Dr. Andreas Heindl •November 15, 2022
Three Ways Open Source Tools Are Slowing Down Your Medical Imaging Model Development
Open-source software and tools are widely available for computer vision and medical imaging machine-learning projects.
In some cases, it can be advantageous to use open-source tools when testing and training a machine-learning model on medical imaging datasets. You can save money, and several tools — such as 3DSlicer and ITK-Snap — are designed specifically for medical image annotation and training ML models on healthcare datasets.
In the healthcare sector, the quality of a dataset and the efficiency of the tools you use to annotate and train machine learning models is crucial. It could be a matter of life and death for patients, as medical specialists and doctors need the most accurate outputs from computer vision and ML models to diagnose patients.
As clinical and data operations teams know, the complexity, formats, and layers of data within medical images are complex and detailed. You need the right tools for the job. Using the wrong tool, such as an open-source annotation application could negatively impact model development.
In this article, we cover the main open-source tools for medical image annotation, the use cases for these tools, and how they’re holding your annotation projects back. We also outline what you need to look for in an annotation tool that will help you overcome these challenges, including the features that will give you the results you need.
What Are the Main Open-source Tools for Medical Image Annotation?
There are numerous open-source tools on the market that support medical image datasets, including 3DSlicer, ITK-Snap, MITK Workbench, RIL-Contour, Sefexa, and several others.
For this article, we will focus on two of the most popular open-source medical image annotation tools: 3DSlicer and ITKSnap. Although, the ways open-source tools can hold medical image annotation projects back aren’t limited to 3DSlicer and ITK-Snap.
What is 3D Slicer?
3D Slicer is a free open-source image computing platform. It was designed for the “visualization, processing, segmentation, registration, and analysis of medical, biomedical, and other 3D images and meshes.”
3D Slicer comes with downloadable desktop software, access to a development platform, and an active community of users and developers working on similar problems. It’s designed to work with some of the most popular and widely-used medical imaging formats, including DICOM and NIfTI.
3D Slicer supports 2D, 3D, and 4D segmentations, AI-based segmentation, tools for ground truth training data generation, and extensions for Deep Learning, Tensorflow, and MONAI compatibility. It also comes with surgical guidance and planning tools, and a whole load more. The US National Institutes of Health (NIH) has been a key contributor and supporter over the last 10 years, and 3D Slicer has had over 1 million downloads since it was first launched.
Despite extensive support and an active community, the user interface is somewhat complex and takes time to learn.
What is ITK-Snap?
It supports DICOM and NIfTI medical image file formats, with the core functionalities focusing on “semi-automatic segmentation using active contour methods, as well as manual delineation and image navigation.”
Improving medical image segmentation during the annotation of datasets is the main reason this tool was created, aiming to achieve a better, more intuitive user interface than other open-source software on the market.
ITK-Snap is the result of a decade-long collaboration between researchers at PICSL at the University of Pennsylvania, and the Scientific Computing and Imaging Institute (SCI) at the University of Utah.
What Are The Main Use Cases for Open-source Medical Image Annotation Tools?
Open-source annotation tools are used in numerous ways when annotators are working on medical image datasets. Images and videos come from dozens of sources (whether the datasets are open-source or in-house), such as MRI machines, X-rays and CT scans.
Particular use cases depend on the objectives and desired outcomes — the problem(s) that needs solving — of a machine-learning or computer vision-based medical imaging project. A consistent end goal is to solve a medical problem. Such as improving the percentage of patients accurately diagnosed, or using ML and AI-based models to more effectively identify diseases, illnesses, and tumors.
The more data you’ve got the better. It improves the chances of accurate outcomes when an ML model has more data to work with. However, high levels of accuracy are only possible if annotation and labeling are implemented accurately and efficiently, and for that, you need the right tools.
Open-source tools aren’t bad tools, as such. The ones we’ve mentioned in this article were created with medical imaging datasets and medical image formats and use cases specifically in mind. Many were created with the help of medical professionals, organizations, and data scientists.
However, there are several limitations, and there is a risk these limitations could hold annotation and computer vision projects back..
3 Ways Open-source Tools Are Holding Your Annotation Projects Back
1. Unable to effectively scale your annotation activity
One of the main challenges is scaling annotation activity.
When you use cloud-based tools and platforms, an annotation team can work collaboratively in real-time across several time zones, and work directly with data and medical ops teams in another country.
However, the tools mentioned in this article are desktop-based. It’s a seriously limiting feature when annotation teams need to work together on large imaging datasets, and teams need to receive quick feedback from medical imaging specialists when training ML models with new datasets.
If an annotation team is using open-source software, the only way to share images and receive feedback is through email and cloud-storage platforms, such as Dropbox. This can make it particularly difficult to scale annotation projects, especially when you’ve got large imaging datasets to work through and strict data security compliance requirements to follow.
2. Weak data security makes FDA and CE certification harder
Data security is absolutely crucial in the healthcare sector. In the US, medical data compliance is governed by the FDA and HIPAA. In the UK and Europe, CE certification and GDPR are always front-of-mind for any teams handling data, whether or not medical images have been stripped of identifiable patient information.
When you are using open-source tools, there’s no audit trail, and this could prove a costly mistake in the healthcare sector. Without an audit trail and timestamps, there’s no way to show who’s worked on which image and who made edits, annotations, labels, or any changes.
It’s much harder to adhere to medical data security regulations when medical imaging data isn’t fully auditable. It’s also easier for annotators to download copies of images onto personal computers and devices, causing a security risk, especially if images still have identifiable patient information on them.
3. You can’t monitor your annotators
Open-source annotation tools are free, but that doesn’t mean they’re cost-effective. In most cases, free tools aren’t as efficient as premium options on the market. Because open-source tools aren’t cloud-based, collaboration is more difficult and annotation, data ops, and medical project managers have no way of monitoring the progress of annotators.
Unlike premium solutions, these tools don’t come with performance and analytical dashboards. If a manager can’t oversee the work of annotators effectively then projects are more difficult to manage and the efficiency of annotations will be negatively impacted.
As a result, annotation projects will take longer, and if re-annotation is needed, or accuracy is low, then it will take even more time to generate accurate training data.
What Should You Look For in a Medical Annotation Tool to Overcome These Challenges?
Considering the challenges associated with open-source medical image annotation tools, it’s understandable that many project leaders and medical ops managers look for premium solutions.
To achieve the results you need from medical image annotation projects, you need a tool with the following features:
An easy-to-use, cloud-based, collaborative interface
It might sound basic, but it’s so important that the interface annotators are using is intuitive and collaborative.
You need to know that annotators in different countries, or on different shifts can work together on the same medical imaging datasets, and those datasets are accessible to data and healthcare ops teams in another country, as required. A cloud-based interface is the most effective way to ensure this.
Designed for and by medical imaging professionals and healthcare data scientists
Similar to the open-source tools, you need annotation software that’s been designed with the support and close collaboration of medical image and data professionals. Medical image annotation is more complex and involved than in other sectors. With the right tool, such as Encord, you can be confident it’s been designed with your needs and project goals in mind.
Native DICOM and NIfTI file support
It’s essential that the right tool comes with native DICOM and NIfTI file support. You need one that includes features that are specifically designed to annotate and label DICOM and other medical image files and formats.
A medical image annotation tool should allow you to see images in 2D orthogonal planes (coronal, sagittal, axial), viewing medical metadata and make window width (WW) and window level (WL) adjustments.
3D and 2D annotation, and powerful automation features
Automation features can save annotation teams a massive amount of time. One of the most powerful automation features is interpolation that can match pixel data, and allows annotators to draw the interpolation labels in arbitrary directions.
Project dashboard and quality control
Having a project dashboard and built-in quality control features is essential for the smooth running of any medical image annotation project. As a project manager, this is something open-source tools can’t provide, and this can make the difference between success or a costly failure.
Audit trails, and SOC 2 and HIPAA compliance
Having easily-accessible audit trails is mission-critical for medical and data ops teams and managers. Achieving FDA, CE, SOC 2 (Systems and Organizational Control 2) or HIPAA (Health Insurance Portability and Accountability Act) compliance is impossible without an auditable data trail. It’s an essential feature to have in any medical image annotation tool.
Encord has developed our medical imaging dataset annotation software in close collaboration with medical professionals and healthcare data scientists, giving you a powerful automated image annotation suite, fully auditable data, and powerful labeling protocols.
Experience Encord in action. Dramatically reduce manual video annotation tasks, generating massive savings and efficiencies. Try it for Free Today.
Dr Andreas Heindl is a Machine Learning Product Manager at Encord. He has spent the past 10 years applying computer vision and deep learning techniques in Healthcare at Encord, The Institute of Cancer Research, and Kheiron Medical. The main focus of Andreas' research and work until now has been to aid radiologists accurately diagnosing cancer by using artificial intelligence and computer vision. https://www.linkedin.com/in/andreasheindl/ https://twitter.com/ah80tw
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