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首页> 外文会议>International conference on intelligent data engineering and automated learning >Deep-Learning-Based Classification of Rat OCT Images After Intravitreal Injection of ET-1 for Glaucoma Understanding
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Deep-Learning-Based Classification of Rat OCT Images After Intravitreal Injection of ET-1 for Glaucoma Understanding

机译：玻璃体腔注射ET-1后青光眼对大鼠OCT图像的基于深度学习的分类

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Optical coherence tomography (OCT) is a useful technique to monitor retinal damage. We present an automatic method to accurately classify rodent OCT images in healthy and pathological (before and after 14days of intravitreal injection of Endothelin-1, respectively) making use of the DenseNet-201 architecture fine-tuned and a customized top-model. We validated the performance of the method on 1912 OCT images yielding promising results (AUC = 0.99 ± 0.01 in a P = 15 leave-P-out cross-validation). Besides, we also compared the results of the fine-tuned network with those achieved training the network from scratch, obtaining some interesting insights. The presented method poses a step forward in understanding pathological rodent OCT retinal images, as at the moment there is no known discriminating characteristic which allows classifying this type of images accurately. The result of this work is a very accurate and robust automatic method to distinguish between healthy and a rodent model of glaucoma, which is the backbone of future works dealing with human OCT images.
机译：光学相干断层扫描（OCT）是监测视网膜损伤的有用技术。我们提出一种自动方法，可使用经过微调的DenseNet-201体系结构和自定义的顶级模型对健康和病理性（分别在玻璃体内注射内皮素1的14天之前和之后）的啮齿动物OCT图像进行准确分类。我们在1912个OCT图像上验证了该方法的性能，并获得了可喜的结果（P = 15的留空P交叉验证中，AUC = 0.99±0.01）。此外，我们还将微调网络的结果与从头开始训练网络的结果进行了比较，获得了一些有趣的见解。所提出的方法在了解病理性啮齿类动物OCT视网膜图像方面迈出了一步，因为目前尚无已知的区分特征可以准确地对这类图像进行分类。这项工作的结果是一种非常准确和强大的自动方法，可以区分青光眼的健康模型和啮齿动物模型，这是未来处理人OCT图像的基础。

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