Youn Sangyeon, Lee Kyungsu, Son Jeehoon, Yang In-Hwan, Hwang Jae Youn
Daegu Gyeongbuk Institute of Science and Technology,Department of Information and Communication Engineering, 333 Techno Jungang-daero, Hyeonpung-myun, Dalseong-gun, Daegu, 42988, South Korea.
S. Youn and K. Lee are equally contributed to this study.
Biomed Opt Express. 2020 May 11;11(6):2976-2995. doi: 10.1364/BOE.390558. eCollection 2020 Jun 1.
A single-beam acoustic trapping technique has been shown to be very useful for determining the invasiveness of suspended breast cancer cells in an acoustic trap with a manual calcium analysis method. However, for the rapid translation of the technology into the clinic, the development of an efficient/accurate analytical method is needed. We, therefore, develop a fully-automatic deep learning-based calcium image analysis algorithm for determining the invasiveness of suspended breast cancer cells using a single-beam acoustic trapping system. The algorithm allows to segment cells, find trapped cells, and quantify their calcium changes over time. For better segmentation of calcium fluorescent cells even with vague boundaries, a novel deep learning architecture with multi-scale/multi-channel convolution operations (MM-Net) is devised and constructed by a target inversion training method. The MM-Net outperforms other deep learning models in the cell segmentation. Also, a detection/quantification algorithm is developed and implemented to automatically determine the invasiveness of a trapped cell. For the evaluation of the algorithm, it is applied to quantify the invasiveness of breast cancer cells. The results show that the algorithm offers similar performance to the manual calcium analysis method for determining the invasiveness of cancer cells, suggesting that it may serve as a novel tool to automatically determine the invasiveness of cancer cells with high-efficiency.
单束声阱捕获技术已被证明在采用手动钙分析方法的声阱中确定悬浮乳腺癌细胞的侵袭性方面非常有用。然而，为了将该技术快速转化应用于临床，需要开发一种高效/准确的分析方法。因此，我们开发了一种基于深度学习的全自动钙图像分析算法，用于使用单束声阱捕获系统确定悬浮乳腺癌细胞的侵袭性。该算法能够对细胞进行分割、找到捕获的细胞，并量化其随时间的钙变化。为了即使在边界模糊的情况下也能更好地分割钙荧光细胞，通过目标反演训练方法设计并构建了一种具有多尺度/多通道卷积操作的新型深度学习架构（MM-Net）。MM-Net在细胞分割方面优于其他深度学习模型。此外，还开发并实施了一种检测/量化算法，以自动确定捕获细胞的侵袭性。为了评估该算法，将其应用于量化乳腺癌细胞的侵袭性。结果表明，该算法在确定癌细胞侵袭性方面与手动钙分析方法具有相似的性能，这表明它可能成为一种高效自动确定癌细胞侵袭性的新型工具。
