ikeda042/abysseye

★ 17TypeScriptAudience · researcherComplexity · 4/5Setup · hard

Mindmap

mindmap
  root((AbyssEye))
    What It Does
      Image organization
      AI inference
      Result review
      Training export
    Tech Stack
      FastAPI
      React
      SQLite
      Docker
    Inputs
      TIFF images
      Trained models
    Use Cases
      Cell detection
      Data labeling
      Model retraining

mindmap root((AbyssEye)) What It Does Image organization AI inference Result review Training export Tech Stack FastAPI React SQLite Docker Inputs TIFF images Trained models Use Cases Cell detection Data labeling Model retraining

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Things people build with this

USE CASE 1

Organize deep-sea microscopy TIFF images into projects and run an AI model to classify candidate microbial cells

USE CASE 2

Review AI predictions one-by-one using the DeepScan interface and manually correct misclassified cells

USE CASE 3

Export corrected, labeled microscopy data to retrain and improve your cell-detection model in the next round

Tech stack

TypeScriptPythonFastAPIReactSQLiteDocker

Getting it running

Difficulty · hard Time to first run · 1h+

Requires Python 3.11, Node.js 20+, and your own trained AI models and TIFF images, no pretrained models or sample data are included.

License terms are pending and not yet finalized, terms of use are unspecified.

In plain English

AbyssEye is a browser-based application for scientists who work with microscopy images. It was developed with JAMSTEC (Japan's oceanography research agency) as part of a study on detecting microbial cells in deep seafloor sediment samples, and it is built around the workflow described in that 2025 Scientific Reports paper. The core problem it addresses is that managing microscopy images, running AI-based analysis on them, reviewing the results, and preparing data for further model training are steps that normally happen across separate tools. AbyssEye puts all of them in one operator-facing interface. The workflow moves through several stages. You start by organizing your microscopy image files (TIFF format) into projects. The app extracts regions of interest from those images, meaning it identifies candidate areas worth examining, and stores them in a local database. You then run inference, which means asking a trained AI model to classify each candidate. After that, a review step called DeepScan lets you go through the AI's results, correct any mistakes manually, and label the data properly. The corrected data can then be exported to train an improved model, which feeds back into the next round of analysis. The application has a Python backend (using FastAPI) and a React-based frontend. It stores data locally in SQLite databases rather than sending anything to external servers. Trained AI models, image files, and databases all stay on the machine running the app. Docker deployment files are included for teams who want a more controlled setup. The repository does not include any pretrained models or sample images. You supply your own TIFF files and your own models through the app's model manager. The software license is listed as pending, so the terms are not yet finalized. The app requires Python 3.11 and Node.js 20 or later. A quick-start section in the README walks through starting the backend and frontend locally.

Copy-paste prompts

Prompt 1

I have TIFF microscopy images of seafloor sediment. Using AbyssEye, walk me through creating a project, loading images, and running inference with my pre-trained model

Prompt 2

Show me how to use AbyssEye's DeepScan interface to step through AI predictions, correct wrong labels, and export the cleaned dataset for retraining

Prompt 3

Set up AbyssEye locally: start the FastAPI backend and React frontend, then load a TIFF file to confirm the region-of-interest extraction is working

Prompt 4

How does AbyssEye store regions of interest and inference results in its SQLite database? Show me the schema and a query to list all reviewed cells

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