Image processing articles within Nature Communications

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  • Article
    | Open Access

    Intestinal homeostasis is maintained by interactions between the gut-associated lymphoid tissue and the resident flora. Here Montorsi et al use multiplexed single cell omics to describe double negative type 2 B cells and DNASE1L3-expressing dendritic cells that interact and associate with microbiota on the human gut antigenic front line.

    • Lucia Montorsi
    • , Michael J. Pitcher
    •  & Jo Spencer

  • Article
    | Open Access

    Identifying active compounds for a target is time- and resource-intensive. Here, the authors show that deep learning models trained on Cell Painting and single-point activity data, can reliably predict compound activity across diverse targets while maintaining high hit rates and scaffold diversity.

    • Johan Fredin Haslum
    • , Charles-Hugues Lardeau
    •  & Erik Müllers

  • Article
    | Open Access

    Improved imaging techniques are required to help advance our understanding of the complex role of the tumour microenvironment (TME). Here, the authors develop a high-throughput, highly multiplexed tissue visualisation workflow and demonstrate its utility by characterising the response of the TME to radiotherapy in preclinical models of glioblastoma.

    • Spencer S. Watson
    • , Benoit Duc
    •  & Johanna A. Joyce

  • Article
    | Open Access

    PDL1 expression is a common biomarker for immunotherapy response in cancer, and it is usually quantified using immunohistochemistry. Here, the authors develop a weakly supervised learning approach combining multiple instance learning and a teacher-student framework to predict PDL1 expression from histopathological imaging.

    • Darui Jin
    • , Shangying Liang
    •  & Xiangzhi Bai

  • Article
    | Open Access

    Histopathology can be limited by the time-consuming and labour-intensive preparation of slides from resected tissue. Here, the authors report DeepDOF-SE, a deep-learning-enabled microscope to rapidly scan intact tissue at cellular resolution without the need for physical sectioning.

    • Lingbo Jin
    • , Yubo Tang
    •  & Ashok Veeraraghavan

  • Article
    | Open Access

    Assessing cell phenotypes in image-based assays requires solid computational methods for transforming images into quantitative data. Here, the authors present a strategy for learning representations of treatment effects from high-throughput imaging, following a causal interpretation.

    • Nikita Moshkov
    • , Michael Bornholdt
    •  & Juan C. Caicedo

  • Article
    | Open Access

    Accurate localization of abnormalities is crucial in the interpretation of chest X-rays. Here the authors present a deep learning framework for simultaneous localization of 14 thoracic abnormalities and calculation of cardiothoracic ratio, based on large X-ray dataset with bounding boxes created via a human-in-the-loop approach.

    • Weijie Fan
    • , Yi Yang
    •  & Dong Zhang

  • Article
    | Open Access

    Cancer biomarkers are often continuous measurements, which poses challenges for their prediction using classification-based deep learning. Here, the authors develop a regression-based deep learning method to predict continuous biomarkers - such as the homologous repair deficiency score - from cancer histopathology images.

    • Omar S. M. El Nahhas
    • , Chiara M. L. Loeffler
    •  & Jakob Nikolas Kather

  • Article
    | Open Access

    High-throughput electron microscopy demands minimal human intervention and high image quality. Here, authors introduce DeepFocus, a data-driven method for aberration correction in electron microscopy, robust for low SNR images, fast and easily adaptable to microscopes and samples. Peer Review Information: Nature Communications thanks Yang Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

    • P. J. Schubert
    • , R. Saxena
    •  & J. Kornfeld

  • Article
    | Open Access

    Segmentation accuracy of serial section electron microscopy (ssEM) images can be limited by the step of aligning 2D section images to create a 3D image stack. Here the authors report a computational pipeline for aligning ssEM images and apply this to a whole fly brain dataset.

    • Sergiy Popovych
    • , Thomas Macrina
    •  & H. Sebastian Seung

  • Article
    | Open Access

    Deep learning frameworks require large human-annotated datasets for training and the resulting ‘black box’ models are difficult to interpret. Here, the authors present Kartezio; a modular Cartesian Genetic Programming-based computational strategy that generates fully transparent and easily interpretable image processing pipelines.

    • Kévin Cortacero
    • , Brienne McKenzie
    •  & Sylvain Cussat-Blanc

  • Article
    | Open Access

    Biological research relies on observing cell phenotypes, often obscured by natural variability. Here, the authors use generative modelling to unveil hidden changes triggered by infections, mutations, or drugs, allowing for accessible discovery of biomarkers.

    • Alexis Lamiable
    • , Tiphaine Champetier
    •  & Auguste Genovesio

  • Article
    | Open Access

    Modern microscopes can image a sample with sub-Angstrom and sub-picosecond resolutions, but this often requires analysis of tremendously large datasets. Here, the authors demonstrate that an autonomous experiment can yield over a 70% reduction in dataset size while still producing high-fidelity images of the sample.

    • Saugat Kandel
    • , Tao Zhou
    •  & Mathew J. Cherukara

  • Article
    | Open Access

    The spatial organization of a tumor affects how it grows and responds to treatment. Here, the authors present VALIS, a software to align sets of whole slide images (WSI) with state-of-the-art accuracy, enabling spatial studies of the tumor ecology.

    • Chandler D. Gatenbee
    • , Ann-Marie Baker
    •  & Alexander R. A. Anderson

  • Article
    | Open Access

    Existing single-molecule localization microscopy analyses overlook important temporal information in living cells. Here, the authors report nanoscale spatiotemporal indexing clustering (NASTIC), which leverages a video game algorithm to fast-track the investigation of the complex temporal dynamics of protein clustering.

    • Tristan P. Wallis
    • , Anmin Jiang
    •  & Frédéric A. Meunier

  • Article
    | Open Access

    Spatial proteomic data serve to provide cell-level location information for the extraction of biological features from tissues, but analyzing such data can be difficult. Here the authors report the development of SPIAT for data analyses and spaSim for simulation and validation of methods to help bridge the gap between the technology and its translation.

    • Yuzhou Feng
    • , Tianpei Yang
    •  & Anna S. Trigos

  • Article
    | Open Access

    Access to Whole-Slide Images has become a cornerstone of the development of AI methods in pathology, for diagnostic use and research. Authors have developed model for privacy risks analysis and propose guidelines for safe sharing of WSI data.

    • Petr Holub
    • , Heimo Müller
    •  & Tomáš Brázdil

  • Article
    | Open Access

    Spatial visualization of metabolites in tissues via mass spectrometry imaging can be prone to user perception bias. Here, the authors report the computational framework moleculaR that introduces probabilistic data-dependent molecular mapping of nonrandom spatial patterns of metabolite signals.

    • Denis Abu Sammour
    • , James L. Cairns
    •  & Carsten Hopf

  • Article
    | Open Access

    Object detection using machine learning universally requires vast amounts of training datasets. Midtvedt et al. proposes a deep-learning method that enables detecting microscopic objects with sub-pixel accuracy from a single unlabeled image by exploiting the roto-translational symmetries of the problem.

    • Benjamin Midtvedt
    • , Jesús Pineda
    •  & Giovanni Volpe

  • Article
    | Open Access

    Rapid antibiotic susceptibility testing (AST) is needed. Here the authors report a method for phenotypic AST at the single cell level, using a microfluidic chip that allows for subsequent genotyping with in situ FISH; they apply this to a mixed sample of 7 species and 4 antibiotics.

    • Vinodh Kandavalli
    • , Praneeth Karempudi
    •  & Johan Elf

  • Article
    | Open Access

    Artificial Intelligence can support diagnostic workflows in oncology, but they are vulnerable to adversarial attacks. Here, the authors show that convolutional neural networks are highly susceptible to white- and black-box adversarial attacks in clinically relevant classification tasks.

    • Narmin Ghaffari Laleh
    • , Daniel Truhn
    •  & Jakob Nikolas Kather

  • Article
    | Open Access

    Understanding blood and lymphatic vasculature networks is currently limited by existing imaging systems and quantification methods. Here the authors use the tissue clearing method CUBIC to generate 3D images, machine learning to capture the signals, and extract geometric features by topological data analysis.

    • Kei Takahashi
    • , Ko Abe
    •  & Kohei Miyazono

  • Article
    | Open Access

    DNA-PAINT image acquisition is limited by speed. Here the authors use the neural network DeepSTORM to predict fluorophore positions from high emitter density DNA-PAINT data in order to achieve image acquisition in one minute; they demonstrate multi-colour and large-area imaging of semi-thin neuronal tissue.

    • Kaarjel K. Narayanasamy
    • , Johanna V. Rahm
    •  & Mike Heilemann

  • Article
    | Open Access

    Live imaging of organoid growth remains a challenge: it requires long-term imaging of several samples simultaneously and dedicated analysis pipelines. Here the authors report an experimental and image processing framework to turn long-term light-sheet imaging of intestinal organoids into digital organoids.

    • Gustavo de Medeiros
    • , Raphael Ortiz
    •  & Prisca Liberali

  • Comment
    | Open Access

    A plethora of work has shown that AI systems can systematically and unfairly be biased against certain populations in multiple scenarios. The field of medical imaging, where AI systems are beginning to be increasingly adopted, is no exception. Here we discuss the meaning of fairness in this area and comment on the potential sources of biases, as well as the strategies available to mitigate them. Finally, we analyze the current state of the field, identifying strengths and highlighting areas of vacancy, challenges and opportunities that lie ahead.

    • María Agustina Ricci Lara
    • , Rodrigo Echeveste
    •  & Enzo Ferrante

  • Article
    | Open Access

    In microscopy, applications in which reactiveness is needed are multifarious. Here the authors report MicroMator, a Python software package for reactive experiments, which they use for applications requiring real-time tracking and light-targeting at the single-cell level.

    • Zachary R. Fox
    • , Steven Fletcher
    •  & Gregory Batt

  • Article
    | Open Access

    Cell segmentation of single-cell spatial proteomics data remains a challenge and often relies on the selection of a membrane marker, which is not always known. Here, the authors introduce RAMCES, a method that selects the optimal membrane markers to use for more accurate cell segmentation.

    • Monica T. Dayao
    • , Maigan Brusko
    •  & Ziv Bar-Joseph

  • Article
    | Open Access

    Current high-dimension imaging data analysis methods are technology-specific and require multiple tools, restricting analytical scalability and result reproducibility. Here the authors present SIMPLI, a software that overcomes these limitations for single-cell and pixel analysis of multiplexed images at spatial resolution.

    • Michele Bortolomeazzi
    • , Lucia Montorsi
    •  & Francesca D. Ciccarelli

  • Article
    | Open Access

    Single-molecule localisation microscopy does not give orientation information. Here the authors combine Stochastic Optical Reconstruction Microscopy (STORM) with single molecule orientation and wobbling measurements using four-polarisation image splitting, 4polar-STORM.

    • Caio Vaz Rimoli
    • , Cesar Augusto Valades-Cruz
    •  & Sophie Brasselet

  • Article
    | Open Access

    In situ transcriptomics maps RNA expression patterns across intact tissues taking our understanding of gene expression to a new level. Here, the authors present a computational method that uncovers gene expression, cell niche, and tissue region patterns from 2D and 3D spatial transcriptomics.

    • Yichun He
    • , Xin Tang
    •  & Xiao Wang

  • Article
    | Open Access

    Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to obtain in many clinical applications. Here, the authors introduce an open-source framework to handle imperfect training datasets.

    • Shanshan Wang
    • , Cheng Li
    •  & Hairong Zheng

  • Article
    | Open Access

    In clinical practice, the continuous progress of image acquisition technology or diagnostic procedures and evolving imaging protocols hamper the utility of machine learning, as prediction accuracy on new data deteriorates. Here, the authors propose a continual learning approach to deal with such domain shifts occurring at unknown time points.

    • Matthias Perkonigg
    • , Johannes Hofmanninger
    •  & Georg Langs

  • Article
    | Open Access

    Determining the quality of localisation microscopy images is currently challenging. Here the authors report use of the Haar wavelet kernel analysis (HAWK) Method for the Assessment of Nanoscopy, termed HAWKMAN, to assess the reliability of localisation information.

    • Richard J. Marsh
    • , Ishan Costello
    •  & Susan Cox

  • Article
    | Open Access

    Inaccurate cell segmentation has been the major problem for cell-type identification and tissue characterization of the in situ spatially resolved transcriptomics data. Here we show a robust cell segmentation-free computational framework (SSAM), for identifying cell types and tissue domains in 2D and 3D.

    • Jeongbin Park
    • , Wonyl Choi
    •  & Naveed Ishaque

  • Article
    | Open Access

    Recent advances in super-resolution microscopy have made it possible to measure chromatin 3D structure and transcription in thousands of single cells. Here, authors present a deep learning-based approach to characterise how chromatin structure relates to transcriptional state of individual cells and determine which structural features of chromatin regulation are important for gene expression state.

    • Aparna R. Rajpurkar
    • , Leslie J. Mateo
    •  & Alistair N. Boettiger

  • Article
    | Open Access

    High-content screening prompted the development of software enabling discrete phenotypic analysis of single cells. Here, the authors show that supervised continuous machine learning can drive novel discoveries in diverse imaging experiments and present the Regression Plane module of Advanced Cell Classifier.

    • Abel Szkalisity
    • , Filippo Piccinini
    •  & Peter Horvath

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