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The Digital Imaging and Communications in Medicine (DICOM) standard for digital medical imaging has been around since the 1980s. First adopted in radiology it is slowly spreading in pathology as well. Now with image analysis being an integral part of medical imaging workflows, the question arose if the annotations made on the images should have a standard format as well? And can the same DICOM format be used?

With deep learning taking over the medical image analysis field, the answer is a definitive yes! Deep learning requires a large number of annotations to train robust image analysis models. Making them requires a lot of time and work and having them in a format that can grant interoperability between different digital pathology and image analysis systems is becoming a requirement. 

In this episode my guest Dr. David Clunie, the DICOM standard author is explaining what annotations are, why do we need to standardize the format in which they are created, and why the interoperability of digital pathology systems is actually the responsibility of the users of the system and how to be proactive with system vendors to grant it. 

If you are working in the medical image analysis field or are looking into different image analysis systems that require annotations, this episode is for you!

And if you want to learn more about the DICOM standard for images, listen to:

• this episode of the “Beyond the Scope” podcast by DPA with Dr. Clunie

Or visit the following resources:

• The DICOM website
• David Clunie's Medical Image Format Site 

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