Bioimage Batch Pipeline

Overview

Convert an exploratory microscopy workflow into a robust batch pipeline that can process folders of images and save consistent outputs.

Default stack

Prefer:

• numpy
• scipy
• scikit-image
• tifffile
• pandas
• matplotlib
• napari

Batch design rules

• Treat one image as the reference case first.
• Separate pipeline logic from file iteration logic.
• Save per-file outputs in a predictable directory structure.
• Log failures without stopping the entire run unless the user asks for fail-fast behavior.

Recommended directory layout

input/
output/
  masks/
  labels/
  tables/
  qc/
  notebooks/
  logs/

Workflow

1. detect input files and group by format
2. inspect one or a few representative files
3. apply the single-image pipeline as a function
4. save outputs with consistent names
5. aggregate summary tables across files
6. write a run log and failure log

Naming rules

Prefer suffix-based outputs such as:

• {stem}_labels.tif
• {stem}_mask.tif
• {stem}_measurements.csv
• {stem}_qc.png
• {stem}_analysis.ipynb

Robustness rules

• Skip hidden files and obvious non-image files.
• Use pathlib rather than ad hoc string concatenation.
• Catch per-file exceptions and record them.
• Record package versions when practical.
• Do not overwrite outputs unless the user explicitly requests it.

Deliverables

By default, create:

• runnable batch Python script
• notebook template for representative-file debugging
• per-file outputs
• aggregate CSV summary
• run log

Resources

• references/batch-patterns.md: file iteration and logging patterns
• scripts/batch_driver.py: minimal batch driver example