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

Pradeeban Kathiravelu; Puneet Sharma; Ashish Sharma; Imon Banerjee; Hari Trivedi; Saptarshi Purkayastha; Priyanshu Sinha; Alexandre Cadrin-Chenevert; Nabile M. Safdar; Judy Wawira Gichoya

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Author Notes:

All the authors have substantially contributed to the conception or design of the work, and the writing and/or revision of the manuscript, and approved the final version of the manuscript, and be accountable for the manuscript's contents. Specific contributions are listed below.

Pradeeban Kathiravelu: Designed and developed the Niffler framework, implemented and deployed the use cases. Wrote and revised the manuscript.

Puneet Sharma: Designed the (2nd use case) scanner utilization use case and wrote and revised the text describing the same.

Ashish Sharma, Imon Banerjee, Hari Trivedi: Designed the Niffler framework and wrote and revised the text describing the same.

Saptarshi Purkayastha, Judy Wawira Gichoya: Designed the (1st use case) IVC filter use case and wrote and revised the text describing the same. Revised the manuscript.

Priyanshu Sinha, Alexandre Cadrin-Chenevert: Designed and developed the (1st use case) IVC filter use case and wrote and revised the text describing the same. Revised the manuscript.

Nabile Safdar: Designed the (3rd use case) scanner clock calibration use case and wrote and revised the text describing the same.

The authors declare no competing interests.

Subjects:

Research Funding:

This work was supported in part by The Cancer Imaging Archive (TCIA) Sustainment and Scalability Platforms for Quantitative Imaging Informatics in Precision Medicine [National Institute of Health (NIH) National Cancer Institute (NCI)] under Grant U24CA215109, and in part by the Methods and Tools for Integrating Pathomics Data into Cancer Registries (NIH NCI) under Grant UH3CA225021.

Keywords:

Machine learning
Picture archiving and communication system
Digital Imaging and Communications in Medicine
Clinical data warehouse

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

Pradeeban Kathiravelu, Emory University

Puneet Sharma, Emory University

Ashish Sharma, Emory University

Imon Banerjee, Emory University

Hari Trivedi, Emory University

Saptarshi Purkayastha, Purdue University

Priyanshu Sinha, Mentor Graphics

Alexandre Cadrin-Chenevert, Laval University

Nabile M. Safdar, Emory University

Judy Wawira Gichoya, Emory University

Tools:

Journal Title:

Journal of Digital Imaging

Publisher:

Springer | 2021-08-17

Type of Work:

Article | Preprint: Prior to Peer Review

Permanent URL:

https://pid.emory.edu/ark:/25593/vj3wv

Publisher DOI:

http://doi.org/10.1007/s10278-021-00491-w

Abstract:

Real-time execution of machine learning (ML) pipelines on radiology images is difficult due to limited computing resources in clinical environments, whereas running them in research clusters requires efficient data transfer capabilities. We developed Niffler, an open-source Digital Imaging and Communications in Medicine (DICOM) framework that enables ML and processing pipelines in research clusters by efficiently retrieving images from the hospitals’ PACS and extracting the metadata from the images. We deployed Niffler at our institution (Emory Healthcare, the largest healthcare network in the state of Georgia) and retrieved data from 715 scanners spanning 12 sites, up to 350 GB/day continuously in real-time as a DICOM data stream over the past 2 years. We also used Niffler to retrieve images bulk on-demand based on user-provided filters to facilitate several research projects. This paper presents the architecture and three such use cases of Niffler. First, we executed an IVC filter detection and segmentation pipeline on abdominal radiographs in real-time, which was able to classify 989 test images with an accuracy of 96.0%. Second, we applied the Niffler Metadata Extractor to understand the operational efficiency of individual MRI systems based on calculated metrics. We benchmarked the accuracy of the calculated exam time windows by comparing Niffler against the Clinical Data Warehouse (CDW). Niffler accurately identified the scanners’ examination timeframes and idling times, whereas CDW falsely depicted several exam overlaps due to human errors. Third, with metadata extracted from the images by Niffler, we identified scanners with misconfigured time and reconfigured five scanners. Our evaluations highlight how Niffler enables real-time ML and processing pipelines in a research cluster.

Copyright information:

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).

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