Translational vision science & technology · 2021
Classification of Pachychoroid on Optical Coherence Tomographic En Face Images Using Deep Convolutional Neural Networks.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Roberson Mya L, Strassle Paula D, Fasehun Luther-King Okunade, Erim Daniel O, Deune E Gene, Ogunleye Adeyemi A
Аннотация
PURPOSE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
TRANSLATIONAL RELEVANCE: En face OCT images can be used by DCNN for classification of pachychoroid.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
TRANSLATIONAL RELEVANCE: En face OCT images can be used by DCNN for classification of pachychoroid.