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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.
Whether you are:
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:
We’ll be updating this review frequently to ensure you can stay on top of notable releases and developments – the first six months of 2023 have been a whirlwind for the computer vision and AI space and the next 6 will be for the books.
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:
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:
Further reading:
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:
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:
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:
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:
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:
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:
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:
Best for: Medical image annotation, students, and research teams.
Pricing: Free!
Further reading:
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:
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:
There you have it! The 6 most popular annotation tools for annotating DICOM, as of time of writing (July 2023).
For further reading, you might also want to check out a few honorable mentions, both paid and free annotation tools:
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