ArticleNature communications2023
A deep learning-based stripe self-correction method for stitched microscopic images.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
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Who cites it
9 citing papers in PubMed.
- From Label-Free Multiphoton Imaging to Pathological Reports: A Vision-Language Breast Cancer Margin Pathological Diagnosis System.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Automatic Tuning of Gaussian Filter for Image Vignetting Correction.Sensors (Basel, Switzerland) · 2026Article
- Automatic optimization of flat-field corrections by evaluation and enhancement (EVEN) in multimodal optical microscopy.Nature communications · 2026Article
- FUSION: A fast and uniform processing framework for whole-brain optical scattering tomography images.bioRxiv : the preprint server for biology · 2026Article
- Integrative whole slide image and spatial transcriptomics analysis with QuST and QuPath.NPJ precision oncology · 2025Article
- Improving the U-Net Configuration for Automated Delineation of Head and Neck Cancer on MRI.Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings · 2025Article
- Spatial analysis by current multiplexed imaging technologies for the molecular characterisation of cancer tissues.British journal of cancer · 2024Review
- Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy.Light, science & applications · 2024Review
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
Stitched fluorescence microscope images inevitably exist in various types of stripes or artifacts caused by uncertain factors such as optical devices or specimens, which severely affects the image quality and downstream quantitative analysis. Here, we present a deep learning-based Stripe Self-Correction method, so-called SSCOR. Specifically, we propose a proximity sampling scheme and adversarial reciprocal self-training paradigm that enable SSCOR to utilize stripe-free patches sampled from the stitched microscope image itself to correct their adjacent stripe patches. Comparing to off-the-shelf approaches, SSCOR can not only adaptively correct non-uniform, oblique, and grid stripes, but also remove scanning, bubble, and out-of-focus artifacts, achieving the state-of-the-art performance across different imaging conditions and modalities. Moreover, SSCOR does not require any physical parameter estimation, patch-wise manual annotation, or raw stitched information in the correction process. This provides an intelligent prior-free image restoration solution for microscopists or even microscope companies, thus ensuring more precise biomedical applications for researchers.
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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.