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.

提出了一种基于深度神经网络的方法，通过对针尖处复杂 OCT 信号的相位和强度数据进行分类，辅助医生在针的插入过程中对组织结构进行识别，并且使用对比预训练技术将同位的数据映射到不同的表示，从而在受限标记数据集的情况下提高分类的精度。在仅使用 10% 的训练集时，使用该技术可以将模型的 F1 分数从 0.60 提高到 0.84。