Viewers VS Phantom

Compare Viewers vs Phantom and see what are their differences.

Viewers

OHIF zero-footprint DICOM viewer and oncology specific Lesion Tracker, plus shared extension packages (by OHIF)
nci-itcr nci-qin quantitative-imaging imaging-informatics cancer-imaging-research Dicom image-analysis medical-image-processing medical-imaging ReactJS JavaScript HacktoberFest dicom-viewer Medical
Source Code
docs.ohif.org
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Phantom

Effective DICOM anonymization in Python (by cyrilzakka)
Anonymization Dicom medical-image-processing medical-imaging pydicom
Source Code
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Viewers Phantom
2 2
2,909 3
1.6% -
9.7 10.0
6 days ago over 1 year ago
TypeScript Python
MIT License -
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

Viewers

Posts with mentions or reviews of Viewers. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-04-21.
  • Show HN: Volume rendering 3D data in Three.js and GLSL
    5 projects | news.ycombinator.com | 21 Apr 2024
    My app does not support DICOM files as input. Just Uint8 256x256x256 raw files that are scaled 1x1x1. Maybe if I had the chance to work on it full-time I'd have the time to add those features, but it's just a side project for now.

    Have you looked into Slicer3D[0] which is a multi-platform desktop app or Open Health Image Foundations dicom viewer[1] which is web-based? Perhaps one of these will help.

    [0] https://www.slicer.org/

    [1] https://github.com/OHIF/Viewers

  • [P] VinDr Lab - an open-source annotation platform for Medical AI
    2 projects | /r/MachineLearning | 2 Apr 2021
    On a side note, I noticed that a significant portion of this viewer is based on work done by folks at OHIF. Because of the incredible amount of work that went into creating those tools, it is worth citing them in the VinDR arxiv paper:

Phantom

Posts with mentions or reviews of Phantom. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing Viewers and Phantom you can also consider the following projects:

cornerstone - JavaScript library to display interactive medical images including but not limited to DICOM

rt-utils - A minimal Python library to facilitate the creation and manipulation of DICOM RTStructs.

vindr-lab - A Data Platform for Medical AI that enables building high-quality datasets and algorithms with lean process and advanced annotation features.

mammography_metarepository - Meta-repository of screening mammography classifiers

cornerstone3D - Cornerstone is a set of JavaScript libraries that can be used to build web-based medical imaging applications. It provides a framework to build radiology applications such as the OHIF Viewer.

torchio - Medical imaging toolkit for deep learning

dwv - DICOM Web Viewer: open source zero footprint medical image library.

raiven - A framework for the translation of AI tools to the radiology environment

dicomviewer - DICOM Viewer in Nextcloud

medicaldetectiontoolkit - The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.

gonii - Standalone NIfTI file parser

image-size - Node module for detecting image dimensions

Viewers vs cornerstone Phantom vs rt-utils Viewers vs vindr-lab Phantom vs mammography_metarepository Viewers vs cornerstone3D Phantom vs torchio Viewers vs dwv Phantom vs raiven Viewers vs dicomviewer Phantom vs medicaldetectiontoolkit Viewers vs gonii Viewers vs image-size