TileDB-BioImaging

Image storage library

A Python package for efficiently storing and querying biomedical imaging data in TileDB arrays.

Package providing bioimaging functionality using TileDB. Source of the tiledb-bioimg Python package.

GitHub

16 stars
8 watching
1 forks
Language: Python
last commit: 16 days ago
bioimagingpython-packagetiledb

Related projects:

Repository Description Stars
tiledb-inc/tiledb-py Provides a Python interface to store and manage large datasets in a distributed, columnar storage system. 190
tiledb-inc/tiledb-java A Java interface to a distributed columnar storage engine 26
tiledb-inc/tiledb-vcf Efficient storage and retrieval of genomic variant-call data using TileDB Embedded 89
tiledb-inc/tiledb A powerful storage engine for dense and sparse multi-dimensional arrays, providing efficient data modeling and access. 1,866
tiledb-inc/tiledb-vector-search A serverless, scale-out vector similarity search and storage system with efficient database capabilities 53
imageomics/bioclip A deep learning framework trained on a large biological image dataset to learn taxonomic labels and classify images. 164
tiledb-inc/tiledb-csharp C# bindings for TileDB Embedded storage engine 15
single-cell-data/tiledb-soma Provides access to single-cell data at scale through a flexible, extensible API with storage and data access patterns 92
cbib/dypfish A Python library for statistical analysis of mRNA and protein distributions in single cell confocal images 2
nipy/nibabel A package that provides access to various neuro-imaging file formats through a unified interface. 654
tiledb-inc/tiledb-r An R interface to the TileDB modern database 103
tiledb-inc/tiledb-go A Go interface to TileDB's storage manager allowing data storage and retrieval in various formats. 51
rraadd88/htsimaging A Python package for analyzing high-throughput single-cell imaging data 5
brainglobe/brainglobe-atlasapi A Python module providing access to brain atlas data from multiple sources. 127
csbdeep/csbdeep Toolbox for improving digital images taken with fluorescence microscopy using deep learning algorithms 285