Science Score: 57.0%
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Low similarity (11.3%) to scientific vocabulary
Repository
Object Detection and 3DLocalization
Basic Info
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- Stars: 0
- Watchers: 1
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- Releases: 3
Metadata Files
README.md
LUENN: PyTorch Package for 3D Single Molecule Localization Microscopy
LUENN (Localization3D) is an open-source PyTorch package for 3D Single Molecule Localization Microscopy (SMLM).
It delivers end-to-end pipelines for synthetic data generation, deep learning model training, sub-pixel emitter localization, and 3D super-resolution image rendering.
Validated in peer-reviewed research, LUENN enables fast, accurate, and robust live-cell imaging, bridging the gap between computational microscopy and biomedical applications.
📄 Results published in: Applied Optics, 2024
✨ Key Features
- Data Generation → create synthetic microscopy datasets for training and benchmarking.
- Sampling Utilities → extract training/validation subsets efficiently from large datasets.
- Custom Model Training → PyTorch-based CNNs for SMLM with flexible architectures and loss functions.
- Post-Processing Functions → refine localization results, suppress artifacts, and enhance reconstruction quality.
- 3D Localization → achieve sub-pixel emitter positioning in 3D, surpassing classical resolution limits.
- Rendering Tools → generate and visualize high-resolution 3D reconstructions, including live-cell time series.
🎥 Example: 3D reconstruction and rendering of a live-cell dataset
🚀 Performance
- Robust across 2D, 3D, and live-cell microscopy modalities.
- Achieves sub-10 nm localization accuracy under challenging noise/light conditions.
- Processes live-cell SMLM data in ~3 seconds, enabling dynamic biological imaging.
📦 Installation
- Clone or download this repository.
- Create the environment (Linux example):
```bash conda env create -f environment_linux.yml conda activate LUENN - Run training, inference, and reconstruction pipelines.
Performance
Luenn has demonstrated exceptional accuracy across a broad spectrum of imaging conditions. Its ability to handle live-cell SMLM data with reduced light exposure in just 3 seconds makes it a valuable asset for dynamic imaging scenarios.
System Requirements
- Linux (Ubuntu 20.04+) or Windows 10
- Python 3.8+
- NVIDIA GPU with CUDA support (tested on Titan Xp, 12 GB VRAM)
- 32 GB RAM recommended
Contributers:
Armin Abdehkakha, Email: arminabd@buffalo.edu
Craig Snoeyink, Email: craigsno@buffalo.edu
Owner
- Name: Armin Abdehkakha
- Login: arminabdeh
- Kind: user
- Company: University at Buffalo
- Website: https://www.researchgate.net/profile/Armin-Abdehkakha-2?ev=hdr_xprf
- Repositories: 1
- Profile: https://github.com/arminabdeh
Ph.D. Candidate, Mechanical Engineering, University at Buffalo
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: LUENN
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Armin
family-names: Abdehkakha
email: arminabd@buffalo.edu
affiliation: University at Buffalo
- given-names: Craig
family-names: Snoeyink
email: craigsno@buffalo.edu
affiliation: University at Buffalo
orcid: 'https://orcid.org/0000-0001-7215-2554'
repository-code: 'https://github.com/arminabdeh/LUENN_tf_version'
abstract: >-
The development of Single-Molecule Localization Microscopy
(SMLM) has enabled the visualization of sub-cellular
structures, but its temporal resolution is limited. To
address this issue, a deep-convolutional neural network
called LUENN has been introduced, which uses a unique
architecture that rejects the isolated emitter assumption.
LUENN is a Python package based on a deep CNN that
utilizes the Tensorflow tool for SMLM. It is capable of
achieving high accuracy for a wide range of imaging
modalities and frame densities.
keywords:
- Convolutional Neural Network
- Single Molecule Localization Microscopy
- Super-Resolution Microscopy
license: MIT
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Dependencies
- actions/checkout v4 composite
- actions/setup-python v3 composite