Multispectral Optoacoustic Tomography (MSOT) resolves oxy- (HbO₂) and deoxy-hemoglobin (Hb) to perform vascular imaging. MSOT suffers from gradual signal attenuation with depth due to light-tissue interactions: an effect that hinders the precise manual segmentation of vessels. Furthermore, vascular assessment requires functional tests, which last several minutes and result in recording thousands of images. Here, we introduce a deep learning approach with a sparse-UNET (S-UNET) for automatic vascular segmentation in MSOT images to avoid the rigorous and time-consuming manual segmentation. We evaluated the S-UNET on a test-set of 33 images, achieving a median DICE score of 0.88. Apart from high segmentation performance, our method based its decision on two wavelengths with physical meaning for the task-at-hand: 850 nm (peak absorption of oxy-hemoglobin) and 810 nm (isosbestic point of oxy-and deoxy-hemoglobin). Thus, our approach achieves precise data-driven vascular segmentation for automated vascular assessment and may boost MSOT further towards its clinical translation.

多光谱光声断层扫描 (MSOT) 解析氧合血红蛋白 (HbO ₂ ) 和脱氧血红蛋白 (Hb) 以进行血管成像。由于光-组织相互作用，MSOT 会随着深度的增加而逐渐衰减信号：这种效应阻碍了血管的精确手动分割。此外，血管评估需要功能测试，该测试持续几分钟并记录数千张图像。在这里，我们引入了一种使用稀疏 UNET (S-UNET) 的深度学习方法，用于 MSOT 图像中的自动血管分割，以避免严格且耗时的手动分割。我们在 33 张图像的测试集上评估了 S-UNET，获得了 0.88 的中位 DICE 分数。除了高分割性能之外，我们的方法还基于对当前任务具有物理意义的两个波长进行决策：850 nm（氧合血红蛋白的峰值吸收）和 810 nm（氧合和脱氧血红蛋白的等吸光点）。因此，我们的方法实现了精确的数据驱动血管分割，用于自动血管评估，并可能进一步推动 MSOT 走向临床转化。