More screenshots here.

• Quick start guides, as well as complete building and installation instructions, are here

• Recent pre-built continuous integration artifacts:

  • Arch Linux
  • Debian
  • MXE (i.e., Windows executables)
  • MacOS

• Recent pre-built Docker containers:

  • 
  • 
  • 
  • 

• Archive of older pre-built continuous integration artifacts

DICOMautomaton is a multipurpose tool for analyzing medical physics data with a focus on automation. It has first-class support for:

• images (2D and 3D; CT, MRI, PET, RT dose),
• surface meshes (2D surfaces embedded in 3D),
• 2D planar contours embedded in 3D,
• point clouds (3D),
• registration and warp transformations (rigid and deformable in 3D),
• radiotherapy plans, and
• line samples (i.e., discretized scalar-valued functions over $\mathbb{R}^1$).

There are five ways of operating DICOMautomaton:

• command-line interface
  • most flexible, best for automation
  • not interactive, not a repl
• a minimal graphical interface
  • best for contouring and simple evaluations
  • can be mixed with the command-line interface
• a terminal graphical interface
  • extremely simplisitic, meant for question-answer interactions
  • can be mixed with the command-line interface
• web interface
  • supports modal workflow interactions
  • not all operations are supported (e.g., contouring is not currently supported)
  • server-client model for off-site/remote installation, can provide cross-platform and low-powered client access
• a scripting interface
  • interactive and integrated editor
  • can be loaded interactively or processed non-interactively

DICOMautomaton provides a diverse array of functionality, including implementations of the following well-known algorithms and analytical techniques:

• contouring
  • interactive contouring
  • threshold-based contouring (2D or 3D)
  • contour erosion/dilation (2D or 3D)
  • sub-segmentation (i.e., splitting contour collections into 2D/3D compartments)
  • confined region-of-interest (ROI) image processing/analysis (2D or 3D)
  • 2D boolean operations
• surface reconstruction and processing
  • Marching Cubes
  • restricted Delauney reconstruction
  • contours-to-contours interpolation (i.e., keyhole surface meshing)
  • mesh subdivision
  • mesh simplification
  • 3D boolean operations, erosion, and dilation/margins (exact for small vertex counts, and via image representation for large vertex counts)
• constructive solid geometry
  • signed distance functions for isosurface extraction
  • 3D boolean operations
  • morphological operations (erosion, dilation)
• point cloud registration
  • Iterative Closest Point (ICP)
  • Procrustes algorithm
  • Affine registration
  • Principal Component Analysis (PCA) rigid registration
  • Thin Plate Spline (TPS) deformable registration
    • when correspondence is known a priori, e.g., fiducial or feature matching
  • Thin Plate Spline Robust Point Matching (TPS-RPM) deformable registration
    • for when correspondence is unknown and must also be estimated
    • includes soft-assign and simulated annealing algorithm implementations
    • extensions: double-sided outlier handling, hard constraints, and multiple solver methods for improved robustness in degenerate situations
  • warping (i.e., applying registration transformations to generic objects)
• detection of shapes within point clouds
  • Random sample concensus (RANSAC) for primitive shapes
  • MR distortion quantification for lattice grids
• rudimentary image processing techniques
  • morphological operations (e.g., open/close, erode/dilate)
  • normalization, standardization, and locally adaptive techniques (2D or 3D)
  • convolution (2D or 3D)
  • sub-image search (2D or 3D)
  • median filtering, min/max filtering, percentile transformation (2D or 3D)
  • Otsu thresholding, simplistic thresholding
  • image gradients (e.g., Sobel, Scharr; first-order, second-order)
  • edge detection, non-maximum edge suppression
• quantitative medical image analysis
  • gamma analysis, distance to agreement analysis (2D/3D to 2D or 3D)
  • Response Evaluation Criteria in Solid Tumours (RECIST) features
  • perfusion imaging quantfication: pharmacokinetic modeling for Dynamic Contrast-Enhanced (DCE) imaging (CT or MR)
  • diffusion imaging quantification: Intravoxel Incoherent Motion (IVIM) modeling and Apparent Diffusion Coefficient (ADC) estimation
  • time series analysis (i.e., '4D' analysis)
• radiobiology
  • Biologically Effective Dose (BED) transformations
  • Equivalent Dose in 2 Gy Fractions (EQD2) transformations
  • extraction of alpha/beta from EQD2 images
  • tissue recovery, including the Jones et al 2014 model
  • standard Tissue Control Probability (TCP) models
  • standard Normal Tissue Control Probability (NTCP) models
  • conformity and heterogeneity index estimation
• radiotherapy planning
  • rudimentary optimization for fixed-field plans
  • Dose Volume Histogram (DVH) extraction
  • clinical protocol evaluation
  • rudimentary automated plan checking support
  • re-treatment dose sculpting/cropping/trimming for base-planning
• routine quality assurance
  • fully nonparametric multileaf collimator (MLC) picket fence leaf displacement quantification
  • light-radiation correspondence using edge-finding
  • basic image quality measures (e.g., mean, standard deviation)
• clustering
  • Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
  • k-means
  • connected components
• geometry
  • 3D geometric primitives (vectors, lines, planes, spheres)
  • distance, closest points, and intersection point estimation
  • Hausdorff distance for point clouds
  • 'point-in-polygon' routines based on winding number and ray intersection
  • ray tracing / path sampling through voxelated geometries or surface point sampling
• numerics
  • Kahan summation routines (i.e., 'compensated' summation)
  • Chebyshev polynomial approximations
  • Wasserman's 'local linear nonparametric regression' (NPRLL) algorithms
  • numerical optimization (Nelder-Mead simplex and more modern algorithms)
  • robust weighted least squares
  • descriptive statistics (min/mean/median/max/percentiles, correlation coefficients, and simple test statistics)
• miscellaneous
  • transformations between data types (e.g., images to surface meshes, surface meshes to point clouds, point clouds to images)
  • fuzzy string matching (including Levenshtein, Jaro-Winkler, n-grams, Soundex, and Metaphone algorithms)
  • radiomic signatures (a subset of the IBSI-defined biomarkers)
  • A* pathfinding (partial)
  • R*-trees
  • 2D contouring via mapping to a 'voxel world game' (not maintained)
  • many operations support concurrent computation via threading (currently CPU only)

Notably absent but (eventually) planned features:

• radiotherapy dose calculation
• direct image registration (compared with indirect registration via feature extraction and point-cloud registration)
• SURF/SIFT/KAZE feature extraction

Interchange is important. DICOMautomaton supports the following standard file formats:

• images/dose
  • DICOM CT (read and write)
  • DICOM MR (read)
  • FITS (read and write)
  • DICOM RTDOSE (read and write)
  • 3ddose (read)
  • XIM (read)
  • CSA DICOM headers (read)
  • SNC (read and write; ASCII; partial)
  • PNG (read and write; partial)
  • JPG (read and write; partial)
  • BMP (read; partial)
  • TGA (read; partial)
• contours
  • DICOM RTSTRUCT (read and write)
  • GPX (read; partial)
• surface meshes
  • PLY (read and write; ASCII and binary; partial)
  • OBJ (read and write; partial)
  • OFF (read and write; partial)
  • STL (read and write; ASCII and binary)
• point clouds
  • PLY (read and write; ASCII and binary; partial)
  • OBJ (read and write; partial)
  • OFF (read and write; partial)
  • XYZ (read and write)
• registration
  • 16 parameter Affine or rigid transformation text files
  • Thin Plate Spline transformation text files (read and write)
• radiotherapy plans
  • DICOM RTPLAN (read)
• line samples
  • CSV (read and write)
• reporting
  • CSV (write)
  • TSV (write)
• direct database query (e.g., to interface directly with a PACs)

Customized file formats are provided for snapshotting internal state.

The basic workflow is:

1. Files are loaded (from a DB or files).

2. A list of operations are sequentially performed, mutating the data state.

3. Files of various kinds can be written or a viewer can be invoked. Both are implemented as operations that can be chained together sequentially.

Some operations are interactive. Others will run on their own (possibly for days or even weeks). See integration_tests/tests/ for specific examples.

Each operation provides a description of the parameters that can be configured. To see the exact, up-to-date documentation, invoke:

$>  dicomautomaton_dispatcher -u

and for general information invoke:

$>  dicomautomaton_dispatcher -h

Alternatively, see documentation/ for documentation snapshots.

Every build of DICOMautomaton can generate its own manual, which is provided in Markdown format. After installing, simply issue:

$>  dicomautomaton_dispatcher -u | more

See the wiki for tutorials and more information.

DICOMautomaton should NOT be used for clinical purposes. It is suitable only for research purposes or in a non-critical supporting role where outputs can be easily validated.

While efforts have been made to verify integrity and validity of the code, no independent audit or review has been performed. The breadth of functionality would make it difficult to test all combinations of operations. We therefore rely on static analysis, code quality metrics, and a limited amount of integration testing for specific workflows.

All materials herein which may be copywrited, where applicable, are. Copyright 2010-2022 Hal Clark and contributing authors.

See LICENSE.txt for details about the license. Informally, DICOMautomaton is available under a GPLv3+ license. The Imebra library is bundled for convenience and was not written by the author; consult the Imebra license file which is informally a simplified BSD-like license. ImGui and ImPlot are also bundled for convenience.

All liability is herefore disclaimed. The person(s) who use this source and/or software do so strictly under their own volition. They assume all associated liability for use and misuse, including but not limited to damages, harm, injury, and death which may result, including but not limited to that arising from unforeseen or unanticipated implementation defects.

If you use DICOMautomaton in an academic work, we ask that you please cite the most relevant publication for that work or the most relevant release DOI, if possible.

DICOMautomaton can be cited as a whole using doi:10.5281/zenodo.4088796.

Individual releases are assigned a DOI too; the latest release DOI can be found via or by clicking here.

Finally, several publications describe core functionality of DICOMautomaton and may be more appropriate to cite.

• The SFML_Viewer operation hangs on some systems after viewing a plot with Gnuplot. This stems from a known issue in Ygor.

• Building with musl may cause character conversion to fail for some DICOM files in some circumstances.

• Some operations make use of threading and create filesystem mutexes to avoid race conditions. If execution is unexpectedly terminated a mutex may remain and stall/hang future operations. This can be resolved by manually removing the mutex.

• If you are limited by an OpenGL version earlier than 3.0, for example in a VirtualBox virtual machine, the SDL viewer may fail to load. This can be worked around by switching to Mesa-based software rendering by configuring the entire system or defining the LIBGL_ALWAYS_SOFTWARE=1 environment variable.

The DICOMautomaton homepage can be found at https://www.halclark.ca/. Source code is available at https://github.com/hdclark/DICOMautomaton/ and mirrored at https://gitlab.com/hdeanclark/DICOMautomaton/.