Ascertaining the collective viability of cells in different cell culture
conditions has typically relied on averaging colorimetric indicators and is
often reported out in simple binary readouts. Recent research has combined
viability assessment techniques with image-based deep-learning models to
automate the characterization of cellular properties. However, further
development of viability measurements to assess the continuity of possible
cellular states and responses to perturbation across cell culture conditions is
needed. In this work, we demonstrate an image processing algorithm for
quantifying cellular viability in 3D cultures without the need for assay-based
indicators. We show that our algorithm performs similarly to a pair of human
experts in whole-well images over a range of days and culture matrix
compositions. To demonstrate potential utility, we perform a longitudinal study
investigating the impact of a known therapeutic on pancreatic cancer spheroids.
Using images taken with a high content imaging system, the algorithm
successfully tracks viability at the individual spheroid and whole-well level.
The method we propose reduces analysis time by 97% in comparison to the
experts. Because the method is independent of the microscope or imaging system
used, this approach lays the foundation for accelerating progress in and for
improving the robustness and reproducibility of 3D culture analysis across
biological and clinical research.

提出了一种用于 3D 培养的细胞存活性的图像处理算法，具有消除基于试剂的指标的需求，通过使用高内容成像系统，能够在个体瘤球和整体培养基水平上成功跟踪细胞存活性，分析时间较专家减少了 97％。这种方法不受显微镜或成像系统的限制，为推动生物和临床研究中 3D 培养分析的进展提供了基础，同时提高了分析的鲁棒性和可复现性。

使用机器学习图像分割进行三维组织培养的无损定量生存力分析

Nondestructive, quantitative viability analysis of 3D tissue cultures  using machine learning image segmentation

Pick-and-place robots are commonly used in modern industrial manufacturing.
For complex devices/parts like camera modules used in smartphones, which
contain optical parts, electrical components and interfacing connectors, the
placement operation may not absolutely accurate, which may cause damage in the
device under test during the mechanical movement to make good contact for
electrical functions inspection. In this paper, we proposed an effective vision
system including hardware and algorithm to enhance the reliability of the
pick-and-place robot for autonomous testing memory of camera modules. With
limited hardware based on camera and raspberry PI and using simplify image
processing algorithm based on histogram information, the vision system can
confirm the presence of the camera modules in feeding tray and the placement
accuracy of the camera module in test socket. Through that, the system can work
with more flexibility and avoid damaging the device under test. The system was
experimentally quantified through testing approximately 2000 camera modules in
a stable light condition. Experimental results demonstrate that the system
achieves accuracy of more than 99.92%. With its simplicity and effectiveness,
the proposed vision system can be considered as a useful solution for using in
pick-and-place systems in industry.

利用基于摄像头和树莓派的硬件以及直方图信息的简化图像处理算法，提出了一种能够增强取放式机器人自主测试摄像头模块记忆体的可靠性的视觉系统，能够在摄像头模块进料托盘中确认其存在并确定在测试插座中的放置准确性。实验结果表明，该系统的准确度超过了 99.92％，并具有简单而有效的特点，可以被认为是一个应用广泛的解决方案。