ArticleCell reports methods2026
Unsupervised deep learning enables blur-free resolution enhancement in two-photon microscopy.
Article in Cell reports methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Two-photon microscopy enables the non-invasive imaging of deep living tissue. Quantitative three-dimensional analysis is hampered by axial blur and anisotropic resolution in two-photon microscopy. We introduce the two-photon microscopy image enhancement network (TENET), a fully unsupervised framework that simultaneously performs deblurring, up to 12× resolution enhancement, and semantic segmentation on two-photon microscopy volumes in a single pass. TENET embeds a physics-informed blur-generation module with a trainable neural implicit point spread function (PSF), requiring only approximate PSF initialization rather than rigorous experimental measurement, paired "unblurred" images, or isotropy assumptions. On synthetic images, fluorescent beads, and in vivo microglia datasets, TENET surpasses the total-variation-regularized Richardson-Lucy (RLTV) algorithm, CARE, and Neuroclear in image fidelity and segmentation accuracy. Using time-lapse images of microglia surrounding metastatic brain tumors, TENET enables automated 3D morphometry that reveals dynamic microglia-tumor interactions. By converting blur-limited two-photon microscopy data into high-fidelity volumetric reconstructions with ready-to-use masks, TENET streamlines downstream analysis and expands the reach of deep-tissue live imaging.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.