CARE, Noise2Void


Deep learning based image restoration methods have recently been made available to restore images from under-exposed imaging conditions, increase spatio-temporal resolution (CARE) or self-supervised image denoising (Noise2Void). These powerful methods outperform conventional state-of-the-art methods and leverage down-stream analyses significantly such as segmentation and quantification.

To bring these new tools to a broader platform in the image analysis community, we developed a simple Jupyter based graphical user interface for CARE and Noise2Void, which lowers the burden for non-programmers and biologists to access these powerful methods in their daily routine.  CARE-less supports temporal, multi-channel image and volumetric data and many file formats by using the bioformats library. The user is guided through the different computation steps via inline documentation. For standard use cases, the graphical user interface exposes the most relevant parameters such as patch size and number of training iterations, while expert users still have access to advanced parameters such as U-net depth and kernel sizes. In addition, CARE-less provides visual outputs for training convergence and restoration quality. Any project settings can be stored and reused from command line for processing on compute clusters. The generated output files preserve important meta-data such as pixel sizes, axial spacing and time intervals.

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ZEN and APEER – Open Ecosystem for integrated Machine-Learning Workflows

Open ecosystem for integrated machine-learning workflows to train and use machine-learning models for image processing and image analysis inside the ZEN software or on the APEER cloud-based platform

Highlights ZEN

  • Simple User Interface for Labeling and Training
  • Engineered Features Sets and Deep Feature Extraction + Random Forrest for Semantic Segmentation
  • Object Classification workflows
  • Probability Thresholds and Conditional Random Fields
  • Import your own trained models as *.czann files (see: czmodel · PyPI)
  • Import "AIModel Containes" from arivis AI for advanced Instance Segmentation
  • Integration into ZEN Measurement Framework
  • Support for Multi-dimensional Datasets and Tile Images
  • open and standardized format to store trained models
ZEN Intellesis Segmentation

ZEN Intellesis Segmentation - Training UI

ZEN Intellesis - Pretrained Networks

ZEN Intellesis Segmentation - Use Deep Neural Networks

Intellesis Object Classification

ZEN Object Classification

Highlights Aarivis AI

  • Web-based tool to label datasets to train Deep Neural Networks
  • Fully automated hyper-parameter tuning
  • Export of trained models for semantic segmentation and AIModelContainer for Instance Segmentation
Annotation Tool

APEER Annotation Tool