Image Segmentation
Fast, intuitive cloud-based segmentation for 2D pharmaceutical images; built for integration with analysis and simulation workflows.
What is Segmentation
Image segmentation plays a key role in image processing enabling users to identify different materials or objects within their microscopy images. Identification is a necessary step in order to begin to quantify any numerical properties of those materials. This labeling process can often be complex and challenging, I2S supports the most state of the art segmentation techniques directly in a web browser interface, ensuring images of any size or complexity can be segmented.
Machine Learning
Machine learning segmentation uses image specific supervised training to rapidly idetntify the complex patterns found within pharmaceutical imaging applications. Whether working with high-noise SEM images or low-contrast micro-CT images, machine learning offers an adaptable framework to segment your imagery. Our supervised training approach ensures accuracy by providing expert-like intervention at each step of the segmentation workflow to ensure model refinement is accurate and efficient.
Deep Learning
State of the art deep learning segmentation enables I2S users to segment even the most complex images. Whether handling a few small images, or optimizing high-throughput workflows deep learning ensures the highest accuracy segmentation can be obtained with lightning fast speeds. The I2S data management framework supports the management of training datasets and DL model generation all in one-place, allowing quick and easy model development, testing, and usage.
Analysis Ready Segmentation
ML and DL segmentations can be further refined to meet the requirements of downstream quantifications and simulations. Users can clean up segmentations, relabel particles, seperate touching structures, fill voids, and more using the extensive set of editing tools. This ensures segmentations are not just visually accurate but computationally consistent for regulatory-ready analyses and simulations.
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