The distinction between malignant and benign tumors is essential to the treatment of cancer. The tissue's elasticity can be used as an indicator for the required tissue characterization. Optical coherence elastography (OCE) probes have been proposed for needle insertions but have so far lacked the necessary load sensing capabilities. We present a novel OCE needle probe that provides simultaneous optical coherence tomography (OCT) imaging and load sensing at the needle tip. We demonstrate the application of the needle probe in indentation experiments on gelatin phantoms with varying gelatin concentrations. We further implement two deep learning methods for the end-to-end sample characterization from the acquired OCT data. We report the estimation of gelatin sample concentrations in unseen samples with a mean error of $1.21 \pm 0.91$ wt\%. Both evaluated deep learning models successfully provide sample characterization with different advantages regarding the accuracy and inference time.

翻译：恶性肿瘤和良性肿瘤的区别对于治疗癌症至关重要。组织弹性可以用作所需组织特征的一个指标。为针插入提议了光一致性活性学探针,但迄今缺乏必要的负荷感测能力。我们提出了一个新型的 OCE 针探针,在针尖同时提供光学一致性透析成像和负载感测。我们展示了针针探在对凝素浓度不同的凝素幻影的缩进实验中的应用。我们从获得的 OCT 数据中进一步采用两种深层次的学习方法对端到端样本的特征定性。我们报告了对未见样品中凝素样本浓度的估计,平均误差为 1.21\ pm 0.91 wt ⁇ 。两种深层学习模型都成功地提供了对精度和推断时间的不同好处的样本特征鉴定。