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How Medical Imaging Annotation Is Transforming Healthcare

Having the right technology in place is essential for healthcare organizations seeking to deliver better patient outcomes. That’s why medical technology developers are working hard to build the next generation of software tools that will help medical professionals to deliver care more effectively. 

Annotation features provide a number of benefits in these ongoing efforts. Although typically associated with editing and workplace collaboration, medical annotations also have a very different and very specific role when it comes to diagnostic imaging and patient health records.

Enhancing Healthcare Collaboration with Annotations

One of the most straightforward use cases for medical annotation is communicating important information regarding diagnostic images. As images like MRIs and X-rays are passed back and forth between providers, radiologists, technicians, and clinicians, the ability to add comments and point out important details greatly reduces the chance of confusion or of some critical detail being overlooked.

The challenge in these cases, however, is to annotate images and documents without altering the integrity of the original files. This requires healthcare technology developers to build solutions that can retain an unaltered version of the file even as multiple collaborators view and make comments. 

Medical Annotation and Machine Learning

Healthcare solutions are rapidly incorporating sophisticated machine learning tools to analyze large quantities of data and make a quick, accurate diagnosis of conditions. Before these powerful tools can perform that diagnostic work, they need to be properly trained to know what they’re looking for, especially when it comes to very nuanced differences between scanned images and seemingly unrelated details in patient records.

By using annotation tools, medical technology specialists can provide excellent guidance for machine learning development. An MRI scan, for instance, contains so much information that an AI-driven program isn’t going to know what to look for unless the key elements are called out with annotations that indicate certain parts of the image or provide comments about noteworthy aspects.

The DICOM Dilemma

While many software integrations allow developers to incorporate annotation tools for common file formats like PDF and JPEG, the healthcare sector presents a unique challenge in the form of DICOM files. This industry-specific format contains both images and important metadata identifiers that provide information about the image itself and the patient in question. While there are ways to extract images from DICOM files and convert them into a more manageable format, doing so could endanger compliance status or permanently degrade the image quality.

Developers working on healthcare technology solutions need to make sure they can not only deliver annotation tools, but also the ability to add annotations to DICOM files without altering the source file itself. 

Mastering Medical Annotation with ImageGear Medical

ImageGear Medical provides a broad range of XML-based annotation features that allows healthcare software developers to implement UI elements for marking up both images and documents. Since this powerful imaging SDK also gives users the ability to create and view DICOM files, it can quickly enhance the functionality of medical applications to enhance collaboration and ensure diagnostic accuracy.

Once integrated into an application with a viewing UI, ImageGear Medical supports several commonly-utilized annotation marks that makes it easy for users to highlight certain aspects of an image, comment on them, and even cover up some elements using filled-in graphical objects. Annotations can also be grouped in layers to make them easier to manage and distinguish from one another.

ImageGear Medical annotation objects for DICOM include:

  • Text: Adds descriptive text using a variety of fonts, colors, and sizes. Opacity can be adjusted and the text object can appear with or without a border.
  • Point: Places a coordinate point on the image or document, which can be used to support other annotation marks.
  • Polyline: A series of connected straight lines formed by dragging and clicking a mouse or pointer.
  • Curve: Used for creating spline curve marks. Users can select multiple vertices and tensions when creating curves.
  • Ellipse: A circular outline mark that can be used to indicate important elements of an image or document. When filled, it can also cover up areas of the image.
  • Polygon: Like the ellipse, it can be filled or unfilled and is typically deployed to cover or highlight some aspect of an image or document. Polygons are especially useful for medical annotation because they can capture more lines and angles than simple rectangles or circles.

In order to maintain the integrity of the original image, ImageGear Medical stores annotations as a separate file that is overlaid upon the image during display. While annotations can be merged, or “burned in” the file, keeping them separate ensures that the original image itself is not altered directly. This is incredibly important when it comes to DICOM files, which often need to be kept on file for baseline comparisons on a future diagnosis.

Enhance Healthcare Flexibility with ImageGear Medical

Annotations and DICOM viewing support are just the beginning of ImageGear Medical’s expansive feature set. It also provides advanced filtering tools for sharpening and smoothing as well as image cleanup functions like despeckling, noise removal, and deskewing. With support for several dozen medical image and document formats, ImageGear Medical can easily convert files into easy-to-manage formats and compress files for efficient storage.

Available for .NET and C/C++ environments, ImageGear Medical can turn your healthcare application into a powerful annotation platform with full support for DICOM files. Start your free trial of this powerful SDK to discover first-hand how it can empower your medical annotation solution.