Background

Reliable localization and tracking of the eye region in the pediatric hospital environment is a significant challenge for clinical decision support and patient monitoring applications. Existing work in eye localization achieves high performance on adult datasets but performs poorly in the busy pediatric hospital environment, where face appearance varies because of age, position and the presence of medical equipment.

Methods

We developed two new datasets: a training dataset using public image data from internet searches, and a test dataset using 59 recordings of patients in a pediatric intensive care unit. We trained two eye localization models, using the Faster R-CNN algorithm to fine-tune a pre-trained ResNet base network, and evaluated them using the images from the pediatric ICU.

Results

The convolutional neural network trained with a combination of adult and child data achieved an 79.7% eye localization rate, significantly higher than the model trained on adult data alone. With additional pre-processing to equalize image contrast, the localization rate rises to 84%.

Conclusion

The results demonstrate the potential of convolutional neural networks for eye localization and tracking in a pediatric ICU setting, even when training data is limited. We obtained significant performance gains by adding task-specific images to the training dataset, highlighting the need for custom models and datasets for specialized applications like pediatric patient monitoring. The moderate size of our added training dataset shows that it is feasible to develop an internal training dataset for clinical computer vision applications, and apply it with transfer learning to fine-tune existing pre-trained models.

中文翻译：

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使用卷积神经网络对住院婴儿和儿童进行自动眼睛定位

背景

儿科医院环境中眼睛区域的可靠定位和跟踪对于临床决策支持和患者监测应用是一项重大挑战。现有的眼睛定位工作可以在成人数据集上实现高性能，但是在繁忙的儿科医院环境中表现不佳，在该环境中，面部外观会因年龄，位置和医疗设备的存在而发生变化。

方法

我们开发了两个新的数据集：一个使用互联网搜索中的公共图像数据的训练数据集，以及一个使用小儿重症监护室中59位患者的记录的测试数据集。我们训练了两个眼睛定位模型，使用Faster R-CNN算法微调了预训练的ResNet基本网络，并使用儿科ICU的图像对其进行了评估。

结果

用成人和儿童数据组合训练的卷积神经网络实现了79.7％的眼睛定位率，远高于仅使用成人数据训练的模型。通过额外的预处理以使图像对比度均等，定位率可提高到84％。

结论

结果表明，即使在训练数据有限的情况下，卷积神经网络在儿科ICU设置中进行眼睛定位和跟踪的潜力。通过将特定于任务的图像添加到训练数据集中，我们强调了对特殊应用（例如小儿患者监护）的定制模型和数据集的需求，从而获得了可观的性能提升。我们添加的训练数据集的适度大小表明，为临床计算机视觉应用开发内部训练数据集，并将其与传递学习一起应用以微调现有的预训练模型是可行的。