Homogenization of multi-institutional chest x-ray images in various data transformation schemes

Hyeongseok Kim; Seoyoung Lee; Woo Jung Shim; Min-Seong Choi; Seungryong Cho

doi:10.1117/1.JMI.10.6.061103

26 April 2023 Homogenization of multi-institutional chest x-ray images in various data transformation schemes

Hyeongseok Kim, Seoyoung Lee, Woo Jung Shim, Min-Seong Choi, Seungryong Cho

Author Affiliations +

Journal of Medical Imaging, Vol. 10, Issue 6, 061103 (April 2023). https://doi.org/10.1117/1.JMI.10.6.061103

Abstract

Purpose

Although there are several options for improving the generalizability of learned models, a data instance-based approach is desirable when stable data acquisition conditions cannot be guaranteed. Despite the wide use of data transformation methods to reduce data discrepancies between different data domains, detailed analysis for explaining the performance of data transformation methods is lacking.

Approach

This study compares several data transformation methods in the tuberculosis detection task with multi-institutional chest x-ray (CXR) data. Five different data transformations, including normalization, standardization with and without lung masking, and multi-frequency-based (MFB) standardization with and without lung masking were implemented. A tuberculosis detection network was trained using a reference dataset, and the data from six other sites were used for the network performance comparison. To analyze data harmonization performance, we extracted radiomic features and calculated the Mahalanobis distance. We visualized the features with a dimensionality reduction technique. Through similar methods, deep features of the trained networks were also analyzed to examine the models’ responses to the data from various sites.

Results

From various numerical assessments, the MFB standardization with lung masking provided the highest network performance for the non-reference datasets. From the radiomic and deep feature analyses, the features of the multi-site CXRs after MFB with lung masking were found to be well homogenized to the reference data, whereas the others showed limited performance.

Conclusions

Conventional normalization and standardization showed suboptimal performance in minimizing feature differences among various sites. Our study emphasizes the strengths of MFB standardization with lung masking in terms of network performance and feature homogenization.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Citation Download Citation

Hyeongseok Kim, Seoyoung Lee, Woo Jung Shim, Min-Seong Choi, and Seungryong Cho "Homogenization of multi-institutional chest x-ray images in various data transformation schemes," Journal of Medical Imaging 10(6), 061103 (26 April 2023). https://doi.org/10.1117/1.JMI.10.6.061103

Received: 7 July 2022; Accepted: 3 April 2023; Published: 26 April 2023

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