Significance: Two-dimensional (2-D) fully convolutional neural networks have been shown capable of producing maps of sO2 from 2-D simulated images of simple tissue models. However, their potential to produce accurate estimates in vivo is uncertain as they are limited by the 2-D nature of the training data when the problem is inherently three-dimensional (3-D), and they have not been tested with realistic images. Aim: To demonstrate the capability of deep neural networks to process whole 3-D images and output 3-D maps of vascular sO2 from realistic tissue models/images. Approach: Two separate fully convolutional neural networks were trained to produce 3-D 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 sO2 and the network output for 40 examples was 4.4% and the standard deviation was 4.5%. Conclusions: 3-D fully convolutional networks were shown capable of producing accurate sO2 maps using the full extent of spatial information contained within 3-D images generated under conditions mimicking real imaging scenarios. We demonstrate that networks can cope with some of the confounding effects present in real images such as limited-view artifacts and have the potential to produce accurate estimates in vivo.

中文翻译：

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迈向准确定量光声成像：从三个维度学习血管血氧饱和度。

意义：二维 (2-D) 全卷积神经网络已被证明能够从简单组织模型的 2-D 模拟图像生成 sO2 图。然而，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三维 (3-D) 时，它们受到训练数据的 2-D 性质的限制，并且它们还没有用真实图像进行测试。目的：展示深度神经网络处理整个 3-D 图像和从真实组织模型/图像输出血管 sO2 的 3-D 地图的能力。方法：训练两个独立的完全卷积神经网络，以从组织模型的多波长模拟图像中生成血管血氧饱和度和血管位置的 3-D 图。结果：40 个示例的真实平均血管 sO2 与网络输出之间的绝对差平均值为 4.4%，标准偏差为 4.5%。结论：3-D 全卷积网络显示能够使用在模拟真实成像场景的条件下生成的 3-D 图像中包含的全部空间信息生成准确的 sO2 地图。我们证明了网络可以应对真实图像中存在的一些混杂效应，例如视野受限的伪影，并有可能在体内产生准确的估计。3-D 全卷积网络显示能够使用在模拟真实成像场景的条件下生成的 3-D 图像中包含的全部空间信息生成准确的 sO2 地图。我们证明了网络可以应对真实图像中存在的一些混杂效应，例如视野受限的伪影，并有可能在体内产生准确的估计。3-D 全卷积网络显示能够使用在模拟真实成像场景的条件下生成的 3-D 图像中包含的全部空间信息生成准确的 sO2 地图。我们证明了网络可以应对真实图像中存在的一些混杂效应，例如视野受限的伪影，并有可能在体内产生准确的估计。