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README.md

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+
+---
+license: cc-by-4.0
+task_categories:
+  - image-to-image
+tags:
+  - FLIM
+  - fluorescence-lifetime
+  - microscopy
+  - TCSPC
+  - biophysics
+size_categories:
+  - 1K<n<10K
+---
+
+
+# FL🦩Mngo Training Dataset: deep learning for fluorescence lifetime predictions
+
+This dataset contains the training data used in [FLIMngo](https://github.com/SofiaKapsiani/FLIMngo), a YOLO-based deep learning network for predicting fluorescence lifetime values from raw TCSPC-FLIM data.
+
+---
+
+## Dataset Summary
+
+The dataset comprises:
+
+- **1242 `.h5` files**, each storing:
+  - **Simulated TCSPC-FLIM data** in the form of 3D tensors `(t, x, y)` 
+  - **Ground truth lifetime maps** as 2D tensors `(x, y)` with per-pixel fluorescence lifetimes (in nanoseconds)
+- **File organisation** the dataset is split across 18 .tar files to aid distribution. Each tar file is approx. 4.5 GB with the whole dataset being 78 GB. 
+
+---
+
+### Simulation parameters
+ 
+- **Laser repetition rate:** 40 MHz
+- **Time bins:** 256, at 0.0977 ns per bin (~25 ns full window)
+- **Photon counts:** 10 - 2500 photons per pixel
+- **Normalisation:** each decay curve has been normalized to have values between 0 and 1
+- **IRF:** each image is convolved with one IRF drawn at random from a library of 12 experimental or simulated IRFs, with laser jitter applied per image.
+- **Lifetimes:** up to four exponential components per pixel, corresponding to microtubules, proteins, nucleus and endoplasmic reticulum channels of the source images. Component ratios are set by Perlin noise.
+- **Noise:** Poisson noise applied after scaling to the per-pixel photon count.
+
+---
+
+## Usage
+
+```python
+import h5py
+import numpy as np
+import torch
+
+# path to an extracted .h5 file
+file_path = '013f0f3a-bbad-11e8-b2ba-ac1f6b6435d0_0.h5'
+
+# internal paths used in the .h5 files
+img_internal_path = 'tpsf'
+label_internal_path = 'tau_maps'
+
+# load data
+with h5py.File(file_path, 'r') as f:
+    raw_data = np.asarray(f[img_internal_path])
+    ground_truth = np.asarray(f[label_internal_path])
+
+# convert to torch tensors
+raw_data_tensor = torch.from_numpy(raw_data).permute(0, 2, 1).float() 
+ground_truth_tensor = torch.from_numpy(ground_truth).permute(1, 0).float()
+```
+
+---
+
+## Licence
+ 
+Released under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). The FLIMngo source code is separately licensed under MIT.
+ 
+---
+ 
+## Citation
+ 
+*If you found **FLIMngo** helpful, please consider citing our work!* 😊
+ 
+<a name="bibtex-citation"></a>
+```bibtex
+@article{Kapsiani2025.02.20.639036,
+    author = {Kapsiani, Sofia and L{\"a}ubli, Nino F and Ward, Edward N and Fernandez-Villegas, Ana and Mazumder, Bismoy and Kaminski, Clemens F and Kaminski Schierle, Gabriele S},
+    title = {Deep learning for fluorescence lifetime predictions enables high-throughput in vivo imaging},
+    elocation-id = {2025.02.20.639036},
+    year = {2025},
+    doi = {10.1101/2025.02.20.639036},
+    URL = {https://www.biorxiv.org/content/early/2025/02/26/2025.02.20.639036},
+    eprint = {https://www.biorxiv.org/content/early/2025/02/26/2025.02.20.639036.full.pdf},
+    journal = {bioRxiv}
+}
+```
+

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