Shortwave infrared window (SWIR: 1000-1700 nm) represents a major improvement compared to the NIR-I region (700-900 nm) in terms of temporal and spatial resolutions in depths down to 4 mm. SWIR is a fast and cheap alternative to more precise methods such as X-ray and opto-acoustic imaging. Main obstacles in SWIR imaging are the noise and scattering from tissues and skin that reduce the precision of the method. We demonstrate that the combination of SWIR in vivo imaging in the NIR-IIb region (1500-1700 nm) with advanced deep learning image analysis allows to overcome these obstacles and making a large step forward to high resolution imaging: it allows to precisely segment vessels from tissues and noise, provides morphological structure of the vessels network, with learned pseudo-3D shape, their relative position, dynamic information of blood vascularization in depth in small animals and distinguish the vessels types: artieries and veins. For demonstration we use neural network IterNet that exploits structural redundancy of the blood vessels, which provides a useful analysis tool for raw SWIR images.

中文翻译：

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深度学习：迈向高分辨率的体内短波红外成像

与NIR-1区域（700-900 nm）相比，短波红外窗口（SWIR：1000-1700 nm）在深度和时间分辨率上低至4 mm方面代表了一项重大改进。SWIR是X射线和光声成像等更精确方法的一种快速而廉价的替代方法。SWIR成像的主要障碍是来自组织和皮肤的噪声和散射，降低了该方法的精度。我们证明NIR-IIb区域（1500-1700 nm）中的SWIR体内成像与先进的深度学习图像分析相结合可以克服这些障碍，并向高分辨率成像迈进了一大步：它可以精确地分割血管从组织和噪声中获取血管网络的形态结构，了解伪3D形状，它们的相对位置，深入了解小动物血液血管化的动态信息，并区分血管类型：大动脉和静脉。为了进行演示，我们使用了神经网络IterNet，该网络利用了血管的结构冗余，为原始SWIR图像提供了有用的分析工具。