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首页> 外文期刊>Journal of Neuroscience Methods >DeepVOG: Open-source pupil segmentation and gaze estimation in neuroscience using deep learning
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DeepVOG: Open-source pupil segmentation and gaze estimation in neuroscience using deep learning

机译：DeepVog：利用深度学习的神经科学的开源瞳孔分割和凝视估计

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Background: A prerequisite for many eye tracking and video-oculography (VOG) methods is an accurate localization of the pupil. Several existing techniques face challenges in images with artifacts and under naturalistic low-light conditions, e.g. with highly dilated pupils. New method: For the first time, we propose to use a fully convolutional neural network (FCNN) for segmentation of the whole pupil area, trained on 3946 VOG images hand-annotated at our institute. We integrate the FCNN into DeepVOG, along with an established method for gaze estimation from elliptical pupil contours, which we improve upon by considering our FCNN's segmentation confidence measure. Results: The FCNN output simultaneously enables us to perform pupil center localization, elliptical contour estimation and blink detection, all with a single network and with an assigned confidence value, at framerates above 130 Hz on commercial workstations with GPU acceleration. Pupil centre coordinates can be estimated with a median accuracy of around 1.0 pixel, and gaze estimation is accurate to within 0.5 degrees. The FCNN is able to robustly segment the pupil in a wide array of datasets that were not used for training. Comparison with existing methods: We validate our method against gold standard eye images that were artificially rendered, as well as hand-annotated VOG data from a gold-standard clinical system (EyeSeeCam) at our institute. Conclusions: Our proposed FCNN-based pupil segmentation framework is accurate, robust and generalizes well to new VOG datasets. We provide our code and pre-trained'FCNN model open-source and for free under www. github. com/pydsgz/DeepVOG.
机译：背景：许多眼跟踪和视频眼图（VOG）方法的先决条件是学生的准确本地化。几种现有技术面临着伪影和自然的低光条件下的图像挑战，例如，在自然主义的低光线下。用高度扩张的瞳孔。新方法：首次提议使用完全卷积的神经网络（FCNN）进行整个瞳孔区域的分割，培训于3946 Vog图像在我们的研究所录音。我们将FCNN集成到DeepVog中，以及从椭圆形瞳孔轮廓的凝视估计的建立方法，我们通过考虑FCNN的分割置信度量来改善。结果：FCNN输出同时使我们能够执行瞳孔中心定位，椭圆形轮廓估计和眨眼检测，所有与单个网络和分配的置信度值，在130 Hz上的商业工作站上以上的商业工作站上的FRAMERATES进行GPU加速度。瞳孔中心坐标可以通过大约1.0像素的中值精度估算，并且凝视估计精确到0.5度以内。 FCNN能够在不用于培训的各种数据集中稳健地将学生段段。与现有方法的比较：我们验证了我们对人工呈现的金标标准眼图像的方法，以及来自我们研究所的金标准临床系统（Eyeseecam）的手工注释的VoG数据。结论：我们提出的基于FCNN的瞳孔分割框架是准确，强大的，概括为新的VOG数据集。我们提供了我们的代码和预先训练的--FCNN模型开源，免费提供免费。 github。 com / pydsgz / deepvog。

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