Heath-AFM-Lab/afMLevel-data

afMLevel: AFM Image Levelling and Masking Dataset

Paired atomic force microscopy (AFM) height images used to train and evaluate the U-Net models in afMLevel: a deep learning tool for AFM image levelling through automated background estimation or foreground masking of AFM topography images.

Each sample consists of a raw AFM height image and its corresponding reference-levelled image, produced by conventional manual levelling in NanoLocz. Both are stored as 32-bit float TIFFs. Binary foreground masks are stored as CSVs.

Note: Do not load the TIFFs through a standard Python image pipeline (PIL, imagefolder, PNG conversion) as these will quantise the 32-bit float height data. Use tifffile.

Dataset structure

train/
  Image/      raw height TIFFs          train_image_0000.tiff … train_image_2000.tiff
  Levelled/   reference-levelled TIFFs  train_levelled_0000.tiff … train_levelled_2000.tiff
  Mask/       binary foreground masks   train_mask_0000.csv … train_mask_2000.csv
test/
  Image/                                test_image_000.tiff … test_image_163.tiff
  Levelled/                             test_levelled_000.tiff … test_levelled_163.tiff
  Mask/                                 test_mask_000.csv … test_mask_163.csv
training_data_sources.csv   per-image provenance for train split
test_data_sources.csv       per-image provenance for test split
README.md                   This datacard

Files are paired by numeric index: train_image_0042.tiff, train_levelled_0042.tiff, and train_mask_0042.csv all correspond to the same acquisition frame.

Split	Images
Train	2001
Test	164
Total	2165

Images are 32-bit float, single-channel, and range from 128×128 to 1024×1024 pixels. Some images are non-square. Z-values are in nanometres.

Images are released in their pre-augmentation form. The models were trained on augmented versions of these images (×3 for MLBackground, ×5 for MLMask); users wishing to replicate training should apply augmentation as described in the accompanying paper.

Loading

import tifffile

# Load a paired sample
raw      = tifffile.imread("train/Image/train_image_0042.tiff")       # float32, nm
levelled = tifffile.imread("train/Levelled/train_levelled_0042.tiff")  # float32, nm

# Iterate all training pairs
import os
for fname in sorted(os.listdir("train/Image")):
    if not fname.endswith(".tiff"):
        continue
    idx = fname.replace("train_image_", "").replace(".tiff", "")
    raw = tifffile.imread(f"train/Image/{fname}")
    lev = tifffile.imread(f"train/Levelled/train_levelled_{idx}.tiff")

Derived targets used during training

The model ground-truth targets are derived from the raw and levelled images at load time in the training scripts, not stored separately as image files. Both are deterministic and fully reproducible from this data plus the training code in the afMLevel repository.

Background model target: the normalised difference between raw and levelled, sharing the raw image's z-scale:

import numpy as np

def xyplanefit(im, px=1, py=1):
    mc = np.mean(im, axis=0); x = np.arange(len(mc))
    im = im - np.polyval(np.polyfit(x, mc, px), x)[np.newaxis, :]
    mr = np.mean(im, axis=1); y = np.arange(len(mr))
    return im - np.polyval(np.polyfit(y, mr, py), y)[:, np.newaxis]

def normalise(im):
    im = im - im.min(); return im / (im.max() + 1e-8)

raw_fit  = xyplanefit(raw)
imnorm   = normalise(raw_fit)
levnorm  = (levelled - raw_fit.min()) / (raw_fit.max() - raw_fit.min())
background_target = imnorm - levnorm   # float32

Mask model target: binary foreground mask via sigmoid → Otsu:

from scipy.ndimage import gaussian_filter
from skimage.filters import threshold_otsu

slev  = 1 / (1 + np.exp(-(levelled - np.mean(levelled))))
slev8 = (slev * 255).astype(np.uint8)
blur  = gaussian_filter(slev8, sigma=1)
mask_target = (blur > threshold_otsu(blur)).astype(np.uint8)   # binary {0, 1}

Both pipelines resize to 256×256 (nearest-neighbour) and min-max normalise before training. See the afMLevel repository for the full training scripts.

Provenance

Per-image source information is in training_data_sources.csv and test_data_sources.csv (columns: new_name, Source, Link, Data Reference, Paper Reference).

Images were sourced from internal acquisitions, collaborating research groups, and previously published datasets. Data taken from published sources was already available under CC-BY-4.0; contributing authors of unpublished data have confirmed their agreement to release under CC-BY-4.0.

Filenames are numeric; sample identities are not disclosed for unpublished internal data. Where images originate from published datasets they carry full citations in the source tables.

Contributor	Train	Test	Total
University of Leeds AFM Facility	1106	68	1174
George Heath — Scheuring Lab — New York	465	46	511
Varun Gupta — Heath Lab, University of Leeds	210	21	231
Pyne Lab — University of Sheffield	114	1	115
Nanobiomaterials Group — Paderborn University	58	4	62
Daniel E. Rollins — Heath Lab, University of Leeds	48	9	57
Abimbola Olulana — Hobbs Lab, University of Sheffield	0	8	8
BioMed AFM Summer School — Sheffield	0	7	7
Total	2001	164	2165

Published source datasets — please cite when using these images

Pyne Lab — DNA supercoiling: Pyne et al. (2021). figshare. https://doi.org/10.6084/m9.figshare.13116890.v1 Paper: Pyne, A.L.B. et al. Nat Commun 12, 1053 (2021). https://doi.org/10.1038/s41467-021-21243-y

Pyne Lab — kinetoplast DNA: Whittle et al. (2024). University of Sheffield. https://doi.org/10.15131/shef.data.26869993.v1 Paper: Diggines, B. et al. Phys. Chem. Chem. Phys. 26, 25798–25807 (2024). https://doi.org/10.1039/D4CP01795A

Nanobiomaterials Group Paderborn — DNA origami: Pothineni et al. (2024). Zenodo. https://doi.org/10.5281/zenodo.14275812 Paper: Pothineni, B.K. et al. Discover Nano 20, 77 (2025). https://doi.org/10.1186/s11671-025-04254-2

Important notes

Lateral scale is intentionally omitted. Images are released without pixel size or scan size metadata. The models are scale-agnostic by design (inputs are resized to 256×256 and min-max normalised). Images were exported via MATLAB and carry no embedded acquisition metadata.

Images are not independent. Where images originate from HS-AFM movies, they were capped at a maximum of 20 frames per movie to reduce over-representation of individual acquisitions. Multiple frames from the same movie may still appear in the dataset.

Train/test split. The split is defined by directory membership and corresponds exactly to the split used to produce the results in the accompanying paper.

Licence

This dataset is released under CC-BY-4.0. All contributing groups have confirmed their agreement to this licence. Data from published third-party datasets (Pyne Lab, Nanobiomaterials Group Paderborn) are also available under CC-BY-4.0 via their original repositories (see links above).

The afMLevel code is BSD-3-Clause, see the code repository.

Citation

Links

• Code and models: https://github.com/mayatek1/afMLevel
• Paper: