Dataset Information

Millisecond, micron precision multi-whisker detector.

ABSTRACT: The neural mechanisms of somatosensory information processing in the rodent vibrissae system are a topic of intense debate and research. Certain hypotheses emphasize the importance of stick-slip whisker motion, high-frequency resonant vibrations, and/or the ability to decode complex textures. Other hypotheses focus on the importance of integrating information from multiple whiskers. Tests of the former require measurements of whisker motion that achieve high spatiotemporal accuracy without altering the mechanical properties of whiskers. Tests of the latter require the ability to monitor the motion of multiple whiskers simultaneously. Here we present a device that achieves both these requirements for two-dimensional whisker motion in the plane perpendicular to the whiskers. Moreover, the system we present is significantly less expensive (<$2.5 k) and simpler to build than alternative devices which achieve similar detection capabilities. Our system is based on two laser diodes and two linear cameras. It attains millisecond temporal precision and micron spatial resolution. We developed automated algorithms for processing the data collected by our device and benchmarked their performance against manual detection by human visual inspection. By this measure, our detection was successful with less than 10 µm deviation between the automated and manual detection, on average. Here, we demonstrate its utility in anesthetized rats by measuring the motion of multiple whiskers in response to an air puff.

SUBMITTER: Grady SK

PROVIDER: S-EPMC3759455 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Millisecond, micron precision multi-whisker detector.

Grady Stephen K SK Hoang Thanh T TT Gautam Shree Hari SH Shew Woodrow L WL

PloS one 20130902 9

The neural mechanisms of somatosensory information processing in the rodent vibrissae system are a topic of intense debate and research. Certain hypotheses emphasize the importance of stick-slip whisker motion, high-frequency resonant vibrations, and/or the ability to decode complex textures. Other hypotheses focus on the importance of integrating information from multiple whiskers. Tests of the former require measurements of whisker motion that achieve high spatiotemporal accuracy without alter ...[more]

PMID: 24023861

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Project description:Encapsulation of cells inside microfluidic droplets is central to several applications involving cellular analysis. Although, theoretically the encapsulation statistics are expected to follow a Poisson distribution, experimentally this may not be achieved due to lack of full control of the experimental variables and conditions. Therefore, there is a need for automatic detection of droplets and cell count enumeration within droplets so a process control feedback to adjust experimental conditions can be implemented. In this study, we use a deep learning object detector called You Only Look Once (YOLO), an influential class of object detectors with several benefits over traditional methods. This paper investigates the application of both YOLOv3 and YOLOv5 object detectors in the development of an automated droplet and cell detector. Experimental data was obtained from a microfluidic flow focusing device with a dispersed phase of cancer cells. The microfluidic device contained an expansion chamber downstream of the droplet generator, allowing for visualization and recording of cell-encapsulated droplet images. In the procedure, a droplet bounding box is predicted, then cropped from the original image for the individual cells to be detected through a separate model for further examination. The system includes a production set for additional performance analysis with Poisson statistics while providing an experimental workflow with both droplet and cell models. The training set is collected and preprocessed before labeling and applying image augmentations, allowing for a generalizable object detector. Precision and recall were utilized as a validation and test set metric, resulting in a high mean average precision (mAP) metric for an accurate droplet detector. To examine model limitations, the predictions were compared to ground truth labels, illustrating that the YOLO predictions closely matched with the droplet and cell labels. Furthermore, it is demonstrated that droplet enumeration from the YOLOv5 model is consistent with hand counted ratios and the Poisson distribution, confirming that the platform can be used in real-time experiments for cell encapsulation optimization.

Dataset Information

Millisecond, micron precision multi-whisker detector.

Publications

Millisecond, micron precision multi-whisker detector.

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