方法：在PSPNet模型结构的基础上构建Phase-fusion PSPNet网络结构用于翼状胬肉病灶的分割，该网络在金字塔池化模块后接入阶段上采样模块，以分阶段增大为原则逐步上采样，减少信息丢失，适合于边缘模糊的分割任务。将南京医科大学附属眼科医院提供的翼状胬肉眼表图像517张分为训练集(330张)、验证集(37张)、测试集(150张)，其中训练集和验证集图像用于训练，测试集图像仅用于测试。比较翼状胬肉病灶智能分割和专家标注的结果。

结果：构建Phase-fusion PSPNet网络结构针对测试数据集的翼状胬肉病灶分割单类平均交并比(MIOU)和平均像素精确度(MPA)分别为86.31%和91.91%； 翼状胬肉单类交并比(IOU)和像素精确度(PA)分别为77.64%和86.10%。

结论：卷积神经网络可以实现翼状胬肉病灶的精准分割，有助于为医生进行进一步疾病诊断和手术建议提供重要参考，同时实现翼状胬肉智能诊断的可视化。

METHODS: The network structure of Phase-fusion PSPNet for the segmentation of pterygium lesions is proposed based on the PSPNet model structure. In our network, the up-sampling module is connected behind the pyramid pooling module, which gradually increase the sampling based on the principle of phased increase. Therefore, the information loss is reduced, it is suitable for segmentation tasks with fuzzy edges. The experiments conducted on the dataset provided by the Affiliated Eye Hospital of Nanjing Medical University, which includes 517 ocular surface photographic images of pterygium were divided into training set(330 images), validation set(37 images)and test set(150 images), which the training set and the validation set images are used for training, and the test set images are only used for testing. Comparing results of intelligent segmentation and expert annotation of pterygium lesions.

RESULTS: Phase-fusion PSPNet network structure for pterygium mean intersection over union(MIOU)and mean average precision(MPA)were 86.31% and 91.91%, respectively, and pterygium intersection over union(IOU)and average precision(PA)were 77.64% and 86.10%, respectively.

CONCLUSION: Convolutional neural networks can segment pterygium lesions with high precision, which is helpful to provide an important reference for doctors' further diagnosis of disease and surgical recommendations, and can also visualize the pterygium intelligent diagnosis.