Microscopy Image Stitching Tool

Collection of tools and scripts useful to automate microscopy workflows in ZEN Blue using Python and Open Application Development tools and AI tools.

Interoperable web-based DICOM slide microscopy viewer and annotation tool

A tool for fast automatic 3D-stitching of teravoxel-sized microscopy images

A collection of packages and tools for electron microscopy data analysis supported by the National Center for Electron Microscopy facility of the Molecular Foundry

A BaSiC Tool for Background and Shading Correction of Optical Microscopy Images

FluoRender is an interactive tool for fluorescence microscopy data visualization and analysis

Tools for microscopic captures and focus stackshots (beta)

🔬 MCP server for AI-powered napari control. Connect Claude, ChatGPT, Cursor, and other LLMs to napari for interactive image analysis. Perfect for automating microscopy workflows and building intelligent image analysis tools.

SIMcheck: ImageJ tools for assessing Structured Illumination Microscopy (SIM) data quality and reliability

pyCHIP is a tool for segmentation and feature classification in transmission electron microscopy (TEM) images based on a small support set of user-provided examples.

PyKNOSSOS is a software tool for the visualization and annotation of 3D image data and was developed for high-throughput image annotation of 3D electron microscopy stacks of brain tissue. https://www.ariadne.ai

SpectraFox is a free tool for managing, processing, and evaluating scientific scanning probe microscopy (SPM) data-files. For example, data measured by scanning tunneling microscopy (STM) and scanning tunneling spectroscopy (STS), as well as measurements by scanning force microscopy (AFM) can be evaluated more comfortable using this software.

Deep Electron Microscopy Image Stitching (DEMIS): a tool for stitching grids of electron microscopy images. Image matching is performed using LoFTR.

Scanning probe microscopy simulation tools based on CP2K

Python Image Tool for visualizing multidimensional data. Applications include analysis of data in microscopy (STM, SSM, optics), ARPES, XRD, or other multidimensional datasets on regularly gridded coordinates.

Analysis tools for spectroscopic imaging scanning tunneling microscopy / spectroscopy

A simple automated feedback microscopy tool for live tracking of moving samples.

High-throughput droplet microfluidic devices with fluorescence detection systems provide several advantages over conventional end-point cytometric techniques due to their ability to isolate single cells and investigate complex intracellular dynamics. While there have been significant advances in the field of experimental droplet microfluidics, the development of complementary software tools has lagged. Existing quantification tools have limitations including interdependent hardware platforms or challenges analyzing a wide range of high-throughput droplet microfluidic data using a single algorithm. To address these issues, an all-in-one Python algorithm called FluoroCellTrack was developed and its wide-range utility was tested on three different applications including quantification of cellular response to drugs, droplet tracking, and intracellular fluorescence. The algorithm imports all images collected using bright field and fluorescence microscopy and analyzes them to extract useful information. Two parallel steps are performed where droplets are detected using a mathematical Circular Hough Transform (CHT) while single cells (or other contours) are detected by a series of steps defining respective color boundaries involving edge detection, dilation, and erosion. These feature detection steps are strengthened by segmentation and radius/area thresholding for precise detection and removal of false positives. Individually detected droplet and contour center maps are overlaid to obtain encapsulation information for further analyses. FluoroCellTrack demonstrates an average of a ~92-99% similarity with manual analysis and exhibits a significant reduction in analysis time of 30 min to analyze an entire cohort compared to 20 h required for manual quantification.

Microsnoop: A generalist tool for microscopy image representation

Scripts and Tools for Electron Microscopy Image Analysis.

A tool for proofreading and refining synaptic polarity annotations in electron microscopy volumes.

An Image quality ranking tool for Microscopy (forked from Bitbucket)

Tools for computing optical properties of materials and measurable quantities for near-field microscopy

A tool for all-kinds segmentation in microscopy imaging which encourages data centric approaches

Utility tool to co-register IMS data with microscopy modality.

DetecDiv provides a comprehensive set of tools to analyze time microscopy images using deep learning methods. The software structure is such that data can be processed either at the command line or using a graphical user-interface. Detecdiv classification models include : image classification and regression, semantic segmentation, LSTM networks to analyze data and image timeseries.

Neural-network functional microscopy: trivially easy access to hidden-layer activations, gradients, and contributions in your favorite pytorch model + memory-efficient reduction and logging tools.

Precise and quick monitoring of key cytometric features such as cell count, cell size, cell morphology and DNA content is crucial for life research and development. Cytometry is important for numerous applications in biotechnology, medical sciences, and cell culture research laboratories. Flow cytometry that relies on aligning cell flow and their characterization by optical or electrical detection has been the dom- inant cytometry approach for high throughput applications. Recent advances in digital microscopy revealed image cytometry as a viable alternative that can lead to simpler, more compact and less expensive solutions. Traditionally, image cytome- try relies on the use of a hemocytometer accompanied with visual inspection of an operator under the microscope. This approach is prone to error due to subjective decisions of the operator. Machine learning approaches have recently emerged as powerful tools enabling quick and highly accurate image cytometric analysis that are easily generalizable to different cell types. Here, we demonstrate a modular deep learning system (DeepCAN) that provides a complete solution for automated cell counting and viability analysis. DeepCAN employs three different neural network blocks called Parallel Segmenter, Cluster CNN, and Viability CNN that are trained for initial segmentation, cluster separation, and cell viability analysis, respectively. Parallel Segmenter and Cluster CNN blocks achieve highly accurate segmentation of individual cells while Viability CNN block performs viability classification A modified U-Net network, a well-known deep neural network model for bio-image analysis, is used in Parallel Segmenter while LeNet-5 architecture and itss modifid versions are used for Cluster CNN and Viability CNN, respectively. We trained the Parallel Seg- menter using 15 images of A2780 cells and 5 images of yeasts cells containing 14742 individual cell images. Similarly, 6101 and 5900 A2480 cell images were employed for training of Cluster CNN and Viability CNN models. 2514 individual A2780 cell images were used to test the overall segmentation performance of Parallel Segmenter combined with Cluster CNN, revealing a high precision of 96.52%. Overall cell count- ing/viability analysis performance of DeepCAN was tested with A2780 (2514 cells), A549 (601 cells), Colo (356 cells), and MDA-MB-231 (857 cells) cell images reveal- ing high counting/viability accuracies of 93.82 %/95.93 %, 92.18 %/97.90 %, and 85.32 %/97.40 %, respectively.

State of the art analysis tools for electron microscopy