How does this skill handle large microscopy image files?

It utilizes pyvips with sequential access mode to stream data, allowing it to process massive 16-bit TIFFs efficiently without exhausting system memory.

Does it support segmentation mask processing?

Yes, it uses nearest-neighbor resampling for masks to ensure that integer label IDs are preserved exactly during the tile generation process.

How does it handle channel colors in TissUUmaps?

It features a predefined 22-marker mapping and a 15-color fallback cycle, using regex normalization to match channel names to specific visualization colors.

Can I avoid re-converting files that haven't changed?

Yes, it implements a skip-existing manifest that tracks file modification times and sizes, ensuring only new or updated images are re-processed.

How is the deployment of tiles managed?

The skill includes rsync-based deployment commands to synchronize generated DZI tiles and .tmap project files to a target web server or Apache directory.

KINTSUGI TissUUmaps Export Pipeline

Name: KINTSUGI TissUUmaps Export Pipeline
Author: smith6jt-cop

bysmith6jt-cop

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データサイエンスとML

Automates the conversion of high-resolution microscopy images into web-optimized DZI tiles and .tmap project files for TissUUmaps visualization.

This skill streamlines the transition from processed spatial biology datasets to collaborative web-based visualization. It leverages pyvips for high-performance DZI tile generation, ensuring that massive 16-bit grayscale images and segmentation masks are processed efficiently via sequential streaming. By automating the creation of .tmap project files, handling complex marker color mappings, and providing rsync-based deployment logic, it enables researchers to quickly move from raw TIFF outputs to interactive, multi-channel visualizations in OpenSeadragon environments like TissUUmaps.

主な機能

01Smart skip-existing logic using mtime and file size to optimize re-runs

02Predefined 22-marker color mapping with normalized name matching and fallback cycles

031 GitHub stars

04Preservation of integer label IDs in segmentation masks via nearest-neighbor resampling

05High-performance DZI tile conversion using pyvips sequential access streaming

06Automated .tmap project file generation with multi-channel composite mode support

ユースケース

01Managing high-throughput microscopy export pipelines on HPC environments like HiPerGator

02Deploying spatial biology datasets to Apache-hosted TissUUmaps servers for team review

03Converting large-scale multiplexed TIFF images into web-accessible interactive tile pyramids

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add smith6jt-cop/skills_registry tissuumaps-export-pipeline

For use in Claude.ai and ChatGPT

Download Skill