ArticleCommunications biology2022
UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- RNF43 p.G659fs leads to natural killer cell dysfunction in MSI-high colorectal cancer through PI3K/AKT/mTOR signaling and HLA-E up-regulation.Science advances · 2026Article
- Modeling development of tertiary lymphoid structures in pulmonary tuberculosis by 3D profiling and trajectory analysis.bioRxiv : the preprint server for biology · 2026Article
- Review
- The evolving landscape of spatial proteomics technologies in the AI age.Fundamental research · 2026Review
- SEAL: Spatially-resolved Embedding Analysis with Linked Imaging Data.IEEE transactions on visualization and computer graphics · 2026Article
- Antibody-Based Multiplex Image Analysis: Standard Analytical Workflows and Artificial Intelligence Tools for Pathologists.Laboratory investigation; a journal of technical methods and pathology · 2025Review
- Multi-modal image analysis for large-scale cancer tissue studies within IMMUcan.Cell reports methods · 2025Article
- Effects of segmentation errors on downstream-analysis in highly-multiplexed tissue imaging.PLoS computational biology · 2025Article
- Integrative multi-omics reveals a regulatory and exhausted T-cell landscape in CLL and identifies galectin-9 as an immunotherapy target.Nature communications · 2025Article
- Novel dual gland GAN architecture improves human protein localization classification using salivary and pituitary gland inspired loss functions.Scientific reports · 2025Article
- SEAL : Spatially-resolved Embedding Analysis with Linked Imaging Data.bioRxiv : the preprint server for biology · 2025Article
- Morphology-Aware Profiling of Highly Multiplexed Tissue Images using Variational Autoencoders.bioRxiv : the preprint server for biology · 2025Article
- Multimodal Spatial Profiling Reveals Immune Suppression and Microenvironment Remodeling in Fallopian Tube Precursors to High-Grade Serous Ovarian Carcinoma.Cancer discovery · 2025Article
- Local mean suppression filter for effective background identification in fluorescence images.Computers in biology and medicine · 2025Article
- Effect of magnetic field strength and segmentation variability on the reproducibility and repeatability of radiomic texture features in cardiovascular magnetic resonance parametric mapping.The international journal of cardiovascular imaging · 2025Article
- Article
- Quality control for single-cell analysis of high-plex tissue profiles using CyLinter.Nature methods · 2024Article
- Spatial analysis by current multiplexed imaging technologies for the molecular characterisation of cancer tissues.British journal of cancer · 2024Review
- Multimodal Spatial Profiling Reveals Immune Suppression and Microenvironment Remodeling in Fallopian Tube Precursors to High-Grade Serous Ovarian Carcinoma.bioRxiv : the preprint server for biology · 2024Article
- UNSEG: unsupervised segmentation of cells and their nuclei in complex tissue samples.Communications biology · 2024Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Upcoming technologies enable routine collection of highly multiplexed (20-60 channel), subcellular resolution images of mammalian tissues for research and diagnosis. Extracting single cell data from such images requires accurate image segmentation, a challenging problem commonly tackled with deep learning. In this paper, we report two findings that substantially improve image segmentation of tissues using a range of machine learning architectures. First, we unexpectedly find that the inclusion of intentionally defocused and saturated images in training data substantially improves subsequent image segmentation. Such real augmentation outperforms computational augmentation (Gaussian blurring). In addition, we find that it is practical to image the nuclear envelope in multiple tissues using an antibody cocktail thereby better identifying nuclear outlines and improving segmentation. The two approaches cumulatively and substantially improve segmentation on a wide range of tissue types. We speculate that the use of real augmentations will have applications in image processing outside of microscopy.
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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.