Needle positioning is essential for various medical applications such as epidural anaesthesia. Physicians rely on their instincts while navigating the needle in epidural spaces. Thereby, identifying the tissue structures may be helpful to the physician as they can provide additional feedback in the needle insertion process. To this end, we propose a deep neural network that classifies the tissues from the phase and intensity data of complex OCT signals acquired at the needle tip. We investigate the performance of the deep neural network in a limited labelled dataset scenario and propose a novel contrastive pretraining strategy that learns invariant representation for phase and intensity data. We show that with 10% of the training set, our proposed pretraining strategy helps the model achieve an F1 score of 0.84 whereas the model achieves an F1 score of 0.60 without it. Further, we analyse the importance of phase and intensity individually towards tissue classification.

翻译：针头定位对于硬膜外麻醉等多种医疗应用至关重要。医生在硬膜外腔中导航针头时依赖自身直觉。在此过程中，识别组织结构有助于为医生提供针头穿刺过程中的额外反馈。为此，我们提出一种深度神经网络，利用针尖处采集的复杂OCT信号的相位和强度数据对组织进行分类。我们在有限标注数据集场景下研究了该深度神经网络的性能，并提出一种新颖的对比预训练策略，学习相位和强度数据的不变表征。研究表明，仅使用训练集的10%时，我们提出的预训练策略帮助模型达到了0.84的F1分数，而未使用该策略时模型仅达到0.60的F1分数。此外，我们分别分析了相位和强度对组织分类的重要性。