Digital Imaging and Communications in Medicine (DICOM) viewers are a global phenomenon as medical experts increasingly rely on these image-viewing solutions to analyze complex sequences such as CT scans, X-rays, MRIs, etc.  DICOM viewers commonly integrate with the Picture Archiving and Communication System (PACS) to store, manage, share, and retrieve medical images. With PACS, DICOM viewers allow healthcare professionals to easily manipulate complex medical data for better diagnosis and patient care. Due to their increasing popularity, the projected compound annual growth rate (CAGR) for the Global Medical Imaging and Radiology software market stands at 7.8% from 2023 to 2030. This article discusses the top players in the market to help you find the best DICOM viewer for the job. How to Choose a DICOM Viewer? DICOM is an international standard introduced in 1993 that defines the format for storing and exchanging medical images. Using artificial intelligence (AI) in medical imaging to diagnose diseases is becoming the norm. This has led to many DICOM solutions with sophisticated annotation features and user interface (UI) enhancements entering the market. As such, the modern medical ecosystem needs a DICOM viewer to handle complex clinical trials requiring detailed analysis and collaboration among medical professionals. However, choosing an appropriate viewer is challenging due to the number of DICOM tools available. So, the list below narrows down the factors you should consider when purchasing a DICOM solution. Compatibility with Operating Systems: You should opt for a DICOM viewer compatible with your desired operating system. However, getting a viewer that works with Mac OS, Linux, and Windows is more practical, as solutions that work on a single platform can reduce collaboration efficiency across team members with different operating systems (or development environments). Ease of Setup: A DICOM viewer with a straightforward installation can be a significant time-saver. Consider a tool that could help your team get started quickly without additional configurations or dependencies. Patient Data Anonymization: With strict patient data regulations, a viewer must provide features allowing users to abstract sensitive information (PII) quickly to prevent data violations.  Intuitive User Interface (UI): An easy-to-use UI simplifies a doctor’s job. Look for features that allow quick navigation and analysis of medical images, such as clear markers, scrolling capabilities, adjustable brightness, multiplanar reconstruction (MPR), and Maximum Intensity Projection (MIP). Select a DICOM viewer with your desired UI specifications for a better user experience. Reporting: A DICOM viewer capable of handling image fusion can facilitate more comprehensive and detailed reporting, allowing healthcare professionals to annotate and report findings directly on the fused images. Image fusion technology allows the combination of images from different imaging modalities, such as Positron Emission Tomography (PET), Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). PACS Integration: Picture Archiving and Communication System (PACS) is the foundational software that helps with information exchange and storage between different medical devices. PACS can be local or cloud-based with improved accessibility. A DICOM viewer that can integrate quickly with a cloud-based PACS is a more appropriate option. Data Security Assurance and Compliance Certifications: The importance of privacy and security certifications like SOC2 cannot be overstated. It ensures that the solution maintains the highest data protection standards, vital in the healthcare sector. Cost-Effectiveness: Invest in a solution that balances price with the desired features at a reasonable cost for a faster return on investment (ROI). Want to know what features to look for in a DICOM annotation tool? Read our article on the Top 7 features to look for in a DICOM Annotation Tool.   Top 15 DICOM Viewers for Medical Images As evident from the previous section, there are many factors to consider when investing in a DICOM viewer. This can be overwhelming, as choosing a suitable viewer becomes tedious. So, the following lists the best DICOM viewers selected based on functionality, ease of use, and cost to simplify your search. Encord DICOM Label Editor 3DimViewer PostDICOM RadiAnt Horos 3D Slicer Mango Escape EMV Ginkgo CADx DICOM Web Viewer Miele LXIV Philips DICOM Viewer Weasis MiViewer Yakami DICOM  Encord DICOM Label Editor Encord is an end-to-end data and AI platform with advanced features for classifying and annotating medical images to build AI models. It provides native support for Digital Imaging and Communications in Medicine (DICOM) browser rendering and data annotations. Encord DICOM Annotation Tool Features Compatibility with Operating Systems: Encord Annotate provides native support for DICOM within a web app compatible with all operating systems Ease of Setup: Setting up Encord is an easy 4-step process. Patient Data Anonymization: The de-identification function lets you protect sensitive patient data. Intuitive User Interface (UI): It has a user-friendly interface to render 20,000-pixel intensities, set custom window widths & levels, and natively display DICOM metadata. Reporting and PACS Integration: With Encord’s DICOM editor, you can annotate modalities from your Mammography, CT, X-ray, and MRI PACS viewer. Data Security Assurance and Compliance Certifications: The platform is SOC2 compliant and conforms to FDA regulations. Key Benefits Annotation Type: Label using any annotation type in 2D (with 3D in the works) and seamlessly toggle between axial, coronal, and sagittal views. Encord also supports several annotation techniques, such as polygons, hanging protocols, segmentation, etc. Full-range intensity values: Encord’s DICOM viewer natively renders files in the browser, supporting displays of up to 20,000 pixels. 2D Multiplanar Reconstruction (MPR): Encord lets you reconstruct images in 2D orthogonal planes through its MPR display for efficient annotation and visualization. Best for Healthcare professionals who want an end-to-end AI-based imaging tool. Pricing Encord has a simple pay-per-user pricing system. Learn more about the DICOM & NIfTI Annotation features Learn how to improve Machine Learning experiments with medical images by reading our Guide to Experiments for Medical Imaging in Machine Learning.   3DimViewer 3DimViewer is an open-sourceDICOM viewing multi-platform software that supports MicrosoftWindows, Mac OS X, and Linux. 3DIMViewer Features 3DimViewer is compatible with Linux, Mac OS, and Windows Installation involves downloading the application from the website. It features data anonymization. 3Dim’s user interface is a bit difficult to understand. Key Benefits 3DimViewer provides three-dimensional (3D) visualizations for DICOM images. Features volume rendering through shaders running on NVIDIA and AMD graphic cards. It offers adjustable-density windows to change brightness and contrast for viewing particular areas in medical scans more clearly. Best for Teams looking for a budget-friendly, lightweight, and versatile DICOM viewer. Pricing 3DIMViewer is a free DICOM viewer. PostDICOM PostDICOM is a cloud-based PACS with an integrated DICOM viewer that lets you view, save, and share DICOM files using PostDICOM’s cloud servers. PostDICOM Features PostDICOM is compatible with Linux, Windows, and Mac OS. PostDICOM can be a bit challenging to set up. It has a data anonymization feature. PostDICOM has a user-friendly interface. The solution is integratable with PACS. Key Benefits PostDICOM features an online DICOM viewer with advanced visualizations like MPR, MIP, and 3D rendering. It lets you view DICOM image files on any device. It helps you share data using a single link and password. Best for Teams that are looking for an end-to-end storage and viewing solution for large DICOM datasets. Pricing PostDICOM offers a Lite, Pro, and Advanced version. RadiAnt RadiAnt is a Windows-only local PACSDICOM viewer that supports dynamic sequences and monochromatic, color, and static images. RadiAnt User Interface Features RadiAnt is only compatible with Windows. Installation and setup require downloading the application and installing using the installing wizard. The user interface can be difficult to understand. It integrates with PACS. Key Benefits The RadiAnt Dicom Viewer features multiple user-friendly tools like fluid zooming and panning, a pen for freehand drawing, preset window settings, etc. It supports PET-CT image fusion and fusion with other modalities. It has multi-touch support for touch-enabled devices. Best for Teams that want a DICOM viewer with an intuitive UI for studying complex images for research purposes. Pricing RadiAnt has a subscription-based pricing mechanism. Horos Horos is an open-source DICOM viewer based on OsiriX, an image-processing library for the Apple Mac OS. Horos Features Horos is only compatible with Mac OS. The solution requires a 5-step setup procedure It integrates with PACS Key Benefits Horos lets you share studies with multiple recipients from within the platform. It integrates with the cloud for efficient storage and file transfer. Best for Teams that want a Mac-OS compatible viewer that can handle large image data. Pricing The tool is free to use. 3D Slicer 3D Slicer is an open-source DICOM software that lets you visualize, process, segment, and analyze 3D medical images. It also supports virtual and augmented reality (VR and AR). 3DSlicer Features 3D Slicer is compatible with Windows, Mac OS, and Linux. Installation is a bit challenging. It features data anonymization. The user interface is a bit complex. It supports PACS. Key Benefits 3D Slicer supports 2D, 3D, and 4D (VR/AR Supported) DICOM objects. It features over 150 plugins and extensions. The viewer is compatible with the latest AR/VR devices, like Oculus and HoloLens. Best for Medical scientists who want a real-time tool for navigating surgical workflows.  Pricing The tool is open-source. Mango Multi-image Analysis GUI (Mango) is a lightweight open-source DICOM solution compatible with Windows, Mac OS, and Linux and offers plugins for Java and Python development. Mango Features Mango is compatible with Windows, Linux, and Mac OS. The user interface is complex. Key Benefits Mango supports multiple image formats, including DICOM, MINC, and NIFTI. It offers customizations for file formats, atlases, color tables, etc. It supports cut planes and overlays for interactive surface modeling. Best for Doctors who want to view and analyze DICOM and other image formats on the go. Pricing The software is free to use. Escape EMV Escape EMV is a Windows-based DICOM viewer with multilingual functionality and integrates with PACS servers to exchange DICOM files. Escape EMV Features Escape EMV is compatible with Windows and MacOS. The user interface is complex. Supports PACS. Key Benefits Escape EMV features a multilingual interface supporting English, French, Italian, Spanish, and Portuguese. It has a full-screen viewing mode. It lets you query servers to fetch your desired files quickly. Best for Teams that need a tool for collaborating across borders.   Pricing Pricing is based on usage. Ginkgo CADx Ginkgo CADx is a multi-platform DICOM image viewer that can convert standard image files into the DICOM format. Ginkgo CADx Features Ginkgo CADx is compatible with Windows, Linux, and MacOSX. It has an easy-to-use interface Key Benefits Ginkgo CADx can convert PNG, JPEG, BMP, and TIFF formats to DICOM. It is compatible with Windows, Mac OS, and Linux. It supports Kitware’s Insight Segmentation and Registration Toolkit. Best for Healthcare professionals who frequently work with multiple image formats and want an easy-to-use DICOM viewer and converter. Pricing The tool is open-source. DICOM Web Viewer DICOM Web Viewer is a free online DICOM viewer based on Javascript and HTML 5, making it compatible with any platform that supports a modern browser. DICOM Web Viewer Features DICOM Web Viewer is compatible with all operating systems. It is easy to set up. Key Benefits DICOM Web Viewer can load data from local and remote servers. It has features like drag, contrast, and zoom to manipulate DICOM images. Best for Professionals who need a versatile viewer with basic functionality. Pricing The solution is free to use. Miele-LXIV Miele-LXIV is a 64-bit DICOM viewing application for Mac OS, compatible with versions 10.14 to 14.2. Miele-LXIV Features Miele-LXIV is only compatible with MAC OS Key Benefits Miele-LXIV supports MacOS Mojave’s Dark Mode. It supports seven languages: Chinese, English, French, German, Italian, Japanese, and Spanish. Its universal binary makes it run on Intel and Apple silicon processors efficiently. Best for Health professionals who work primarily on Mac OS to view DICOM images. Pricing It has a fixed price. Philips DICOM Viewer The Philips DICOM Viewer is a basic read-only application, which means you cannot save changes to an image. It only lets you export and view DICOM files. Philips DICOM Viewer Features The software is only compatible with Windows. Key Benefits The software features a series selector and viewer. It supports multiple image viewing types simultaneously for multimodal studies. Best for Physicians and technicians who want a tool for quickly viewing and printing DICOM images. Pricing Pricing is not publicly disclosed Weasis Weasis is a web-based DICOM solution with a modular architecture and integrations with PACS, Hospital Information Systems (HIS), Patient Health Records (PHR), and Radiology Information Systems (RIS). Weasis Features Weasis is compatible with Windows, Mac OS, and Linux. Installation requires direct download from the website. Supports data anonymization. It has a user-friendly interface. It integrates with PACS. Key Benefits Weasis lets you view multiple DICOM types, including MPEG-2, ECG, RT, etc. It offers several layouts to compare studies. It features tools for measuring and annotating images. Best for Healthcare practitioners who want an advanced solution for analyzing multiple clinical trials cheaply. Pricing Weasis is free to use. MiViewer MILLENSYS Dicom Viewer (MiViewer) is a general-purpose DICOM tool requiring no installation, making it easier to operate and view multimodality images. MiViewer Features MiViewer is easy to set up User interface is challenging to operate. Key Benefits The software lets you view cine loops with playback controls, special filters, and multi-frame images. It offers several tools to manipulate images, such as multi-format display, windowing, annotation, etc. It lets you export DICOM images as compressed JPEG files. Best for Practitioners who are new to the medical profession and want easy-to-use software. Pricing It is not publicly disclosed Yakami DICOM Yakami DICOM is a collection of free applications, such as a DICOM viewer, converter, table maker, PACS client, file mover, etc., to manage DICOM data. It is compatible with Windows versions 2000 to 10. Yakami DICOM Features Yakami is only compatible with Windows. It is difficult to set up as you have to configure multiple applications. Supports data anonymization. The user interface is challenging to operate. The software comes with a PACS client. Key Benefits The platform lets you magnify images by 1600% and supports window level and width adjustments for better clarity. The DICOM viewer supports multiple image formats, including JPEG, PNG, TIFF, W3C, etc. The Table Maker package automatically creates tables by reading headers in DICOM files. Best for Researchers who want free DICOM management tools to conduct independent studies. Pricing Yakami DICOM is free to use. DICOM Viewers for Medical Images: Key Takeaways After exploring various DICOM viewers, it's evident that there's a range of options with diverse functionalities for viewing, storing, and sharing medical images. Here are some essential takeaways to guide your choice: Functionality vs. Cost: While open-source DICOM viewers are an attractive option due to their low cost, paid solutions often offer enhanced customer support, regular software updates, and ongoing development to meet evolving medical imaging needs. However, some open-source tools also provide substantial functionality and active community support. Advanced visualization features: Essential features for a DICOM viewer include custom window presets for different imaging modalities, Multiplanar Reconstruction (MPR), Maximum Intensity Projection (MIP), and efficient tools for zooming, panning, marking, and annotating images. These capabilities are crucial for detailed medical image analysis. Support for PACS: DICOM viewer integration with Picture Archiving and Communication Systems (PACS) is vital in today's interconnected medical landscape. Such integration facilitates streamlined storage and exchange of extensive DICOM datasets, enhancing overall workflow efficiency and data management. In conclusion, when choosing a DICOM viewer, balance the considerations of cost and functionality with your specific needs for visualization features and system integration to find the most suitable solution for your medical imaging requirements.

The FDA has approved over 300 AI algorithms over the last 4 years – the vast majority of which relate to medical imaging.  With the increase in medical AI and computer vision applications, healthcare teams are turning to AI models for more accurate and faster diagnosis at scale.  A correct or incorrect diagnosis impacts treatment, care plans, and outcomes. And ultimately, computer vision and machine learning applications across medical AI have the potential to materially impact the chances of a positive outcome.  And as we know, it all starts with data. Getting a radiology AI product to market – not to mention through FDA or CE clearance – starts with data quality and speed, which in turn relies heavily on accurate annotation and labels, whether the images come from CT, X-ray, PET, ultrasound, or MRI scans.  To help you navigate all the DICOM labeling tools and frameworks on the market, we have compiled a list of the most popular annotation tools for annotating DICOM and NIfTI files. 💡Read more: Our Encord DICOM June product updates are out!   Whether you are: A data science team at a fast-growing radiology AI startup trying to bring your first products to market or obtain FDA approval A data operations team at a large healthcare organization evaluating medical imaging tools to help your team analyze CT scans and MRI scans  ...or a computer vision team at a healthcare provider or vendor delivering high-value machine learning-based solutions for hospitals, doctors, and other medical professionals.  This guide will help you compare the top tools to annotate DICOM and NIfTI files and help you find the right one for you. We will compare them across a few key features – collaboration, quality control (QC) and quality assurance (QA), and ease of use for annotators and medical data operations managers. If you’re evaluating NIfTI labeling tools, you can find more about the key features you need to look out for here. So let’s get into it! In this post, we’ll cover six of the most popular AI-based medical image annotation tools: Encord DICOM 3D Slicer Labelbox Kili ITK-Snap MONAI Review of 6 Best Medical AI Annotation Tools for DICOM Encord DICOM Encord is the leading DICOM annotation platform trusted by leading medical AI teams at healthcare institutions.  Encord’s AI-based annotation tool was purpose-built in close collaboration with healthcare teams for machine learning and computer vision projects in the medical profession. Encord and Encord Active are designed to handle vast medical image and video-based datasets (e.g. surgical video), alongside DICOM, NIfTI and +25 other data formats. Benefits & Key features: Native DICOM rendering: Render 20,000+ pixel intensities natively in the browser with a PACS-style interface. 3D views: Multiplanar reconstruction (axial, coronal, and sagittal views) and maximum intensity projection (MIP). Windowing support: Preset window settings for numerous modalities and the most common objects that need detecting, identifying, and annotating (e.g., lung, bone, heart, brain, etc.). Hanging protocols support: For Mammography, CT and MRI. Expert review workflows: Collaborative workflows designed for medical teams and scalable data operations. Foundation models support: Generate mask predictions with our AI-based auto-segment tool. Configurable labeling protocols: Create complex medical labels and protocols to train your annotation team with our medical-grade annotation tool.  Support for multiple annotation types: Bounding boxes, polygons, segmentation, polylines, keypoints, object primitives, and classification.  Best for: Teams rolling out new healthcare AI models, computer vision DataOps teams, annotation providers, ML engineers, and data scientists in medical organizations. Pricing: Free trial model and simple per-user pricing after that. 💡 More insights on labeling DICOM with Encord: Here are some examples of healthcare and medical imaging projects that Encord has been used for: Floy, a radiology AI company that helps radiologists detect critical incidental findings in medical images, reduces CT & MRI annotation time with AI-assisted labeling. RapidAI reduced MRI and CT annotation time by 70% using Encord for AI-assisted labeling.  Stanford Medicine cut experiment duration time from 21 to 4 days while processing 3x the number of images in 1 platform rather than 3   Further reading: Best Practice for Annotating DICOM and NIfTI Files The 7 Features to Look Out For When Choosing a DICOM Annotation Tool 3D Slicer 3D Slicer is an open-source software application designed for medical image processing and visualization. It provides a platform for 3D image segmentation and registration. The US The National Institutes of Health (NIH) and other healthcare partners have played an important role in funding 3D Slicer, alongside Harvard Medical School, and dozens of other public and private funding sources.  There have been numerous contributors to 3D Slicer, with an active community improving the source code, architecture, building modules, securing funding, and citing 3D slicer in medical computer vision and machine learning model training experiments and development.   Benefits & Key features: Easy (& free) to get started labeling DICOM files. Great for manual data annotation — also supports semi-assisted labeling. Robust ground-level annotation capabilities (including classification and object detection) for a broad set of computer vision use cases. Best for: Students, researchers, and academics testing the waters with DICOM annotation (perhaps with a few files or a small open-source medical imaging dataset). Pricing: Free! 💡 More insights on image labeling with 3D Slicer: If your team is looking for a free annotation tool, you should know… 3D Slicer is one of the most popular open-source tools in the space, with over 1.2 million downloads since it was launched in 2011. Other popular free image annotation alternatives to 3D Slicer are CVAT, ITK-Snap, MITK Workbench, HOROS, OsiriX, MONAI and OHIF Viewer. If data security is a requirement for your annotation project… Commercial labeling tools will most likely be a better fit — as key security features like audit trails, encryption, SSO, and generally-required vendor certifications (like SOC2, HIPAA, FDA, and GDPR) are not available in open-source tools. Further reading: Buy vs build for computer vision annotation - what's better? Overview of open-source annotation tools for computer vision Labelbox Labelbox is a US-based data annotation platform founded in 2018, after the founders experienced the difficulties associated with building in-house ML operations tools. Like most of the other platforms mentioned in this guide, Labelbox offers both an image labeling platform, as well as labeling services.  Teams can annotate a wide range of data types (PDF, audio, images, videos, and more) using the Labelbox data engine that can be configured for numerous ML, AI, and computer vision use cases.  Benefits & Key features: Support for two annotation types – polyline and segmentation – and common imaging modalities – CT, MRI, and ultrasound.  SaaS or on-premise workflows with privacy and security built-into the platform.  Catalog view to help medical annotation teams label and sift and find patterns within vast multi-format datasets.  Best for: Teams wanting to annotate other file formats alongside DICOM, like documents, video, text, audio, and PDFs. Pricing: 10,000 free LBUs to begin with, and custom pricing beyond that. 💡 More insights on labeling DICOM with Labelbox: If your team is looking for on-demand labeling services, you should know… Labelbox can connect your in-house team with outsourcing partners for large ML annotation projects.  If data security is a requirement for your annotation project… Labelbox comes with enterprise-grade security as standard for healthcare and AI teams.  Further reading: Top 10 Free Healthcare Datasets for Computer Vision 3 ECG Annotation Tools for Machine Learning Kili Kili is a data annotation platform founded in 2018 by a French team who had previously built the AI company, MyElefant, and an AI lab from scratch for BNP Paribas. The platform allows users to create and manage annotation projects, monitor progress, and collaborate with team members in real time. Kili has been used by businesses across various industries, including healthcare, finance, and retail, to accelerate their AI development. Benefits & Key features: Support for multiple annotation types, including text, image, video, and audio.  A platform designed to label, find, and fix data annotation issues and simplify DataOps for AI teams of every size. For small-scale projects, DataOps can implement Kili with 5 lines of code to turn a machine learning workflow into a data-centric AI workflow.  Best for: ML and DataOps teams across a range of sectors, either with in-house or outsourced teams.  Pricing: Free tier for individuals, alongside corporate and enterprise plans for businesses.  💡 More insights on labeling DICOM with Kili: If your team is looking for an easy-to-integrate ML tool, you should know… Kili was designed to embed into ML workflows easily – it doesn’t have as many features as some computer vision SaaS products, but it integrates rapidly in a wide range of data tech stacks. Further reading: How to Annotate DICOM and NIfTI Files  Medical Image Segmentation: A Complete Guide  ITK-Snap ITK-Snap is a free, open-source, multi-platform software application used for image segmentation. ITK-Snap provides semi-automatic segmentation using active contour methods as well as manual delineation and image navigation.  ITK-Snap was originally developed by a team of students at the University of North Carolina led by Guido Gerig (NYU Tanden School of Engineering) in 2004. Since then, it’s evolved considerably, now being overseen by Paul Yushkevich, Jilei Hao, Alison Pouch, Sadhana Ravikumar and other researchers at the Penn Image Computing and Science Laboratory (PICSL) at the University of Pennsylvania. The latest version, ITK-Snap 4.0, was released in 2020, funded by a grant from the Chan-Zuckerberg Initiative.  Benefits & Key features: Manual segmentation in three planes. Support for additional 3D and 4D image formats alongside DICOM, like NIfTI. A 3D cut-plane tool for faster processing of image segmentation results and multiple images, including an advanced distributed segmentation service (DSS).   Best for: Medical image annotation, students, and research teams. Pricing: Free! Further reading: 9 Best Image Annotation Tools for Computer Vision [2024 Review] The Top 6 Artificial Intelligence Healthcare Trends of 2024 MONAI MONAI is an open-source, PyTorch-based framework designed for deep learning in medical imaging. The project was started in 2019 by NVIDIA, the National Institutes of Health (NIH), and other contributors. The framework provides various tools, including a labeling tool, to assist in the creation of annotated datasets for training deep learning models.  MONAI’s labeling tool allows users to annotate images with 2D or 3D bounding boxes, segmentation masks, and points. The annotations can be saved in a variety of formats and easily integrated into the MONAI pipeline for training and evaluation. MONAI has gained popularity due to its ease of use and its ability to accelerate research in medical imaging. Benefits & Key features: Easy (& free) to get started labeling biomedical and healthcare images with the MONAI Label Server. Capabilities for training AI models for healthcare imaging across a range of modalities and medical specialisms with two transformer-based architectures. Convenient integrations through the MONAI Deploy App SDK.  Best for: Medical imaging, annotation, and research teams that need an open-source healthcare AI platform.  Pricing: Free! 💡 More insights on labeling DICOM with MONAI: If your team is looking for an open-source alternative to commercial tools, you should know… MONAI is designed as an AI-based collaborative platform with a suite of features you can host and deploy in a wide range of medical environments.   If data security is a requirement for your annotation project… MONAI is better equipped than most open-source medical imaging projects with layers of enterprise-grade security.  Further reading: 7 Ways to Improve Medical Imaging Dataset Guide to Experiments for Medical Imaging in Machine Learning DICOM Annotation Tools: Key Takeaways  There you have it! The 6 most popular annotation tools for annotating DICOM.  For further reading, you might also want to check out a few honorable mentions, both paid and free annotation tools: Hive: Cloud-based AI tools for organizations that need to apply labels across a wide range of data types Dataloop: Software to train and improve ML and AI models with extensive annotation capabilities Appen: One of the oldest labeling services platforms on the market, launched in 1996 VOTT: An open-source tool with tags and asset export features compatible with Tensorflow and the YOLO format.  Ready to improve the accuracy, outputs, and speed to get your healthcare AI models production-ready with DICOM annotations?  Sign-up for an Encord Free Trial: The Active Learning Platform for Computer Vision, used by the world’s leading computer vision teams.  AI-assisted labeling, model training & diagnostics, find & fix dataset errors and biases, all in one collaborative active learning platform, to get to production AI faster. Try Encord for Free Today.  Want to stay updated? Follow us on Twitter and LinkedIn for more content on computer vision, training data, and active learning. Join our Discord channel to chat and connect.

Viewing and annotating medical data, images, and videos is a crucial, and frequent, task for many practitioners in the healthcare industry.  A starting point for many when evaluating how to go about this task, will be to start with open-source medical imaging annotation tools – these tools are a popular choice in the medical sector and can be a smart way to save money when getting started on an image or video dataset annotation project.  In this article, we will cover the handful of key data annotation tools that our team often discusses with leaders from Data Operations and Machine Learning teams (as well as radiologists, clinicians, and the broader annotation community) as they are getting started in their ‘data annotation’ journey.  We will mainly cover a handful of tools designed to solve specific DICOM annotation pain points and problems, such as MITK Workbench, ITK-Snap, 3D Slicer, and several others, rather than broader-ranging computer vision annotation tools.  As we all know, there are pros and cons to using open-source tools for DICOM annotation projects. When conducting your own evaluation, it’s worth comparing what is on the market with what your own requirements are – based on your specific use cases and forward-looking plans. In this article, we will cover several of the most popular open-source tools for DICOM annotation, including the key use cases, benefits, and downsides of these tools – we will also look ahead to what’s next after getting started with these tools, and considerations that teams make as they go forward in their data annotation journey.  What are Open-Source Annotation Tools?  Open-source annotation tools are software programs whose source code is freely available for anyone to use. When we think of annotation platforms, what we mean by open-source annotation tools are tools that help teams with the broad annotation and labeling process (including use cases like image classification, image segmentation, data labeling, and object detection). They will be aimed at supporting almost any image or video annotation purpose (unless the license specifically prohibits a certain type of use). Open-source tools are usually built collaboratively, with numerous — sometimes hundreds or thousands — of developers contributing to the source code. Tools are often tested using publicly available medical imaging datasets, and are usually financially supported by a charitable foundation, public/users donations, or one or more tech company sponsors.  What are The Advantages of Using Open-Source Annotation Tools for Medical Imaging? The key advantages of open-source annotation tools for medical imaging are:  They are free to use They are available for commercial use and can be built upon and customized They typically support community and academic use cases alike In most cases, they support an array of medical image file formats (including DICOM and NIfTI medical image file formats).  Now let’s take a closer look at some of the most popular open-source annotation tools on the market.  What are Some of the Most Popular Medical Imaging Open-Source Annotation Tools?  A wide range of open-source tools support, and were specifically created to, manage annotation projects for medical image datasets. In this article, we will focus on several of the most popular, including MITK Workbench, ITK-Snap, 3D Slicer, HOROS, OsiriX, and the OHIF viewer.  MITK Workbench MITK Workbench is a free open-source software for medical image processing, annotation, and segmentation. It’s based on The Medical Imaging Interaction Toolkit (MITK), “open-source software for the development of interactive medical image processing software.”   The source code is stored in GitHub and there is MTIK Workbench software that anyone can download and use for Windows, Linux, and Mac (macOS). Here’s more information about how you can use the MTIK Workbench for medical imaging annotation and segmentation projects.  MTIK, and the subsequent open-source workbench tool, were originally developed for and by PhD students and researchers in the Division of Medical and Biological Informatics (MBI) of the German Cancer Research Center. ITK-Snap ITK-Snap is another open-source medical imaging annotation tool – unlike some of the others we will cover in this article, it is focused exclusively on one step of the broader data annotation process: the segmentation task.  It was created as the result of a long-term collaboration between researchers at PICSL at the University of Pennsylvania, and the Scientific Computing and Imaging Institute (SCI) at the University of Utah, and as a result has a heavy academic following.  ITK-Snap’s main offering is manual segmentation tools (eg. brush and paint); it also provides a basic set of semi-automatic tools (mainly the ‘Snake Interaction Mode’) and complementary tools to the segmentation process (mainly the interpolation feature). It is a perfect fit and a very popular option for practitioner teams just starting out, and it supports DICOM and NIfTI medical image file formats.  3D Slicer 3D Slicer was created for the “visualization, processing, segmentation, registration, and analysis of medical, biomedical, and other 3D images and meshes.” It comes with downloadable desktop software, can be used commercially, and enables access to an extensive development platform, and an active network of users. 3D Slicer helps medical imaging operations and data teams implement segmentation on multi-layered medical images, including 2D and 3D segmentation – tools available for segmentation include manual ones (eg. brush, drawing tool, and eraser) as well as a larger set of semi-automatic ones compared to ITK-Snap (eg. thresholding and level tracking).  3D Slicer also allows for basic tasks complementary to segmentation, including basic interpolation between slices, and filters. The main downside that teams often lament when using 3D Slicer for annotating images and files is the functionality set (which is often reported as being quite convoluted) and the steeper learning curve compared to other tools mentioned in this article.  For over 10 years, the US National Institutes of Health (NIH) has been a key contributor and supporter, and 3D Slicer has had over 1 million downloads since it was launched.  HOROS  HOROS is another free open-source medical imaging viewer and annotation software project. It is often a preferred tool when annotating with Apple computers and, not by coincidence, its stated goal is “to develop a fully functional, 64-bit medical image viewer for macOS”. Annotation and ML teams can use the HOROS viewer and annotation tools to annotate medical images and videos, store them in the cloud, and create reports to document an annotation project collaboratively.  Several other medical and healthcare-related projects have contributed and are a part of HOROS, including OsiriX, OpenJPEG, OpenGL, VTK, ITK, DCMTK, GDCM, Grok, and Horos Cloud. HOROS works with and is supported by technology partners in the healthcare sector as well as by donations from users.  OsiriX  Interconnected to, and a supporter of HOROS, OsiriX is another option that many teams opt for when looking for a labeling tool to get started with. Initially fully open-sourced, OsiriX now offers either a ‘Lite’ version, which is available for free as a demo application, or OsiriX MD, which is a commercial version that you can use from $69.99 per month.  Similar to most open-source tools, OsiriX Lite is often leveraged by early-stage startups, proof-of-concept (POC) projects, and research work. Based on our many conversations with teams, a few key features are worth digging into when evaluating a tool like OsiriX Lite against others; specifically, its capabilities in regards to 3D rendering, as well as DICOM and collaboration features (which, depending on the use case, teams often cite as being limited).  On the other hand, one of the main benefits of OsiriX MD is to make up for the issue of security, which is one of the main challenges with open-source annotation tools (and which we’ll go through in a second more in-depth).  OsiriX MD is an FDA-cleared and CE II labeled tool, and this increased level of security and safety makes it a better option for teams annotating professionally (or undergoing or eyeing the FDA approval). OHIF Viewer  The OHIF Viewer was developed by and is supported by the Open Health Imaging Foundation (OHIF), at the Massachusetts General Hospital (MGH), and is open-source software under an MIT license.  The OHIF Viewer is a tool for creating “custom workflows with user-friendly interfaces. Review cases and report results quickly, zero installation required.” It includes advanced visualization tools, and an easy-to-use annotation suite, and is compliant with DICOMWeb and OpenID Connect standards.  OHIF is an open-source annotation tool that comes close to commercial options, as it supports multi-modal image fusion, multiplanar reformatting, and more. It also comes with a cloud-based interface, making it easier to manage collaborative annotation projects.  Despite the various benefits of using open-source annotation tools for medical imaging projects, there are several downsides too.  What are the Downsides of Using Open-Source Tools for DICOM Annotation?  As with any DICOM annotation tool, the ultimate goal of the labeling process is to provide high-quality data to the next step in the process; annotation is simply one stage, albeit a crucial one – in the case of building machine learning applications, once the datasets are labeled and annotated, you will be putting them to test by feeding them into a model (often a broader machine learning (ML) or computer vision (CV) model), then training and iterating on the training, until you are ready to launch a production-ready model and finally solve the critical objective you set out to achieve. Similarly, a hospitality software development company can apply such structured data processing and iterative enhancements to optimize guest experiences and operational efficiencies in travel and hospitality industries. Open-source tools are often a great starting point when going from 0 to 1 with the annotation of medical imaging and video datasets, but, as we all know, they are inherently limited in their ability to achieve some of the more critical and powerful outcomes that teams face as they progress through the journey.  Throughout conversations with thousands of practitioners and leaders across the Medical AI community, we recurrently hear about numerous key downsides of open-source annotation software – these are often useful to consider ahead of time, in order for you to effectively plan out what your next steps in your data journey will be.  Below, we’ll dig deeper into the three main ones – scale, security, and collaboration – which you can also read more about in our blog here. These are:  Scaling annotation activity with open-source tools is a big challenge. Open-source tools often come with a basic set of features (and hence are a perfect fit for many teams as they get started), but lack the wider set of needs that companies start to require at scale. For example, whereas it’s often a sensible setup for teams to collaborate on the annotation process over an open-source tool and back-and-forth emails, as the number of annotators and volume of data increases, in-app and real-time collaboration capabilities like tagging become key. Teams we work with often start to feel this pain point as they start to scale, and that’s when a more solid commercial option can help save resources, speed up the process, and also avoid errors and inaccuracy. Open-source tools inherently fall behind on security requirements. In most cases, open-source tools don’t benefit from the rigorous compliance standards of commercial tools, and by nature don’t include features like auditing, which more established companies require in order to achieve milestones like FDA approval. Many don’t have auditable data trails that can be monitored, tracked, and reported on, making it more difficult to achieve compliance with the FDA and HIPAA, or GDPR and CE certification in Europe.  Free doesn’t always mean cost-effective. As the volume of annotations and annotators increases, the hidden costs of managing the process start to grow exponentially. At this stage, project leaders often find themselves needing to start being able to quantify, manage and measure their process and work; they need to be able to monitor and gain clear insight into the process and streamline operations on multiple spectrums. This is where limitations of open-source tools start to heavily affect their ability to achieve their objectives; two examples we hear often at this stage are frustrations around needing to write off a large percentage of time on non-value add tasks, as well as not being able to track how each annotator is performing, leading to poor process and output.  After getting started with open-source annotation software for their medical imaging dataset annotation projects, most teams tend to graduate to a commercial, proprietary tool that’s purpose-built to help take their project from 1 onwards. Tools like these are often easy to collaborate across, have best-practice security standards, and allow data operations and machine learning teams to scale their project cost-effectively (as well as help attain milestones like FDA clearance).  Encord is the leading annotation platform in medical AI, with a platform built to easily manage the annotation process, while allowing for the most complex of annotation tasks. At Encord, we have developed our medical imaging dataset annotation software in collaboration with data operations, machine learning, and AI leaders across the medical industry – this has enabled us to build a powerful, automated DICOM annotation suite, allowing for fully auditable data, and powerful labeling protocols. A few of the successes achieved by the medical teams we work with:  Stanford Medicine cut experiment duration time from 21 to 4 days while processing 3x the number of images in 1 platform rather than 3  King’s College London achieved a 6.4x average increase in labeling efficiency for GI videos, automating 97% of the labels and allowing their annotators to spend time on value-add tasks Memorial Sloan Kettering Cancer Center built 1000, 100% auditable custom label configurations for its pulmonary thrombosis projects Experience Encord in action. Sign-up for an Encord Free Trial: The Active Learning Platform for Computer Vision, used by the world’s leading computer vision teams.  AI-assisted labeling, model training & diagnostics, find & fix dataset errors and biases, all in one collaborative active learning platform, to get to production AI faster. Try Encord for Free Today.  Want to stay updated? 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