Artificial intelligence (AI) is the next frontier for imaging applications. DRVISION is at the forefront of AI-enabled technology for next-generation image analysis with Aivia, the first commercial image analysis software with a fully integrated end-to-end pipeline for deep learning.
Aivia provides a turnkey solution for applying pre-trained deep learning models for diverse imaging applications from restoration to segmentation and prediction. By providing some training images with ground truths (e.g. annotations), you can train a new model or update an existing one optimized for your application. Try Aivia today and experience the future of image analysis.
In Aivia you can use deep learning models for three types of application: restoration, segmentation and prediction. You can apply a pre-trained model in Aivia or augment one of the models with your own data using Aivia Cloud.
Segmentation of 3D electron microscopy (3DEM) datasets can be a tedious task for many researchers. Typically, scientists must go through hundreds of image slices and manually draw the boundary for each cell, which could take days to accomplish.
In Aivia, we have developed several pre-trained deep learning models based on different convolutional neural network architectures (DenseNet, UNet, 3D-UNet) to tackle EM image segmentation.
The deep learning model can applied to EM datasets like an image processing step. The model transforms the input image into a probability map of cell regions that can be easily segmented by threshold, enabling complete reconstruction of the image stack in minutes.
Image prediction takes a pair of images, such as phase contrast image and a corresponding fluorescent nuclei image, and creates a model for predicting the localization of the paired features. The model uses a UNet architecture to predict the localization of subcellular features (e.g. nuclei) from brightfield images. Aivia can create new image channel(s) for a desired feature that is virtually indistinguishable from fluorescence imaging of the feature.
The prediction model can be invaluable at prototyping imaging experiments - without using precious reagent. Additionally, the model works well in label-sparse or label-free environment, enabling you to amplify the data quantity to collect.
Apply one of the standard image analysis recipes in Aivia to the prediction output to segment and characterize the predicted objects. With prediction, you can extend your imaging experiment and analysis further than ever before.
Long-term imaging of live samples can be challenging for microscopists needing to balance limiting phototoxicity from light exposure without compromising image signal-to-noise ratio or spatial resolution. With deep learning image restoration, it is possible to obtain high-quality data while limiting the sample to low light exposure.
Using a Residual Channel Attention Network (RCAN), Aivia can enhance images acquired with extremely low light exposure to be indistinguishable from images acquired with optimal imaging conditions (i.e. much higher laser power and lower frame rate). Additionally, restoration with deep learning can retain (and recover) the spatial resolution of fine subcellular features (e.g. mitochondria, filaments).