Department of Ophthalmology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
Department of Ophthalmology, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Gyeonggi-do, Republic of Korea.
Transl Vis Sci Technol. 2021 Jun 1;10(7):28. doi: 10.1167/tvst.10.7.28.
To study the efficacy of deep convolutional neural networks (DCNNs) to differentiate pachychoroid from nonpachychoroid on en face optical coherence tomography (OCT) images at the large choroidal vessel.
En face OCT images were collected from eyes with neovascular age-related macular degeneration, polypoidal choroidal vasculopathy, and central serous chorioretinopathy. All images were prelabeled pachychoroid or nonpachychoroid based on quantitative and qualitative criteria for choroidal morphology on multimodal imaging by two retina specialists. In total, 1188 nonpachychoroid and 884 pachychoroid images were used for training (80%) and validation (20%). Accuracy for identification of pachychoroid by DCNN models was analyzed. Trained models were tested on a test set containing 79 nonpachychoroid and 93 pachychoroid images.
The accuracy on the validation set was 94.1%, 93.2%, 94.7%, and 94.4% in DenseNet, GoogLeNet, ResNet50, and Inception-v3, respectively. On a test set, each model demonstrated accuracy of 80.2%, 83.1%, 89.5%, and 90.1% and an F1 score of 0.782, 0.824, 0.904, and 0.901, respectively.
DCNN models could classify pachychoroid and nonpachychoroid with good performance on OCT en face images. Automated classification of pachychoroid will be useful for tailored treatment of individual patients with exudative maculopathy.
En face OCT images can be used by DCNN for classification of pachychoroid.
研究深度卷积神经网络（DCNN）在大脉络膜血管的脉络膜 OCT 图像上区分厚脉络膜与非厚脉络膜的效果。
收集了新生血管性年龄相关性黄斑变性、息肉状脉络膜血管病变和中心性浆液性脉络膜视网膜病变患者的脉络膜 OCT 图像。两位视网膜专家根据多模态成像的脉络膜形态的定量和定性标准，对所有图像进行了厚脉络膜或非厚脉络膜的预标记。共使用了 1188 个非厚脉络膜和 884 个厚脉络膜图像进行训练（80%）和验证（20%）。分析了 DCNN 模型识别厚脉络膜的准确性。在包含 79 个非厚脉络膜和 93 个厚脉络膜图像的测试集中测试了训练好的模型。
在验证集上，DenseNet、GoogLeNet、ResNet50 和 Inception-v3 的准确率分别为 94.1%、93.2%、94.7%和 94.4%。在测试集上，每个模型的准确率分别为 80.2%、83.1%、89.5%和 90.1%，F1 评分分别为 0.782、0.824、0.904 和 0.901。
DCNN 模型可以在 OCT 图像上很好地区分厚脉络膜和非厚脉络膜。对渗出性黄斑病变患者进行厚脉络膜的自动分类将有助于为每位患者提供个性化治疗。
曹明轩
