An efficient magnetic resonance image data quality screening dashboard

Purpose Complex data processing and curation for artificial intelligence applications rely on high‐quality data sets for training and analysis. Manually reviewing images and their associated annotations is a very laborious task and existing quality control tools for data review are generally limited...

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Bibliographic Details
Published in:Journal of applied clinical medical physics 2022-04, Vol.23 (4), p.e13557-n/a
Main Authors: Gates, Evan D. H., Celaya, Adrian, Suki, Dima, Schellingerhout, Dawid, Fuentes, David
Format: Article
Language:English
Subjects:
Annotations
Artificial Intelligence
Bias
Brain cancer
Brain Neoplasms
Brain research
dashboard
Data Accuracy
data curation
Data processing
Datasets
Humans
Image Processing, Computer-Assisted - methods
imaging informatics
Interfaces
Magnetic Resonance Imaging - methods
MRI
Technical Note
Technical Notes
Web browsers
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Summary:Purpose Complex data processing and curation for artificial intelligence applications rely on high‐quality data sets for training and analysis. Manually reviewing images and their associated annotations is a very laborious task and existing quality control tools for data review are generally limited to raw images only. The purpose of this work was to develop an imaging informatics dashboard for the easy and fast review of processed magnetic resonance (MR) imaging data sets; we demonstrated its ability in a large‐scale data review. Methods We developed a custom R Shiny dashboard that displays key static snapshots of each imaging study and its annotations. A graphical interface allows the structured entry of review data and download of tabulated review results. We evaluated the dashboard using two large data sets: 1380 processed MR imaging studies from our institution and 285 studies from the 2018 MICCAI Brain Tumor Segmentation Challenge (BraTS). Results Studies were reviewed at an average rate of 100/h using the dashboard, 10 times faster than using existing data viewers. For data from our institution, 1181 of the 1380 (86%) studies were of acceptable quality. The most commonly identified failure modes were tumor segmentation (9.6% of cases) and image registration (4.6% of cases). Tumor segmentation without visible errors on the dashboard had much better agreement with reference tumor volume measurements (root‐mean‐square error 12.2 cm3) than did segmentations with minor errors (20.5 cm3) or failed segmentations (27.4 cm3). In the BraTS data, 242 of 285 (85%) studies were acceptable quality after processing. Among the 43 cases that failed review, 14 had unacceptable raw image quality. Conclusion Our dashboard provides a fast, effective tool for reviewing complex processed MR imaging data sets. It is freely available for download at https://github.com/EGates1/MRDQED.
ISSN:1526-9914
1526-9914
DOI:10.1002/acm2.13557