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AI-Powered Multiplexed Image Analysis to Explore Colon Adenocarcinoma

In this application note, we demonstrate a spatial biology workflow via an AI-powered multiplexed image analysis-based exploration of the tumor immune microenvironment in colon adenocarcinoma.

A Meta-cancer Analysis of the Tumor Spatial Microenvironment

Learn how clustering analysis of Cell DIVE datasets in Aivia can be used to understand tissue-specific and pan-cancer mechanisms of cancer progression
Multiplexed Cell DIVE imaging of Colon Adenocarcinoma (CAC) tissue. A panel of approximately 30 biomarkers targeted towards various leukocyte lineages, epithelial, stromal, and endothelial cell types was utilized to characterize the tumor immune microenvironment in human colon adenocarcinoma (CAC) tissue.

Mapping the Landscape of Colorectal Adenocarcinoma with Imaging and AI

Discover deep insights in colon adenocarcinoma and other immuno-oncology realms through the potent combination of multiplexed imaging of Cell DIVE and Aivia AI-based image analysis
Clustering based analysis reveals various immune cell populations enriched in tumor cells within CT26.WT syngeneic mouse tumor models.

Spatial Architecture of Tumor and Immune Cells in Tumor Tissues

Dig deep into the spatial biology of cancer progression and mouse immune-oncology in this poster, and learn how tumor metabolism can effect immune cell function.
2D slice of colon cancer tissue stained with 30 markers and imaged using the Cell DIVE system. Analysis performed using Aivia 13’s new multiplex cell detection recipe and automatic clustering tool. Each phenotype denoted in a different color.

Transforming Multiplexed 2D Data into Spatial Insights Guided by AI

Aivia 13 handles large 2D images and enables researchers to obtain deep insights into microenvironment surrounding their phenotypes with millions of detected objects and automatic clustering up to 30…
Single cell datasets

Exploring Subcellular Spatial Phenotypes with SPARCS

Discover spatially resolved CRISPR screening (SPARCS), a platform for microscopy-based genetic screening for spatial subcellular phenotypes at the human genome scale.
THY1-EGFP labeled neurons in mouse brain processed using the PEGASOS 2 tissue clearing method, imaged on a Leica confocal microscope. Neurons were traced using Aivia’s 3D Neuron Analysis – FL recipe. Image credit: Hu Zhao, Chinese Institute for Brain Research.

Unlocking Insights in Complex and Dense Neuron Images Guided by AI

The latest advancement in Aivia AI image analysis software provides improved soma detection, additional flexibility in neuron tracing, 3D relational measurement including Sholl analysis and more.
Microscopy for neuroscience research

What are the Challenges in Neuroscience Microscopy?

eBook outlining the visualization of the nervous system using different types of microscopy techniques and methods to address questions in neuroscience.
AI-based workflow for fast rare event detection in living biological samples using Autonomous Microscopy powered by Aivia

AI Microscopy Enables the Efficient Detection of Rare Events

Localization and selective imaging of rare events is key for the investigation of many processes in biological samples. Yet, due to time constraints and complexity, some experiments are not feasible…
Automated Laser Microdissection for Proteome Analysis

Precise Spatial Proteomic Information in Tissues

Despite the availability of imaging-based and mass-spectrometry-based methods for spatial proteomics, a key challenge remains connecting images with single-cell-resolution protein abundance…
Protist Paramecium (Paramecium tetraurelia) stained to show the nucleus

AI-Enabled Spatial Analysis of Complex 3D Datasets

VIDEO ON DEMAND - This edition of MicaCam offers practical advice on the extraction of publication grade insights from microscopy images. Our special guest Luciano Lucas (Leica Microsystems) will…
Mouse whole-mount retina. Image courtesy of the Experimental Ophthalmology Group, University of Murcia, Spain.

Fast, High Acuity Imaging and AI-assisted Analysis

The use of state-of-the-art AI systems is pushing image analysis into a new generation. Challenges like the conflict between imaging power and sample integrity are being overcome with THUNDER’s…
3D reconstruction of an isolated human islet

Create New Options for Live Cell Imaging

The use of state-of-the-art AI systems is pushing image analysis into a new generation. Challenges like the conflict between imaging power and sample integrity are being overcome with THUNDER’s…
Image of fixed U2OS cell expressing mEmerald-Tomm20 denoised using a 3D RCAN model trained with matching low and high SNR image pairs acquired on an iSIM system.

AI Microscopy Image Analysis – An Introduction

Artificial intelligence-guided microscopy image analysis and visualization is a powerful tool for data-driven scientific discovery. AI can help researchers tackle challenging imaging applications,…
Dual color volume rendering of Drp1 oligomers (green) and mito OM (red) in a live U2OS cell

Multicolor 4D Super Resolution Light Sheet Microscopy

The AI Microscopy Symposium offers a unique forum for discussing the latest AI-based technologies and tools in the field of microscopy and biomedical imaging. In this scientific presentation, Yuxuan…
Left-hand image: The distribution of immune cells (white) and blood vessels (pink) in white adipose tissue (image captured using the THUNDER Imager 3D Cell Culture). Right-hand image: The same image after automated analysis using Aivia, with each immune cell color-coded based on its distance to the nearest blood vessel. Image courtesy of Dr. Selina Keppler, Munich, Germany.

Accurately Analyze Fluorescent Widefield Images

The specificity of fluorescence microscopy allows researchers to accurately observe and analyze biological processes and structures quickly and easily, even when using thick or large samples. However,…

Applying AI and Machine Learning in Microscopy and Image Analysis

Prof. Emma Lundberg is a professor in cell biology proteomics at KTH Royal Institute of Technology, Sweden. She is also the director of the Cell Atlas, an integral part of the Swedish-based Human…

Using Machine Learning in Microscopy Image Analysis

Recent exciting advances in microscopy technologies have led to exponential growth in quality and quantity of image data captured in biomedical research. However, analyzing large and increasingly…

The AI-Powered Pixel Classifier

Achieving reproducible results manually requires expertise and is tedious work. But now there is a way to overcome these challenges by speeding up this analysis to extract the real value of the image…
H&E stained micrograph of an intramucosal esophageal adenocarcinoma (left) enhanced with Aivia’s Pixel Classifier (right)

Simplifying the Cancer Biology Image Analysis Workflow

As cancer biology data sets grow, so do the challenges in microscopy image analysis. Aivia experts cover how to overcome these challenges with AI.
Single timepoint of a drosophilia embryo, 3D object detection

Examining Critical Developmental Events in High-Definition

Extended live cell imaging of embryo development requires a delicate balance between light exposure, temporal resolution and spatial resolution to maintain cells’ viability. Compromises between the…
Single timepoint of a time-lapse recording of mammary epithelial micro spheroid cultured in 3D highlighting individual mitotic events

Observing Complex Cellular Interactions at Multiple Scales

Learn how to observe challenging cellular interactions with easy to deploy object detection and relationship measurements.
Aivia_Neuroscience-VBE comparison mouse-1_traced_ROI

Accelerating Neuron Image Analysis with Automation

The ability to examine complex neural processes relies on the accurate reconstruction of neuronal networks at scale. Most data extraction methods in neuroscience research are time-consuming and…

Save Time and Effort with AI-assisted Fluorescence Image Analysis

The powerful synergy of THUNDER and Aivia analyze fluorescence images with greater accuracy, even when using low light excitation.
Separation of cells based on their tracking status: A colourised binary mask of a time-lapse microscopy field of view of medium confluency with individual cells highlighted as survivors if they can be tracked since the initial movie frame (cyan), incomers if they migrated into the field of view throughout the movie (yellow) or mistracks if an error occurred in the automated trajectory reconstruction (red).

Tracking Single Cells Using Deep Learning

AI-based solutions continue to gain ground in the field of microscopy. From automated object classification to virtual staining, machine and deep learning technologies are powering scientific…
Analysis of anatomy and axon orientation of an adult mouse brain tissue with QLIPP.

Learning the Cellular Architecture from its Optical Properties

In the last 3 years, microscopists have started to use "AI based" solutions for a wide range of applications, including image acquisition optimization (smart microscopy), object classification, image…

AI in Microscopy Webinar

We demonstrate residual channel attention networks for restoring and enhancing volumetric time-lapse (4D) fluorescence microscopy data.

적용 분야


인간의 건강과 질병을 기준으로 세포를 이해하는 것에 연구의 초점이 맞추어져 있다면 관심 세포를 시공간 및 분자 측면에서 자세히 조사하는 것은 매우 중요합니다. 이는 현미경이 세포생물학에서 매우 중요한 도구인 이유입니다. 현미경을 사용하면 세포 기관과 고분자를 분석할 뿐만 아니라, 시료의 구조적 환경 내에서 시료를 자세히 연구할 수 있습니다. 세포생물학…
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