Edward-E-S-Wang

Edward-E-S-Wang / Radiomics-Table-Reformatter-for-3D-Slicer-Radiomics-Extraction-Outputs

Public

Batch reformatting of 3D Slicer radiomics CSV files into sample-by-feature tables for machine learning.

3d-slicer 3d-slicer-extension medical-imaging radiomics radiomics-feature-extraction
27
3
100% credibility
Found Mar 09, 2026 at 15 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Jupyter Notebook
AI Summary

This utility combines multiple vertical CSV data files from radiomics extraction into a single horizontal table with one row per sample and one column per feature, ideal for machine learning and analysis.

How It Works

1
🔍 Find the data tidy-up tool

After getting lots of vertical data lists from your medical image analysis software, you discover a simple tool to turn them into neat horizontal tables.

2
📥 Grab the tool

Download the easy-to-use utility to your computer so you can start organizing.

3
📁 Gather your files

Place all your individual data files into a single folder, ready for combining.

4
⚡ Run the reshaper

Point the tool at your folder, and it quickly extracts and rearranges the data into one perfect table.

5
📊 See your new table

Open the fresh Radiomics file where each row is one case and each column is one feature.

🎉 Dive into analysis

Your data is now perfectly shaped for machine learning models or statistical reviews, saving you hours of work.

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AI-Generated Review

What is Radiomics-Table-Reformatter-for-3D-Slicer-Radiomics-Extraction-Outputs?

This Jupyter Notebook tool batch processes CSV files exported from 3D Slicer radiomics extraction, reshaping vertical feature lists from multiple files into a single sample-by-feature table—one row per sample, one column per feature—ready for machine learning. It scans a folder of radiomics outputs, extracts values from a fixed column and row range, and outputs a clean Radiomics.csv file, skipping filenames by default. Solves the hassle of manually merging batch files for stats or model training.

Why is it gaining traction?

Stands out for its laser focus on 3D Slicer outputs, automating what devs waste hours on in Excel or custom scripts—batch CSV reformatting into ML-ready tables. Handles sorting files by name and precise extraction without config hassles, like a github batch processing script for radiomics data. Hook is the zero-setup folder-drop workflow for quick iteration in feature selection pipelines.

Who should use this?

Radiomics researchers extracting features from medical images in 3D Slicer for oncology ML models. Data scientists prepping batch files for downstream analysis or model development. Teams doing statistical validation on radiomics CSV outputs who hate vertical-to-horizontal pivots.

Verdict

With 12 stars and 1.0% credibility score, it's early-stage and niche—solid README docs but no tests or broad adoption yet. Grab it if your 3D Slicer workflow matches exactly; otherwise, fork and extend for production use.

(178 words)

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