fbpx
 
IMG 6322

26 Mar From Pixels to Patients: Machine Vision in Medical Products

Posted at 14:53h in featured, Machine Vision, Robotics by

Machine vision is at the core of many Key Tech systems, enabling critical functions such as isolating single cells, guiding macro level motion with articulated robots, and segmenting objects from consumables to organisms. In other cases, machine vision plays a supporting role by confirming proper user interactions, handling errors, and assisting upstream with assembly, formulation, and quality control (QC).

Just like human eyes rely on the brain to interpret visual data, a camera’s value is dependent on properly paired algorithms behind it. These algorithms form a model, which acts as the vision system’s “brain” by processing and analyzing input images to extract meaningful information or make decisions. After over a decade of applying machine vision to solve complex product development challenges, Key Tech’s engineers are well-versed in selecting the right tools and techniques to build safe, reliable, and effective vision systems for medical devices.

Traditional vs Deep Learning-Based Machine Vision

A key technical decision when developing a vision system is choosing between a traditional and deep learning-based machine vision model.

  • Traditional vision uses manually tuned, rule-based algorithms to analyze predefined image features, such as edges, shapes, and colors.
  • Deep learning-based vision leverages neural networks to autonomously learn and extract image features from provided input data.

In some instances, the best approach is a hybrid—blending traditional techniques with deep learning to optimize performance and reliability. Choosing the right single or combination method early can significantly reduce development time, ensure consistent behavior, and prevent costly rework.

Common Pitfalls to Avoid

When choosing the right tool for the job, whether that be a traditional, deep learning, or hybrid approach, below are four common pitfalls to avoid from the get-go!

  1. Misjudging the Complexity of the Problem

Applying deep learning for constrained, structured tasks or relying on traditional vision for highly variable or complex features can create mismatches that lead to headaches and inefficiencies. For example, verifying reagent fill levels can be effectively handled with classic rule-based techniques, while identifying biologic anomalies in microfluidics may demand deep learning. Target variability and structure, known features, and the defect range, and much more, all contribute towards defining problem complexity. In the spirit of Goldilocks, ensure your solution is neither too simple nor too complex for the problem but rather, just right.

  1. Underestimating Data Requirements

Machine vision thrives on plentiful, diverse data, but determining “enough” data is task-specific and dictated by variability of the target, desired performance, noise, and model complexity, among other factors. Especially for deep learning approaches, skimping on training data or overlooking diversity can lead to models that underperform and act unpredictably in real-world scenarios. All hope is not lost, however, as a structured, systematic approach that includes starting small and gradually increasing training data, augmenting available data, and tracking progress metrics can help mitigate these risks.

  1. Overlooking Computational Needs

Traditional machine vision with millisecond-level decision making models can often run on select microcontrollers (MCUs), making them lightweight and efficient. Deep learning, however, introduces higher computational demands, frequently requiring dedicated GPUs or specialized accelerators. And even when latency bottlenecks and frame drops can be avoided by more powerful hardware, the resulting and significant thermal impacts and power demands can be unachievable in some medical device applications, highlighting the need for early definition of system hardware constraints.

  1. Failing to Capture Edge Cases

A near certainty in real-world vision applications, to various degrees of significance, is unpredictability. Novel edge cases are inevitable, environments will vary, and a model not prepared for these distant bounds can cripple system performance. Adjustments in lighting, background noise, and component orientation are common culprits of failures in underdeveloped models. Between the two vision model approaches, traditional methods excel under fewer edge cases and in controlled environments, such as inside of a closed diagnostic device, while deep learning is better suited for diverse, variable conditions—provided the training data adequately reflects or at least forecasts those conditions.

Machine Vision in Action

From guiding robotic endoscopic tools during surgery to identifying high-throughput screening (HTS) samples and managing QC for reagent production, machine vision plays a pivotal role in modern medical products. Each application blends technical prowess with creative problem-solving to develop a solution that meets stringent safety and performance standards. Avoiding the above-mentioned four common pitfalls and implementing the right vision model form just one step towards developing a reliable and high-performing system.

Get in Touch!

At Key Tech, we excel in de-risking and developing turnkey machine vision solutions tailored to the unique demands of your medical application. Whether you’re exploring where machine vision can enhance your processes or have well-defined requirements, we’re here to help. Machine vision doesn’t check our inbox, but our experienced team certainly does—reach out to learn how we can support your next project!

Ryder Babik
Latest posts by Ryder Babik (see all)


Every challenge is different – Tell us about yours.