Lif2Tiff: An ImageJ Macro for Automated Extraction and Conversion of LIF Files
Bibliographic record
Abstract
Lif2Tiff.ijm is an ImageJ macro designed to streamline the workflow for users handling Leica .lif files generated by Leica's LAS X software. This tool automates the extraction and conversion of all image stacks within a .lif file into .tif files, facilitating easier viewing, processing, and analysis while preserving crucial metadata such as channels and stacks. Key Features: Automated Conversion: Select a .lif file, and Lif2Tiff will automatically extract and save all contained images as .tif files in a designated folder. Channel Separation: Option to save individual channels as separate .tif files. Users can easily split channels with a simple checkbox selection. Metadata Preservation: Retains essential metadata from the original .lif files, ensuring data integrity for subsequent analyses. Intelligent Channel Naming: The macro parses image metadata to assign meaningful names to channels based on the Look-Up Tables (LUTs) used (e.g., "Channel 1 - Blue"). Users can also opt to provide custom channel names for better organization. User Settings Retention: Saves the user's most recent settings and retrieves them upon the next launch, enhancing efficiency and user experience. Integration with Bio-Formats and OME-TIFF: Utilizes the capabilities of the Bio-Formats and OME-TIFF plugins (Besson et al., 2019; Linkert et al., 2010) to ensure compatibility and robust performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.162 | 0.114 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".