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Multispectral extended depth-of-field fluorescence microscopy with co-designed meta-optics and neural reconstruction.

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2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 2 matches
  1. [1] § Methods › D-CNN ↔ unet.py, lines 127–203 · score 0.70 · UNet, channel attention, decoder, encoder, learnable, identity
  2. [2] § Methods › D-CNN ↔ lit.py, lines 24–124 · score 0.51 · 0–1, ReLU, bilinear, deblurred, Clamp, batch

Paper

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The authors' code

Python · 203 lines · 7.7 KB · MIT · 1 match

  1. __author__ = "Anil Appak"
  2. __email__ = "[email hidden]"
  3. __organization__ = "Tampere University"
  5. import torch
  6. import torch.nn as nn
  7. import torch.nn.functional as F
  10. # ---------- Channel Attention (SE) ----------
  11. class SE(nn.Module):
  12. def __init__(self, C, r=8, bias_to_one=2.0):
  13. super().__init__()
  14. h = max(1, C // r)
  15. self.fc1 = nn.Linear(C, h)
  16. self.fc2 = nn.Linear(h, C)
  17. # start near identity so we don't throttle early
  18. nn.init.constant_(self.fc2.bias, bias_to_one)
  20. def forward(self, x): # x: [B,C,H,W]
  21. s = x.mean(dim=(2, 3)) # [B,C]
  22. a = torch.sigmoid(self.fc2(F.relu(self.fc1(s)))) # (0,1)
  23. gate = 0.5 + 0.5 * a # ~[0.5,1.0], starts ~0.94
  24. return x * gate[:, :, None, None]
  27. # ---------- Fourier Channel Attention (spectral attention) ----------
  28. class FourierChannelAttention(nn.Module):
  29. """
  30. Scores each channel by its Fourier magnitude statistics and gates features.
  31. Cheap and works well with few wavelengths (e.g., 4).
  32. """
  33. def __init__(self, C, r=8, bias_to_one=2.0):
  34. super().__init__()
  35. h = max(1, C // r)
  36. self.mlp = nn.Sequential(
  37. nn.Linear(C, h), nn.ReLU(inplace=True),
  38. nn.Linear(h, C)
  39. )
  40. self.fca_ln = nn.LayerNorm(C, elementwise_affine=False)
  42. # near-identity start
  43. with torch.no_grad():
  44. for m in self.mlp:
  45. if isinstance(m, nn.Linear):
  46. nn.init.kaiming_uniform_(m.weight, a=1.0)
  47. nn.init.zeros_(m.bias)
  48. self.mlp[-1].bias.fill_(bias_to_one)
  50. def forward(self, x): # x: [B,C,H,W]
  51. X = torch.fft.rfft2(x, norm="backward") # [B,C,H,Wf]
  52. mag = X.abs()
  53. fvec = mag.mean(dim=(2, 3)) # [B,C]
  54. #fvec = self.fca_ln(fvec) # remove per-sample channel-scale bias, channels clearly have different contrast/texture across λ,
  55. #which often means different HF energy/SNR. In that case LN (or a scale-invariant statistic) helps.
  56. print('fvec mean per λ:', fvec.mean(0)) # large spread? -> use LN
  57. a = torch.sigmoid(self.mlp(fvec)) # (0,1)
  58. a = a - a.mean(dim=1, keepdim=True) # relative, zero-mean per sample
  59. gate = 1.0 + 0.3 * a # small ±30% modulation around 1
  60. x = x * gate[:, :, None, None]
  61. print('gate mean per λ:', (1+0.3*torch.sigmoid(self.mlp(fvec))).mean(0)) # always same λ on top?
  63. #gate = 0.5 + 0.5 * a # ~[0.5,1.0]
  64. return x #x * gate[:, :, None, None]
  67. # ---------- building blocks ----------
  68. class DoubleConv(nn.Module):
  69. def __init__(self, in_channels, out_channels, mid_channels=None, gn_groups=8):
  70. super().__init__()
  71. if not mid_channels:
  72. mid_channels = out_channels
  73. self.double_conv = nn.Sequential(
  74. nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1, bias=False),
  75. nn.GroupNorm(num_groups=min(gn_groups, mid_channels), num_channels=mid_channels),
  76. nn.ReLU(inplace=True),
  77. nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False),
  78. nn.GroupNorm(num_groups=min(gn_groups, out_channels), num_channels=out_channels),
  79. nn.ReLU(inplace=True),
  80. )
  82. def forward(self, x):
  83. return self.double_conv(x)
  86. class Down(nn.Module):
  87. def __init__(self, in_channels, out_channels, gn_groups=8):
  88. super().__init__()
  89. self.maxpool_conv = nn.Sequential(
  90. nn.MaxPool2d(2),
  91. DoubleConv(in_channels, out_channels, gn_groups=gn_groups)
  92. )
  94. def forward(self, x):
  95. return self.maxpool_conv(x)
  98. class Up(nn.Module):
  99. def __init__(self, in_channels, out_channels, bilinear=True, gn_groups=8):
  100. super().__init__()
  101. if bilinear:
  102. self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
  103. self.conv = DoubleConv(in_channels, out_channels, in_channels // 2, gn_groups=gn_groups)
  104. else:
  105. self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)
  106. self.conv = DoubleConv(in_channels, out_channels, gn_groups=gn_groups)
  108. def forward(self, x1, x2):
  109. x1 = self.up(x1)
  110. # pad if shapes mismatch
  111. diffY = x2.size()[2] - x1.size()[2]
  112. diffX = x2.size()[3] - x1.size()[3]
  113. x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
  114. diffY // 2, diffY - diffY // 2])
  115. x = torch.cat([x2, x1], dim=1) # fusion
  116. return self.conv(x)
  119. class OutConv(nn.Module):
  120. def __init__(self, in_channels, out_channels):
  121. super().__init__()
  122. self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
  123. def forward(self, x):
  124. return self.conv(x)
  127. # ---------- UNet ----------
  128. class UNet(nn.Module):
  129. def __init__(self, n_channels, n_classes, bilinear=False, gn_groups=8):
  130. super().__init__()
  131. self.n_channels = n_channels
  132. self.n_classes = n_classes
  133. self.bilinear = bilinear
  135. # Per-channel normalization at the very front (learnable affine)
  136. self.in_norm = nn.InstanceNorm2d(n_channels, affine=True, eps=1e-5)
  138. # Identity-initialized spectral 1x1 mixing
  139. self.spectral_mix = nn.Conv2d(n_channels, n_channels, kernel_size=1, bias=True)
  140. with torch.no_grad():
  141. self.spectral_mix.weight.zero_()
  142. for c in range(n_channels):
  143. self.spectral_mix.weight[c, c, 0, 0] = 1.0
  144. self.spectral_mix.bias.zero_()
  146. # Fourier Channel Attention (spectral attention)
  147. self.fca = FourierChannelAttention(n_channels, r=8, bias_to_one=2.0)
  149. # Encoder
  150. self.inc = DoubleConv(n_channels, 32, gn_groups=gn_groups)
  151. self.down1 = Down(32, 64, gn_groups=gn_groups)
  152. self.down2 = Down(64, 128, gn_groups=gn_groups)
  153. factor = 2 if bilinear else 1
  154. self.down3 = Down(128, 256 // factor, gn_groups=gn_groups)
  156. # Decoder + SE after each fusion
  157. self.up2 = Up(256, 128 // factor, bilinear, gn_groups=gn_groups)
  158. self.se_dec2 = SE(128 // factor)
  159. self.up3 = Up(128, 64 // factor, bilinear, gn_groups=gn_groups)
  160. self.se_dec3 = SE(64 // factor)
  161. self.up4 = Up(64, 32, bilinear, gn_groups=gn_groups)
  162. self.se_dec4 = SE(32)
  164. self.outc = OutConv(32, n_classes)
  166. # If you want residual later:
  167. self.use_residual_head = True
  168. self.res_scale = 0.5 # only used if use_residual_head=True
  170. self.out_gain = nn.Parameter(torch.ones(1, n_classes, 1, 1)) # start ~1–2
  171. self.out_bias = nn.Parameter(torch.zeros(1, n_classes, 1, 1)) # start 0
  173. def forward(self, x):
  174. # front-end normalization and spectral attention
  175. x = self.in_norm(x)
  176. x_mixed = self.spectral_mix(x)
  177. x_mixed = self.fca(x_mixed)
  179. # encoder
  180. x1 = self.inc(x_mixed)
  181. x2 = self.down1(x1)
  182. x3 = self.down2(x2)
  183. x4 = self.down3(x3)
  185. # decoder with SE after fusions
  186. y = self.up2(x4, x3)
  187. #y = self.se_dec2(y)
  188. y = self.up3(y, x2)
  189. #y = self.se_dec3(y)
  190. y = self.up4(y, x1)
  191. #y = self.se_dec4(y)
  193. o = self.outc(y)
  195. # Stable head
  196. if not self.use_residual_head:
  197. out = torch.sigmoid(o) # bounded, non-saturating early
  198. else:
  199. #out = (x_mixed + self.res_scale * o).clamp(0, 1)
  200. out_pre = x_mixed + self.res_scale * o # residual head
  201. out = torch.sigmoid(self.out_gain * out_pre + self.out_bias) # <— no hard clamp
  203. return out

unet.py at commit 3e12bf2, under MIT · at the source

Overview

Authors: Ipek Anil Atalay Appak1,2, Haobijam Johnson Singh1, Sanna Korpela3, Teemu O. Ihalainen3, Erdem Sahin4, Christine Guillemot2, Humeyra Caglayan1,5
ORCID iDs: Ipek Anil Atalay Appak, Haobijam Johnson Singh, Sanna Korpela, Teemu O. Ihalainen, Erdem Sahin, Humeyra Caglayan
  1. Physics Unit, Faculty of Engineering and Natural Sciences, Tampere University,Tampere, 33720 Finland
  2. INRIA Rennes, Bretagne Atlantique Research Centre,Rennes, 35042 France
  3. Bio Unit, Faculty of Medicine and Health Technology, Tampere University,Tampere, 33520 Finland
  4. Signal Processing Research Centre, Tampere University,Tampere, 33720 Finland
  5. Department of Electrical Engineering, Photonic Integration Group, Eindhoven University of Technology,Eindhoven, 5600 MB The Netherlands
Institutions: Tampere University (Finland); Centre Inria de l'Université de Rennes (France); Eindhoven University of Technology (Netherlands)
Journal: Light, science & applications, volume 15, issue 1, article 242
Dates: received 4 December 2025; accepted 28 April 2026; published online 19 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41377-026-02337-y · PMID 42156362 · PMCID PMC13187408 · OpenAlex W7161640643
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality)
Methods: Connectivity, Machine learning, fMRI & imaging
Keywords: Wide-field fluorescence microscopy, Metamaterials, Nanophotonics and plasmonics
Topic: Advanced Fluorescence Microscopy Techniques (Biophysics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Jane ja Aatos Erkon Säätiö (High-speed 3D Microscopy); Research Council of Finland (project no. 336357, PROFI 6 - TAU Imaging Research Platform); H2020 Marie Skłodowska‐Curie Actions (956770)
Citations: not cited yet (Europe PMC); 36 references in the paper

Abstract

High numerical aperture fluorescence microscopy provides subcellular resolution, but its depth of field is extremely limited, so thick specimens quickly fall out of focus and typically require axial scanning. Multispectral imaging further compounds this problem because chromatic aberrations shift the best focus plane and distort registration across emission channels, especially in thick samples. In this work, we present MANTIS (Multispectral All-Depth meta-opTic Imaging System), a co-designed computational microscopy imaging system that achieves extended depth-of-field from a single acquisition without axial scanning. MANTIS combines a learned meta-optic with a physics-guided neural network trained end-to-end to reconstruct sharp multispectral images from depth, and wavelength-dependent blurred sensor measurements. We experimentally demonstrate a 50 μm extended depth of field at NA 1.1, corresponding to an 82-fold increase over a conventional wide-field microscope. In addition, simulations show that MANTIS can target different depth-of-field ranges, with the expected trade-off that larger depth-of-field ranges come at a cost in reconstruction fidelity. We validate the approach on biologically relevant fluorescence specimens, including 50 μm thick three-dimensional cultured MDCK II spheroids, and show that, compared with conventional wide-field fluorescence microscopy, reconstructions maintain contrast and lateral detail across depth, with reduced defocus blur and consistent performance across spectral channels.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

humeyracaglayan/MANTIS

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 3e12bf2e7e1dba9d1b923adc5670084051cbec2f, 27 February 2026
Languages: Python (6)
Size: 28 files, 6 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: PyTorch (5 files), PyTorch Lightning (3 files), NumPy (2 files), Pillow (2 files), Matplotlib (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
8 files
At the source: github.com/humeyracaglayan/MANTIS

Code availability

Training and inference code for the reconstruction network, together with pretrained model weights and example configuration files, are available on GitHub: https://github.com/humeyracaglayan/MANTIS. A black box version of the optical forward model, which maps ground-truth images to simulated sensor measurements, is also provided. The differentiable implementation of the optics forward model is available from the authors upon reasonable request, subject to intellectual property constraints.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 6 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability

The MDCK fluorescence dataset acquired in this work has been deposited in Zenodo: https://zenodo.org/records/18609221. Third-party data from the Hyperspectral Image Dataset are publicly available as cited in the manuscript. Additional data supporting the findings of this study are available from the corresponding authors upon reasonable request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 3 keywords, 3 funders, 31 references.

Cite

This paper

Atalay Appak, I. A., Singh, H. J., Korpela, S., Ihalainen, T. O., Sahin, E., Guillemot, C., & Caglayan, H. (2026). Multispectral extended depth-of-field fluorescence microscopy with co-designed meta-optics and neural reconstruction. Light, science & applications, 15(1), 242. https://doi.org/10.1038/s41377-026-02337-y

BibTeX

@article{atalayappak2026multispectral,
author = {Atalay Appak, Ipek Anil and Singh, Haobijam Johnson and Korpela, Sanna and Ihalainen, Teemu O. and Sahin, Erdem and Guillemot, Christine and Caglayan, Humeyra},
title = {{Multispectral extended depth-of-field fluorescence microscopy with co-designed meta-optics and neural reconstruction}},
journal = {Light, science \& applications},
year = {2026},
month = may,
volume = {15},
number = {1},
pages = {242},
publisher = {Nature Publishing Group},
issn = {2095-5545},
doi = {10.1038/s41377-026-02337-y},
url = {https://doi.org/10.1038/s41377-026-02337-y},
pmid = {42156362},
pmcid = {PMC13187408}
}

RIS

TY - JOUR
AU - Atalay Appak, Ipek Anil
AU - Singh, Haobijam Johnson
AU - Korpela, Sanna
AU - Ihalainen, Teemu O.
AU - Sahin, Erdem
AU - Guillemot, Christine
AU - Caglayan, Humeyra
TI - Multispectral extended depth-of-field fluorescence microscopy with co-designed meta-optics and neural reconstruction
T2 - Light, science & applications
J2 - Light Sci Appl
PY - 2026
DA - 2026/05/19
VL - 15
IS - 1
SP - 242
SN - 2095-5545
PB - Nature Publishing Group
DO - 10.1038/s41377-026-02337-y
UR - https://doi.org/10.1038/s41377-026-02337-y
LA - en
ER -

CSL-JSON

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