Percutaneous nephrostomy (PCN) is a commonly used procedure for kidney surgeries. However, difficulties persist in precisely locating the PCN needle tip during its insertion into the kidney. Challenges for PCN needle guidance exist in two aspects: 1) Accurate tissue recognition, and 2) Renal blood vessel detection. In this study, we demonstrated an endoscopic optical coherence tomography (OCT) system for PCN needle guidance. Human kidney samples are utilized in the experiments. Different renal tissues including: 1) cortex, 2) medulla, 3) calyx, 4) fat, and 5) pelvis can be clearly distinguished based on their OCT imaging features. We conduct kidney perfusion experiments to mimic the renal blood flow. Our system can efficiently detect the blood flow in front of PCN needle using Doppler OCT function. To improve surgical guidance efficiency and alleviate the workload of radiologists, we employ convolutional neural network (CNN) methods to automate the procedure. Three CNN models including ResNet50, InceptionV3, and Xception were applied for tissue classification. All of them demonstrate promising prediction results, with InceptionV3 achieving the highest recognition accuracy of 99.6%. For automatic blood vessel detection, nnU-net was applied, and it exhibited intersection over unions (IoU) values of 0.8917 for blood vessel and 0.9916 for background.

中文翻译：

---

基于卷积神经网络的内窥镜光学相干断层扫描系统引导经皮肾造口术

经皮肾造口术（PCN）是肾脏手术的常用手术。然而，在 PCN 针尖插入肾脏期间精确定位仍然存在困难。 PCN针引导的挑战存在于两个方面：1）准确的组织识别，2）肾血管检测。在这项研究中，我们展示了用于 PCN 针引导的内窥镜光学相干断层扫描 (OCT) 系统。实验中使用人类肾脏样本。根据 OCT 成像特征，可以清楚地区分不同的肾组织，包括：1) 皮质、2) 髓质、3) 肾盏、4) 脂肪和 5) 骨盆。我们进行肾脏灌注实验来模拟肾血流。我们的系统可以利用多普勒 OCT 功能有效检测 PCN 针前面的血流。为了提高手术指导效率并减轻放射科医生的工作量，我们采用卷积神经网络（CNN）方法来自动化手术。 ResNet50、InceptionV3 和 Xception 三种 CNN 模型用于组织分类。所有这些都展示了有希望的预测结果，其中 InceptionV3 实现了 99.6% 的最高识别准确率。对于自动血管检测，应用了 nnU-net，其血管交集 (IoU) 值为 0.8917，背景为 0.9916。