Niffler: A DICOM Framework for Machine Learning Pipelines against Real-Time Radiology Images

I have been developing Niffler for quite some time. It is a DICOM network framework for machine learning pipelines. It retrieves the DICOM images from the PACS real-time and retrospectively based on the queries. It has been running for 19 months in our lab stable now, and has powered several machine learning research.

It extracts and stores metadata from the PACS in real-time, and also retrieves studies retrospectively for each specific studies on-demand.

Now it is time to make this project public for the broader scientific community. Please find it at https://github.com/Emory-HITI/Niffler. This is still an alpha release. But we are working around the clock to make it usable by anyone in the universe of radiology!

 For more details and citation, please follow our pre-print: 

Kathiravelu, Pradeeban, Ashish Sharma, Saptarshi Purkayastha, Priyanshu Sinha, Alexandre Cadrin-Chenevert, Imon Banerjee, and Judy Wawira Gichoya. Developing and Deploying Machine Learning Pipelines against Real-Time Image Streams from the PACS. arXiv preprint arXiv:2004.07965 (2020).

We will keep you updated! Please feel free to send me if you have any questions or suggestions for improvement. I am also up for collaborations, as always! :)