AI-Driven Microscopy Image Analysis in Pharma and Biotech

Artificial Intelligence (AI) enables computers to mimic human intelligence. AI can learn to identify objects in images without being explicitly programmed, similarly to how we as human beings learn.

One form of AI is Machine Learning (ML), which allows machines to learn by extracting feature patterns from data. ML can assess features beyond just pixel intensity, e.g., textural information, which yields more robust results from data sets that include highly variable images.

Deep Learning (DL) is a specialized form of ML which learns from interpreting vast amounts of data with millions of parameters. While it does require more data to feed the algorithm, DL can mimic how multiple layers of networked neurons process data, enabling it to successfully process very complex and difficult to segment data sets.

We train AI by using a representative subset of images containing features of interest, e.g., cell nuclei. Instead of defining a sequence of processing steps to achieve a desired segmentation outcome (as in conventional image analysis), the desired outcome is reinforced in training and the AI automatically optimizes the algorithm to achieve this very segmentation. AI-powered analysis pipelines yield outstanding results, even when applied to particularly challenging imaging scenarios, like low-contrast imagery (e.g., label-free) and images with high densities of objects (e.g., confluent cell culture or tissues). Furthermore, AI pipelines are readily automated and applied to large data sets to extract robust, statistically relevant insights about biological phenomena.