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Analyzes, processes and visualizes multi-dimensional microscopy images. BioImageXD puts open-source computer science tools for three-dimensional visualization and analysis into the hands of all researchers, through a user-friendly graphical interface tuned to the needs of biologists. BioImageXD has no restrictive licenses or undisclosed algorithms and enables publication of precise, reproducible and modifiable workflows. It allows simple construction of processing pipelines and should enable biologists to perform challenging analyses of complex processes.
GALA / Graph-based Active Learning of Agglomeration
Provides state of the art automatic segmentation accuracy for image segmentation. GALA utilises machine learning to obtain a merge priority function or policy, which dictates which pair of segments to merge next. It is able to mask volumes so that partial ground truth can be used. The tool outperforms previous agglomeration methods for automatic segmentation of an isotropic focused ion beam scanning electron microscope (FIBSEM) dataset of Drosophila larva neuropil.
Represents a deep learning method for segmenting 3D anisotropic brain electron microscopy images. DeepEM3D can efficiently build feature representation and incorporate sufficient multi-scale contextual information. DeepEM3D is able to produce highly accurate 3D neurite boundary probability maps, thereby requiring only a simple watershed method to do segmentation. This tool uses the power of inception and residual structures in the bottom-up path to integrate image information, and combines skip connection techniques with pyramid multi-scale contexture aggregation in the top-down path.
A deep neural network model architecture that is highly optimized for serial-section transmitted electron microscopy image segmentation. We trained a pixel classifier that operates on raw pixel intensities with no preprocessing to generate probability values for each pixel being a membrane or not. While the use of neural networks in image segmentation is not completely new, we developed novel insights and model architectures that allow us to achieve superior performance on EM image segmentation tasks. Our submission based on these insights to the 2D EM Image Segmentation Challenge achieved the best performance consistently across all the three evaluation metrics.
A simple, user-friendly tool for interactive image classification, segmentation and analysis. It is built as a modular software framework, which currently has workflows for automated (supervised) pixel- and object-level classification, automated and semi-automated object tracking, semi-automated segmentation and object counting without detection. Most analysis operations are performed lazily, which enables targeted interactive processing of data subvolumes, followed by complete volume analysis in offline batch mode.
VAST / Volume Annotation and Segmentation Tool
Allows manual annotation and segmentation of large volumetric (voxel) data sets. VAST is a software which enables to work with voxel data sets in the Terabyte or even Petabyte range at interactive speeds, to explore them visually and to label structures of interest by voxel painting. It can be used to generate volumetric training data sets, and can to some extent also be used for importing, proof-reading and correcting results of segmentation algorithms.
Amira 3D Software for Life Sciences
Allows users to visualize, manipulate, and understand data from imaging modalities such as computed tomography, microscopy or Magnetic resonance imaging (MRI). Amira 3D Software for Life Sciences provides features to import and process 2D and 3D images data, visualization techniques and tools for visual analysis. Users can also create and share presentations. The base product can be customized by adding functional extensions to fit special needs in different application areas.
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