深度学习用于两种遗传性视网膜疾病光学相干断层扫描中保留的光感受器分割
Deep learning for the segmentation of preserved photoreceptors on optical coherence tomography in two inherited retinal diseases.
作者信息
Camino Acner, Wang Zhuo, Wang Jie, Pennesi Mark E, Yang Paul, Huang David, Li Dengwang, Jia Yali
机构信息
Casey Eye Institute, Oregon Health and Science University, Portland, OR, 27239, USA.
These authors contributed equally to this manuscript.
出版信息
Biomed Opt Express. 2018 Jun 12;9(7):3092-3105. doi: 10.1364/BOE.9.003092. eCollection 2018 Jul 1.
Abstract
The objective quantification of photoreceptor loss in inherited retinal degenerations (IRD) is essential for measuring disease progression, and is now especially important with the growing number of clinical trials. Optical coherence tomography (OCT) is a non-invasive imaging technology widely used to recognize and quantify such anomalies. Here, we implement a versatile method based on a convolutional neural network to segment the regions of preserved photoreceptors in two different IRDs (choroideremia and retinitis pigmentosa) from OCT images. An excellent segmentation accuracy (~90%) was achieved for both IRDs. Due to the flexibility of this technique, it has potential to be extended to additional IRDs in the future.
摘要
对遗传性视网膜变性（IRD）中光感受器损失进行客观量化对于衡量疾病进展至关重要，并且随着临床试验数量的不断增加，现在尤为重要。光学相干断层扫描（OCT）是一种广泛用于识别和量化此类异常的非侵入性成像技术。在此，我们基于卷积神经网络实现了一种通用方法，用于从OCT图像中分割出两种不同IRD（脉络膜视网膜病变和视网膜色素变性）中保留的光感受器区域。两种IRD均实现了出色的分割准确率（约90%）。由于该技术的灵活性，未来有潜力扩展到其他IRD。
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