PDF(Pubmed)
Abstract：
Accelerating the measurement for discrimination of samples, such as classification of cell phenotype, is crucial when faced with significant time and cost constraints. Spontaneous Raman microscopy offers label-free, rich chemical information but suffers from long acquisition time due to extremely small scattering cross-sections. One possible approach to accelerate the measurement is by measuring necessary parts with a suitable number of illumination points. However, how to design these points during measurement remains a challenge. To address this, we developed an imaging technique based on a reinforcement learning in machine learning (ML). This ML approach adaptively feeds back \"optimal\" illumination pattern during the measurement to detect the existence of specific characteristics of interest, allowing faster measurements while guaranteeing discrimination accuracy. Using a set of Raman images of human follicular thyroid and follicular thyroid carcinoma cells, we showed that our technique requires 3,333 to 31,683 times smaller number of illuminations for discriminating the phenotypes than raster scanning. To quantitatively evaluate the number of illuminations depending on the requisite discrimination accuracy, we prepared a set of polymer bead mixture samples to model anomalous and normal tissues. We then applied a home-built programmable-illumination microscope equipped with our algorithm, and confirmed that the system can discriminate the sample conditions with 104 to 4,350 times smaller number of illuminations compared to standard point illumination Raman microscopy. The proposed algorithm can be applied to other types of microscopy that can control measurement condition on the fly, offering an approach for the acceleration of accurate measurements in various applications including medical diagnosis.
摘要：
加速样品辨别的测量，如细胞表型的分类，当面临巨大的时间和成本限制时，这是至关重要的。自发拉曼显微镜提供无标签，丰富的化学信息，但由于散射截面极小，采集时间长。加速测量的一种可能的方法是通过用合适数量的照明点测量必要的部分。然而,如何在测量过程中设计这些点仍然是一个挑战。为了解决这个问题，我们开发了一种基于机器学习（ML）强化学习的成像技术。这种ML方法在测量期间自适应地反馈“最佳”照明模式，以检测感兴趣的特定特征的存在，允许更快的测量，同时保证鉴别的准确性。使用一组人类滤泡性甲状腺和滤泡性甲状腺癌细胞的拉曼图像，我们表明，我们的技术需要3,333至31,683倍的照明数量来区分表型比光栅扫描。根据必要的辨别精度定量评估照明的数量，我们制备了一组聚合物珠混合物样品来模拟异常和正常组织。然后，我们应用了配备我们算法的家用可编程照明显微镜，并证实该系统可以区分样品条件，与标准点照明拉曼显微镜相比，照明次数少104到4,350倍。所提出的算法可以应用于其他类型的显微镜，可以在飞行中控制测量条件，提供了一种方法，用于加速包括医疗诊断在内的各种应用中的精确测量。
