Medical Image Deidentification, Cleaning and Compression Using Pylogik

TL;DR

PyLogik is a Python library that de-identifies, compresses, and prepares ultrasound images for data sharing and deep learning, achieving high accuracy and significant file size reduction.

Contribution

The paper introduces PyLogik, a novel Python-based tool for ultrasound image de-identification, cleaning, and compression, with demonstrated effectiveness and adaptability to other medical images.

Findings

Average Dice coefficient of 0.976 for de-identification accuracy

Approximately 72% reduction in image file size

Effective processing of ultrasound data for machine learning

Abstract

Leveraging medical record information in the era of big data and machine learning comes with the caveat that data must be cleaned and de-identified. 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 de-identifies the images, reduces file sizes, and prepares image volumes for applications in deep learning and data sharing. To evaluate its effectiveness in processing ultrasound data, 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 ~72% smaller after processing by PyLogik. Our results suggest that PyLogik is a viable methodology for data cleaning and de-identification, determining ROI, and file compression which will facilitate efficient storage, use, and dissemination of ultrasound data. Variants of the pipeline have also been created for use with other medical imaging data types.