Machine learning has the potential to play a huge role in the medical industry, especially when it comes to medical images. High-quality training data is the key to building models that can improve medical image diagnosis and preventing misdiagnosis.
We’ve already compiled a list of the best image tagging tools for computer vision, but medical imaging requires requires a unique set of high precision features.
Here is a curated list of the best image annotation tools that are suited for medical imaging.
Medical Image Tagging Tools
- TrainingData.io: TrainingData.io is a medical image annotation tool for data labeling. It supports DICOM image format for radiology AI.
- Lionbridge AI: Lionbridge AI has deep experience in all aspects of the medical devices vertical. We have 500,000 qualified contributors who can provide image annotation services quickly, with high precision. In addition, Lionbridge’s team can help you manage your project timeline, budget, and quality control.
- ImageJ: ImageJ is a Java-based image processing program developed at the National Institutes of Health and the Laboratory for Optical and Computational Instrumentation.
- OsiriX Viewer: OsiriX is an image processing application for Mac dedicated to DICOM images produced by equipment. OsiriX is complementary to existing viewers, in particular to nuclear medicine viewers. It can also read many other file formats: TIFF (8,16, 32 bits), JPEG, PDF, AVI, MPEG and QuickTime.
- ITK-SNAP: ITK-SNAP is a free-software and cross-platform tool that provides semi-automatic segmentation using active contour methods, as well as manual delineation and image navigation.
- Cogito: Cogito provides machine learning training data. The services offered include image annotation, content moderation, sentiment analysis, chatbot training, and more.
- LabelBox: Labelbox is a platform for data labeling, data management, and data science. Its features include image annotation, bounding boxes, text classification, and more.
- Dataturks: Dataturks is a data annotation outsourcing company that offers many data annotation capabilities, ranging from image segmentation to named entity recognition (NER) tagging in documents.
- ePAD: ePAD is a freely available quantitative imaging informatics platform, developed by the Rubin Lab at Stanford Medicine Radiology at Stanford University. Thanks to its plug-in architecture, ePAD can be used to support a wide range of imaging-based projects.
- 3D Slicer: 3D Slicer is an open source software platform for medical image informatics, image processing, and three-dimensional visualization. Built over two decades through support from the National Institutes of Health and a worldwide developer community, Slicer brings free, powerful cross-platform processing tools to physicians, researchers, and the general public.
- Edgecase: Edgecase is a data factory providing synthetic data and data labeling services. With connections to universities and industry experts, Edgecase provides data annotation and complex datasets to AI companies in retail, agriculture, medicine, security and more.
- Parallel Dots: Parallel Dots provides text, image, and video analysis API such as sentiment analysis and face recognition.
- Ratsnake: Ratsnake is a software tool for efficient, semantically-aware graphic annotation of images. It aims to aid the collection of knowledge regarding image content for pattern recognition, image mining and related applications. Ratsnake uses a novel semi-automatic image annotation framework based on a customizable active contour model that takes into account quick user annotations.
In addition to these awesome image tagging tools, we at Lionbridge AI provide fast, accurate medical image tagging services. We have deep experience in all aspects of the medical devices vertical, and our network of 500,000 experts can annotate your medical images quickly and precisely.
Contact us now to learn more about Lionbridge AI’s medical image tagging services.