Artificial intelligence (AI) is the next frontier for imaging applications. Aivia is at the forefront of AI-enabled technology for next-generation image analysis. We are 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.
Model applications
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.
Image segmentation
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
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.
Image restoration
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).
Using deep learning, you can perform long-term live cell imaging with significantly lower light exposure thus dramatically reducing photo-toxicity.
Accelerated training
Even with the most advanced GPU on the market, training a deep learning model can take many hours (or even days) per run. With Aivia Cloud, you can harness the power of cloud computing to accelerate the training process and obtain results in minutes.
Aivia Cloud gives you access to the state-of-the-art hardware offered by Google Cloud Platform for training deep learning models. Each training run can be parallelized across up to eight (8) NVIDIA V100 GPUs using Aivia Cloud. With Aivia Cloud, you can generate deep learning models quickly without the costly investment of purchasing and maintaining cutting-edge hardware.
Additionally, Aivia Cloud provides cloud-based storage for your data and remote access to powerful hardware running Aivia from anywhere - a complete analysis and visualization solution.
Processing
Storage
Remote access
Aivia Cloud
Resources
Aivia comes with several pre-trained deep learning models that integrates the most cutting edge neural network architectures available. Here are some of the solutions we have implemented in Aivia (with references):
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3D UNet [read the paper on ArXiv.org]
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DenseNet [read the paper on ArXiv.org]
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RCAN [read the paper on ArXiv.org] [learn about our recent work on BiorXiv.org]