Medical Image Segmentation: A Complete Guide

In 1985, the PUMA 560 surgical robot made history by assisting the team at Memorial Medical Center during a stereotactic brain biopsy, marking one of the earliest recorded instances of robotic-assisted surgery and astonishing the world. Fast forward to today — surgical robotic systems are supporting surgeons across a growing array of medical interventions, assisting surgeries in ways few people imaged a few decades ago. Over the past eight years alone, the Robotically-Assisted Surgical (RAS) Devices market has expanded from $800 million in 2015 to well over $3 billion today. From prominent healthcare organizations to cutting-edge research institutes, from rapidly growing startups to non-profit initiatives, diverse teams are busy developing innovative surgical robotic systems. Their goal is to enhance surgical efficiency, improve precision and, ultimately, deliver better outcomes for patients. The recent leaps in computer vision have also further spurred this growth, as artificial intelligence is rapidly entering the operating room and enabling these systems to better perceive and interpret visual information in real time and aid surgeons on a wider range of tasks. This article explores the landscape of AI applications in surgical video analysis, some of the key innovators in the space and the role of high-quality training data in the development of AI-assisted surgical systems. AI-Assisted Surgical Robotics Companies like Intuitive Surgical, creator of the Da Vinci Surgical System, led the way in the 1990s: Da Vinci was the first robotics system approved by the FDA, initially for visualization and tissue retraction in 1997 and later for general surgery in 2000. With over 6,000 robots installed worldwide and over $6b in annual revenue, Intuitive has dominated the surgical robotics industry for the better part of the last 20 years, transforming the industry and enabling patient outcomes that were previously impossible. Yet 2019 marked the start of some of its patent expiries, and with that, a wave of new entrants and innovators. The use of AI-assisted techniques in robotics now extends from preoperative planning, to intraoperative guidance and postoperative care, and has advanced significantly thanks to the close collaboration of surgeons, programmers, and scientists. Let’s discuss some of the major real-world applications and teams working in this field — starting with preoperative planning. Preoperative planning Preoperative (pre-op) planning includes a range of workstreams — from visualizing the steps of the operation, to forming a plan to tackle navigation or improve precision. Machine learning and computer vision are being leveraged in pre-op planning in many ways: from rapidly analyzing the tabular and visual data of patients (like medical records or scans), to ensuring precise trajectory planning, optimizing incision sites, and gaining more insights into potential complications. Surgical planning begins with processing and fusing various medical imaging modalities, such as CT scans, MRI scans, and ultrasound scans, to generate a comprehensive 3D model of the patient's anatomy. Computer vision algorithms and deep learning models are then employed to quickly analyze this visual data and surface recommendations and risks with pursuing different surgical steps. Algorithms also enable surgeons to identify and segment specific anatomical structures and regions of interest from the imaging data (like organs, blood vessels, abnormalities, and other critical structures within the 3D model). This segmentation is crucial for surgical planning as it provides a clear visualization of the target area. From here, surgeons can explore different surgical approaches and plan the optimal trajectory for instruments and incisions, assessing the risk factors by quantifying the distance or overlap between the planned surgical path and nearby structures. Pre-op data can also be combined with intraoperative data to achieve surgical outcomes not otherwise possible. One of the most innovative end-to-end platforms is Paradigm™ by Proprio Vision, who just a few days announced the successful completion of the world's first light field-enabled spine surgery. Using an array of advanced sensors and cameras, Paradigm captures high-definition multimodal images during surgery and integrates them with preoperative scans to provide surgeons with real-time mapping of the anatomy. In addition to augmenting navigation capabilities during a procedure, Paradigm also collects large amounts of pre-op and intra-op data to inform future surgical decision-making and improve surgical efficiency and accuracy. You can read more about Proprio's announcement on their website here. Another end-to-end robotic system is Senhance, by Asensus Surgical, which in 2021 was cleared by the FDA for general surgeries. Senhance allows surgeons to create simulations for preoperative planning, while also providing real-time data for intraoperative guidance and generating insightful analytics for postoperative performance assessments and care. Intraoperative guidance A recent report by Bain & Company found that over 50% of surgeons surveyed made use of robotic systems in some capacity during general surgeries. During procedures, where even the slightest hand trembling can risk causing significant harm, image-guided surgery is turning into a requirement. Here, computer vision is often employed for instrument tracking and object recognition, which in turn are leveraged to feed video data to AI models that can monitor the procedure and generate guidance and warnings in case of anomalies, such as excessive bleeding or tissue damage. AI-assisted systems allow surgical robots to locate and follow the movement of surgical instruments, ensuring they are precisely positioned and maneuvered. They can also be used to identify critical structures and masses in the video footage, providing augmented guidance to the surgeon in real time. Model-assisted annotations of polyps in the Encord training data platform General and Minimally Invasive Surgery (MIS) Robotic assisted devices are more and more frequent in Minimally Invasive Surgeries (MIS). The primary objective of MIS is to reduce the trauma to the patient's body; the incision surface area is smaller, and often serves as an entry point, or port, for specialized instruments and a camera, known as a laparoscope, to enter the tissues and feed back real-time video data, which allows surgeons to view internal stuctures on a monitor and be guided through the procedure. MIS employs long, thin instruments with articulating tips that can be maneuvered through the small incisions. Systems like Dexter (by Distalmotion) are currently being used for daily gynecology, urology and general surgery procedures in Europe. “Surgeons can choose to operate entire procedures robotically, or they can leverage the ability to easily switch between the robotic and laparoscopic modalities to perform specialized tasks such as stapling with their preferred and trusted instruments,” Distalmotion CEO Michael Friedrich said in a recent press release announcing their upcoming US expansion. Another promising platform is Maestro (built by Moon Surgical), which sits at the intersection of robotic-assisted surgery and conventional surgery: acting as a robotic surgical assistant, it augments the precision and control of laparoscopic surgery, increasing the dexterity of a surgeon's own hand. Just this month, Moon Surgical announced the successful completion of the first 10 laparoscopic surgeries with its Maestro system in France. The procedures — bariatric and abdominal surgery procedures — were performed by laparoscopic surgeons Dr. Benjamin Cadière and Dr. Georges Debs, who said that the platform provided them with stability and precision that are difficult to match with human assistance. Many different procedure types are benefitting from the innovation in surgical assisted devices. A few examples are: Orthopedic Surgery. Orthopedic surgery is primarily used for the treatment of musculoskeletal conditions and disorders, mostly relating to bones and joints. With deep learning and computer vision, surgeons can build a pre-op model to plan the creation of patient-specific implants and the precise alignment of bones and joints, and then leverage a robotic arm to facilitate the optimal placement during the surgery. Stryker, the creators of the MAKO surgical assistant, are one of the pioneers in this space: MAKO turns a CT scan of a patient's joint into a 3D model, measures soft tissue balance, and, during surgery, ensures the placement is optimized to the patient's anatomy. Ganymed Robotics is another innovator in the space of orthopedic robotics. The Paris-based startup's team of computer vision and deep learning imaging experts have built a tool that leverages multimodal sensors to improve hard tissue surgery, starting with total knee arthroplasty (TKA). Robotic Bronchoscopy. Bronchoscopy helps evaluate and diagnose lung conditions, obtain samples of tissue or fluid, and remove foreign bodies. During a robotic bronchoscopy, the doctor uses a controller to operate a robotic arm, which guides a catheter (a thin, flexible, and maneuverable tube equipped with a camera, light, and shape-sensing technology) through the patient’s airways. Noah Medical received FDA clearance earlier this year for its Galaxy System™: a computer vision powered lung navigation system that improves the visualization and access of robotic brochoscopies. Microsurgery. Microsurgery requires the use of high-powered microscopes and precision instruments to perform intricate procedures on tiny structures within the body, such as blood vessel, nerve and tissue repairs. These kinds of surgeries operate hard-to-see anatomical structures that are often invisible to the human eye, and surgeons performing them need to undergo extensive training to develop exceptional hand-eye coordination. A handful of computer vision powered systems are being built to help improve the outcomes of these delicate surgeries, like MUSA-3, the microsurgery robot by Microsure, which allows surgeons to use a joystick to control instrument positioning during lymphatic surgery. The system is optimized for tremor-filtered movements and high-precision, and uses high-definition on-screen displays to enable real-time image analysis during these exceptionally delicate procedures. The Microsure team raised a €38m Series B earlier this month, as they eye FDA clearance in the US and CE-mark in Europe. Postoperative analysis and training Successful patient outcomes are achieved before, during, and after what happens in the operating room. AI surgical systems are valuable in post-operative analysis, as surgeons can review the process to understand improvement areas, identify potential health risks for the patient, and share insights to align expectations. Video data can also help trained newly formed surgeons, and provide education and knowledge share for the academic surgery community. Annotated surgical videos contain information regarding critical procedures, and can help inform students about effective surgical practices or risks involved with specific techniques. AI systems can also assess surgical performance by monitoring live video feeds and comparing a surgeon’s techniques with those used in similar procedures previously. The system can record custom metrics such as an operation’s total duration, patient satisfaction and post-operative complications, establishing benchmarks and shared understanding. A leader in this space is Orsi Academy, a Belgian training and research community that helps train medical professionals in new AI-driven techniques, such as computer vision for analyzing surgical videos, surgical data science for performance evaluation, and 3D printing, to simulation to help surgeons better understand and view specific body parts and surgical sites. Just a few days ago, Orsi Academy announced that their augmented reality tool (developed by Orsi Innotech) had enabled surgeons at Erasmus Medical Center to perform the world's first robot-assisted lobectomy using augmented reality, marking a huge achievement for the AI-assisted world of surgery. During this surgery, virtual overlay of the tumor, blood vessels and airways were projected over the camera image of the patient’s lung and was rendered with real-time AI-assisted robotic instrument detection. This allows surgeons to find their way inside the patient’s body more safely & effectively.  Orsi Academy will be hosting their annual Orsi Event in Belgium, on December 14th and 15th. Details will be available on their website shortly.

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.

Machine learning has made waves within the medical community and healthcare industry. Artificial Intelligence (AI) has proven itself useful in numerous uses across a variety of domains, from Radiology and Gastroenterology to Histology and Surgery.  The frontier has now hit Electrocardiography (ECG) as well. With an annotation tool, you can annotate the different waves on your Electrocardiogram diagrams and train machine learning models to recognize patterns in the data. The first open-source frameworks have been developed to build models based on ECG data e.g. Deep-Learning Based ECG Annotation. In this example, the author automated the process of annotating peaks of ECG waveforms using a recurrent neural network in Keras. Even though the model was not 100% performant (it struggles to get the input/output right). It seems to work well on the QT database of PhysioNet. The Authors does mention it fails in some cases that it has never seen. Potential future development of machine learning would be to play with augmenting the ECGs themselves or create synthetic data. The 3 main components of an ECG: the P wave, which represents the depolarization of the atria; the QRS complex represents the depolarization of the ventricles; and the T wave, which represents the repolarization of the ventricles. Source: Wikipedia Another example of how deep learning and machine learning is useful in ECG waveforms can be found in the MathWorks Waveform Segmentation guide. Using a Long Short-Term Memory (LSTM) network, MathWorks achieved impressive results as seen in the confusion matrix below: If you want to get started yourself you can find a lot of open-source ECG datasets, e.g. the QT dataset from PhysioNet. Why are ECG Annotations Important in Medical Research? ECG annotation is an essential aspect of medical research and diagnosis, involving the identification and interpretation of different features in the ECG waveform. It plays a critical role in the accurate diagnosis and treatment of heart conditions and abnormalities, allowing you to detect a wide range of heart conditions, including arrhythmias, ischemia, and hypertrophy. Through the meticulous analysis of the ECG waveform, experts can identify any irregularities in the electrical activity of the heart, accurately determining the underlying cause of a patient's symptoms. The information gleaned from ECG annotation provides vital indicators of heart health, including heart rate, rhythm, and electrical activity. Regular ECG monitoring is invaluable in the management of patients with chronic heart conditions such as atrial fibrillation or heart failure. Here ECG annotation assists experts in identifying changes in heart rhythm or other abnormalities that may indicate a need for treatment adjustment or further diagnostic testing. With regular ECG monitoring and annotation, clinicians can deliver personalized care, tailoring interventions to the unique needs of each patient. How can Machine Learning Support ECG Annotations? Machine learning has significant potential in supporting and automating the analysis of ECG waveforms, providing a powerful tool for clinicians for improving the accuracy and efficiency of ECG interpretation.  By utilizing machine learning algorithms, ECG waveforms can be automatically analyzed and annotated, assisting clinicians in detecting and diagnosing heart conditions and abnormalities faster and at higher accuracy. One of the main benefits of machine learning in ECG analysis is the ability to process vast amounts of patient data. By analyzing large datasets, machine learning algorithms can identify patterns and correlations that may be difficult or impossible for humans to detect. This can assist in the identification of complex arrhythmias or other subtle changes in the ECG waveform that may indicate underlying heart conditions. Additionally, machine learning algorithms can help in the detection of abnormalities or changes in the ECG waveform over time, facilitating the early identification of chronic heart conditions. By comparing ECG waveforms from different time points, machine learning algorithms can detect changes in heart rate, rhythm, or other features that may indicate a need for treatment adjustment or further diagnostic testing. Lastly, machine learning models can be trained to recognize patterns in ECG waveforms that may indicate specific heart conditions or abnormalities. For example, an algorithm could be trained to identify patterns that indicate an increased risk of a heart attack or other acute cardiac event. By analyzing ECG waveforms and alerting clinicians to these patterns, it can help in the early identification and treatment of these conditions, potentially saving lives. The three tools we will be reviewing today are: Encord ECG OHIT ECG Viewer WaveformECG Encord ECG Encord is an automated and collaborative annotation platform for medical companies looking at ECG Annotation, DICOM/NIfTI annotation, video annotation, and dataset management. It's the best option for teams that are: Looking for automated, semi-automated or AI-assisted image and video annotation. Annotating all ontologies. Working with other medical modalities such as DICOM and NIfTI. Wanting one place to easily manage annotators, track performance, and create QA/QC workflows. Benefits & Key features: Use-case-centric annotations — from native DICOM & NIfTI annotations for medical imaging to ECG Annotation tool for ECG Waveforms. Allows for point and time interval annotations. Supports the Bioportal Ontology such as PR and QT intervals. Integrated data labeling services. Integrated MLOps workflow for computer vision and machine learning teams. Easy collaboration, annotator management, and QA workflows — to track annotator performance and increase label quality. Robust security functionality — label audit trails, encryption, FDA, CE Compliance, and HIPAA compliance. Advanced Python SDK and API access (+ easy export into JSON and COCO formats). Best for teams who: Are graduating from an in-house solution or open-source tool and need a robust, secure, and collaborative platform to scale their annotation workflows. Haven't found an annotation platform that can actually support their use case as well as they'd like (such as building complex nested ontologies, or rendering ECG waveforms). Team looking to build artificial neural networks for the healthcare industry. AI-focused cardiology start-ups or mature companies looking to expand their machine-learning practices should consider the Encord tool. Pricing: Free trial model, and simple per-user pricing after that. OHIF ECG Viewer The OHIF ECG Viewer provides can be found from Radical Imaging’s Github. The tool provides a streamlined annotation experience and native image rendering with the ability to perform measurements of all relevant ontologies. It is easy to export annotations or create a report for later investigation. The tool does not support any dataset management or collaboration which might be an issue for more sophisticated and mature teams. For a cardiologist just getting started this is a great tool and provides a baseline for comparing to other tools. Benefits & Key features: Leader in open-source software. Renders ECG waveform natively. Easy (& free) to get started labeling images with. Great for manual ECG annotation. Best for: Teams just getting started. Pricing: Free. WaveformECG The WaveformECG tool is a web-based tool for managing and analyzing ECG data. The tool provides a streamlined annotation experience and native image rendering with the ability to perform measurements of all relevant ontologies. It is easy to export annotations or create a report for later investigation. The tool does not support any dataset management or collaboration which might be an issue for more sophisticated and mature teams. So if you're new to the deep learning approach to ECG annotations the WaveformECG tool might be useful but if you’re looking at more advanced artificial neural networks or deep neural networks it might not be the best place. Benefits & Key features: Allows for point and time interval annotations and citations. Supports the Bioportal Ontology and metrics. Annotations are stored with the waveforms, ready for data analysis. Renders ECG waveform natively. Supports scrolling through each ECG waveform. Best for: Researchers and students. Pricing: Free. Conclusion There you have it! The 3 Best ECG annotation Tools for machine learning in 2023.  We’re super excited to see the frontier being pushed on ECG waveforms in machine learning and proud to be part of the journey with our customers. If you’re looking into augmenting the ECGs themselves or creating synthetic data get in touch and we can provide you input and help with it!