Medical Image Deidentification, Cleaning and Compression Using Pylogik. (arXiv:2304.12322v1 [eess.IV])

Leveraging medical record information in the era of big data and machine
learning comes with the caveat that data must be cleaned and deidentified.
Facilitating data sharing and harmonization for multi-center collaborations are
particularly difficult when protected health information (PHI) is contained or
embedded in image meta-data. We propose a novel library in the Python
framework, called PyLogik, to help alleviate this issue for ultrasound images,
which are particularly challenging because of the frequent inclusion of PHI
directly on the images. PyLogik processes the image volumes through a series of
text detection/extraction, filtering, thresholding, morphological and contour
comparisons. This methodology deidentifies the images, reduces file sizes, and
prepares image volumes for applications in deep learning and data sharing. To
evaluate its effectiveness in the identification of regions of interest (ROI),
a random sample of 50 cardiac ultrasounds (echocardiograms) were processed
through PyLogik, and the outputs were compared with the manual segmentations by
an expert user. The Dice coefficient of the two approaches achieved an average
value of 0.976. Next, an investigation was conducted to ascertain the degree of
information compression achieved using the algorithm. Resultant data was found
to be on average approximately 72% smaller after processing by PyLogik. Our
results suggest that PyLogik is a viable methodology for ultrasound data
cleaning and deidentification, determining ROI, and file compression which will
facilitate efficient storage, use, and dissemination of ultrasound data.



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