Metadata-Version: 2.1
Name: deepblink
Version: 0.1.4
Summary: Threshold independent detection and localization of diffraction-limited spots.
Home-page: https://github.com/bbquercus/deepblink/
Author: Bastian Eichenberger, YinXiu Zhan
Author-email: bastian@eichenbergers.ch, yinxiuzhan89@gmail.com
License: MIT
Project-URL: Documentation, https://github.com/BBQuercus/deepBlink/wiki
Project-URL: Changelog, https://github.com/BBQuercus/deepBlink/releases
Project-URL: Issue Tracker, https://github.com/bbquercus/deepblink/issues
Keywords: deep-learning,biomedical,image analysis,spot detection
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Artificial Life
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: Topic :: Utilities
Description-Content-Type: text/plain
License-File: LICENSE

In biomedical microscopy data, a common task involves the detection of diffraction-limited spots that
visualize single proteins, domains, mRNAs, and many more. These spots were traditionally detected with
mathematical operators such as Laplacian of Gaussian. These operators, however, rely on human input ranging
from image-intensity thresholds, approximative spot sizes, etc. This process is tedious and not always
reliable.

DeepBlink relies on neural networks to automatically find spots without the need for human
intervention. DeepBlink is available as a ready-to-use command-line interface.

All deepBlink wheels distributed on PyPI are MIT licensed.
