论文标题
迈向准确的定量光声成像：在3D中学习血管血氧饱和度
Towards accurate quantitative photoacoustic imaging: learning vascular blood oxygen saturation in 3D
论文作者
论文摘要
意义：2D完全卷积神经网络已显示能够从简单组织模型的2D模拟图像中产生$ _2 $的图。但是，它们在体内产生准确估计的潜力是不确定的，因为当问题本质上是3D时，它们受训练数据的2D性质的限制，并且尚未使用逼真的图像进行测试。 目的：展示深神经网络处理整个3D图像和输出血管的3D图的能力，因此来自现实的组织模型/图像的$ _2 $。 方法：训练了两个单独的完全卷积神经网络，以产生来自组织模型的多波长模拟图像的血管血氧饱和度和血管位置的3D图。 结果：真实平均容器之间绝对差的平均值，因此$ _2 $和40个示例的网络输出为4.4％，标准偏差为4.5％。 结论：显示3D完全卷积网络能够产生准确的绘制，因此使用在模拟真实成像场景的条件下生成的3D图像中包含的全部空间信息$ _2 $地图。这项工作表明，网络可以应对实际图像中存在的一些混杂效应，例如限量视图，并有可能在体内产生准确的估计值。
Significance: 2D fully convolutional neural networks have been shown capable of producing maps of sO$_2$ from 2D simulated images of simple tissue models. However, their potential to produce accurate estimates in vivo is uncertain as they are limited by the 2D nature of the training data when the problem is inherently 3D, and they have not been tested with realistic images. Aim: To demonstrate the capability of deep neural networks to process whole 3D images and output 3D maps of vascular sO$_2$ from realistic tissue models/images. Approach: Two separate fully convolutional neural networks were trained to produce 3D maps of vascular blood oxygen saturation and vessel positions from multiwavelength simulated images of tissue models. Results: The mean of the absolute difference between the true mean vessel sO$_2$ and the network output for 40 examples was 4.4% and the standard deviation was 4.5%. Conclusions: 3D fully convolutional networks were shown capable of producing accurate sO$_2$ maps using the full extent of spatial information contained within 3D images generated under conditions mimicking real imaging scenarios. This work demonstrates that networks can cope with some of the confounding effects present in real images such as limited-view artefacts, and have the potential to produce accurate estimates in vivo.
