What do you get if you strap a microscope onto a CNC and throw in a gaming controller? The answer, according to Reddit user [AskewedBox] is something kind of awesome: you get a microscope that can be controlled with the game controller for easier tracking of tiny creepy-crawlies.
[ASkewedBox] set up this interesting combination of devices, attaching their Adonostar AD246S microscope to the stage of a no-brand 1610 CNC bought off Amazon, then connected the CNC to a computer running Universal G-Code Sender. This great open source program takes the input from an Xbox game controller and uses it to jog the CNC.
With a bit of tweaking, the game controller can now move the microscope, so it can be used to track microbes and other small creatures as they wander around on the slide mounted below the microscope eating each other. The movement of this is surprisingly smooth: the small CNC and a well-mounted microscope means that there seems to be very little wobble or backlash as the microscope moves.
[Askewedbox] hasn’t finished yet, though: in the latest update, he adds a polarizing lens to the setup and mentions that he wants to add focus control to the system, which is controlled by a remote that comes with the microscope.
There are plenty of other things that could be added beyond that, though, such as auto pan and stitch for larger photos, auto focus stacking and perhaps even auto tracking using OpenCV to track the hideous tiny creatures that live in the microscopic realm. What would you do to make this even cooler?
Ziehl-Neelsen Sputum Smear Microscopy (ZN) is one of most common methods for diagnosing Tuberculosis. On the equipment side, it requires not much more than an optical microscope, although it still needs a trained professional to look through the glass, identify and count the number of bacteria in a sample. To provide reliable and effective Tuberculosis diagnostic to regions, where both equipment and trained personnel is in short supply, [Rodrigo Loza] and [khalilnallar] are developing an automated digital microscope based on computer vision and machine learning, their entry for the Hackaday Prize.
They started out gathering images of Tuberculosis bacteria from the internet and experimented with color threshold algorithms to detect dyed bacteria, as well as algorithms for counting individual and clusters of bacteria. This process alone can, according to the team, take a trained professional 30 minutes or more. A graphical interface highlights identified bacteria and reads the bacteria count.
[Rodrigo Loza] and [khalilnallar] are testing their device at the Dr. Roberto Galindo Teran hospital in Cobija, Bolivia. However, getting access to a lab environment is one thing, and being given access to a steady supply of fresh M. Tuberculosis samples is another. Unable to obtain samples, which they need to test their algorithms on live subjects, they turned to another front of their project: The hardware. In several iterations, they developed a low-cost, 3D-printable kit, which transforms a laboratory-grade optical microscope into an embedded CNC-controlled microscopy platform. Their kit comprises three stepper-motor-based axis for the X, Y and Z direction, as well as a webcam mount. An Intel Edison and a custom, Arduino compatible shield control the system to achieve features such as homing procedures, autofocus and bacteria detection.
The team is currently in the process of refining their bacteria detection pipeline, exploring the feasibility of semi-automated detection methods, machine learning and neural networks for classification of bacteria within the hardware constraints. The video below shows their latest update on the Z-axis of their microscope.
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