用于组织识别、癌症诊断和癌症边缘检测的人工智能增强型无标记分子成像方法。
Artificial intelligence-augmented, label-free molecular imaging method for tissue identification, cancer diagnosis, and cancer margin detection.
作者信息
Li Jiasong, Liu Jun, Wang Ye, He Yunjie, Liu Kai, Raghunathan Raksha, Shen Steven S, He Tiancheng, Yu Xiaohui, Danforth Rebecca, Zheng Feibi, Zhao Hong, Wong Stephen T C
机构信息
Department of Systems Medicine and Bioengineering, Houston Methodist Cancer Center, Weill Cornell Medicine, Houston, TX 77030, USA.
These authors contributed equally to this work.
出版信息
Biomed Opt Express. 2021 Aug 13;12(9):5559-5582. doi: 10.1364/BOE.428738. eCollection 2021 Sep 1.
Label-free high-resolution molecular and cellular imaging strategies for intraoperative use are much needed, but not yet available. To fill this void, we developed an artificial intelligence-augmented molecular vibrational imaging method that integrates label-free and subcellular-resolution coherent anti-stokes Raman scattering (CARS) imaging with real-time quantitative image analysis via deep learning (artificial intelligence-augmented CARS or iCARS). The aim of this study was to evaluate the capability of the iCARS system to identify and differentiate the parathyroid gland and recurrent laryngeal nerve (RLN) from surrounding tissues and detect cancer margins. This goal was successfully met.
目前非常需要用于术中的无标记高分辨率分子和细胞成像策略，但尚未有可用的。为了填补这一空白，我们开发了一种人工智能增强的分子振动成像方法，该方法将无标记和亚细胞分辨率的相干反斯托克斯拉曼散射（CARS）成像与通过深度学习进行的实时定量图像分析（人工智能增强的CARS或iCARS）相结合。本研究的目的是评估iCARS系统从周围组织中识别和区分甲状旁腺和喉返神经（RLN）以及检测癌边缘的能力。这一目标已成功实现。
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