AI-Driven Microscopy Image Analysis in Pharma and Biotech
Extract Breakthrough Discoveries from Large, Complex Data Sets
The explosion in biomedical research is providing opportunities for pharma and biotech industries to develop new drugs, applications, and technologies. Innovation requires complex experimental designs which result in unmanageable, multi-dimensional data sets. Without automated analysis, companies face difficulties moving from data collection to groundbreaking discoveries. ZEISS AI-based image analysis addresses three main challenges to aid innovation:
The Limitations of Classical Image Analysis
Drug discovery and biotech researchers have long been pursuing methods for automating image analysis. The common approach has been to apply a sequence of processing functions (like smoothing, edge detection etc.) followed by threshold-based segmentation. Such sequences can be configured to set up basic analysis pipelines for typical tasks like identifying and counting nuclei, cells, and other sub-cellular features of interest. Despite its power for many standard image analysis tasks, this classical threshold-based image segmentation has limitations which can cause significant setbacks for large-scale analyses of complicated data sets.
These algorithms only consider a small subset of image parameters, most notably brightness. Images must be highly consistent in terms of target signal intensity and homogeneous background. These pipelines will often fail with heterogenous sets of images, leading to artifacts or incomplete analysis. Building and testing the pipelines requires experience and proficiency in analysis algorithms. Such reliance on a specific skillset can lead to inconsistent results when there is a heterogeneous level of expertise among the scientists performing image analysis, or when there is a lack of the necessary expertise altogether.
Harness Artificial Intelligence to Advance Automated Image Analysis
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.
AI-Powered Pipelines Yield Outstanding Results in Challenging Scenarios
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.
ZEISS Image Analysis Ecosystem Powered by AI
AI-based image analysis workflows combine accurate image segmentation and classification with automation for reproducible, high throughput data analyses. Applying AI analysis workflows, biopharma research teams can boost their success in identifying and developing better drug candidates from early research and discovery to pre-clinical, IND-enabling studies and beyond.
The benefit is twofold:
- Avoid high attrition costs associated with failing late in development.
- Gain a competitive advantage by achieving faster time to market.
ZEISS software solutions empower users of all experience levels to harness AI and boost productivity:
- The cloud-based arivis Cloud learning platform enables easy and efficient labeling of training data sets for development of deep learning models without requiring users to have any coding skills.
- Deep learning models can be put to action in ZEISS ZEN software as part of an image analysis setting or a streamlined Bio Apps workflow.
- If your analysis task is highly complex and/or data sizes are very big, your analysis pipeline will benefit from deployment in arivis Pro – a high-efficiency, image analysis suite for challenging, multidimensional data sets. Finally, plug your AI-powered analysis pipelines into the arivis ProHub server-based analysis platform and augment the arivis Pro analysis technology with added throughput and scale.
The images shown on this page represent research content. ZEISS explicitly excludes the possibility of making a diagnosis or recommending treatment for possibly affected patients on the basis of the information generated with an Axioscan 7 slide scanner.