# SimpleITK/SimpleITK

SimpleITK: a layer built on top of the Insight Toolkit (ITK), intended to simplify and facilitate ITK's use in rapid prototyping, education and interpreted languages.

Repository: https://github.com/SimpleITK/SimpleITK
Canonical: https://ross.abutalabs.com/products/simpleitk
Homepage: http://www.simpleitk.org
Language: C++
License: Apache-2.0
License Family: permissive
Topics: simpleitk, c-plus-plus, swig, registration, python, image-analysis, segmentation, image-processing, itk, r, csharp, java, ruby, tcl, lua
Last push: 2026-09-02T14:26:47+00:00

## Health v2 (maintenance only)
Score: 98/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 100, release rhythm 95, longevity 100
- inputs: {"age_days": 5775, "days_push": 0, "days_rel": 34, "gap_med": 0, "n_releases_24m": 20}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1084, forks 234 (observed 2026-09-03T02:15:08.149679+00:00)

## What it is
SimpleITK is a simplified C++ interface to the Insight Toolkit (ITK) for multi-dimensional image analysis, including filtering, segmentation, and registration, with bindings for Python, R, Java, C#, Lua, Ruby, and TCL via SWIG. It supports 2D/3D/4D spatial images, 20+ file formats (DICOM, TIFF, PNG, etc.), and over 280 analysis filters.

## Use cases
- segment medical images from CT or MRI scans
- register 2D and 3D images with rigid or deformable transforms
- read and convert DICOM files in Python
- evaluate segmentation quality with Dice and Hausdorff metrics
- threshold and watershed segment microscopy images
- build image analysis pipelines in R or Java

## When to choose
- you need robust medical/scientific image segmentation or registration without deep C++ expertise
- you want ITK's algorithms from Python, R, Java, or C#
- you need DICOM and other medical format I/O with physical-space image handling

## When to avoid
- you only need simple pixel-array processing where scikit-image or OpenCV suffice
- you need deep learning based segmentation models rather than classical algorithms
- you require ITK's full n-dimensional (>4D) image support or fine-grained pipeline control

## Facets
- artifact type: library
- maturity: stable
- function: image-processing, computer-vision
- domain: image-processing, healthcare, bioinformatics, computer-vision
- platform: cross-platform, python, cpp, jvm, dotnet, ruby, lua
- tags: medical-imaging, segmentation, registration, dicom, itk, swig-bindings, scientific-computing

## Member repositories
- SimpleITK/SimpleITK (main) score 98

## Provenance
- Observed fields: from GitHub, fetched 2026-09-03T02:15:08.149679+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:51:13.842449+00:00, confidence not recorded.
  - readme: https://github.com/SimpleITK/SimpleITK (fetched 2026-09-03T02:15:08.149679+00:00, sha 2ca1f9d3a909)
  - homepage: http://www.simpleitk.org (fetched 2026-08-29T12:53:15.998805+00:00, sha 138bd574a72c)
  - site_page: https://simpleitk.org/about.html (fetched 2026-08-29T12:53:16.001478+00:00, sha 7d37eaea9f1a)
- Data as of 2026-08-30T08:39:29.467469+00:00.