models outperform single-task models on the same dataset. On this basis, a fully annotated retinal OCTA segmentation (FAROS) dataset is constructed semi-automatically, filling the vacancy of a pixel-level fully-labeled OCTA dataset. OMSN multi-task segmentation model retrained with FAROS further certifies its outstanding accuracy for simultaneous RVN and FAZ segmentation.
