Patient Re-Identification Based on Deep Metric Learning in Trunk Computed Tomography Images Acquired from Devices from Different Vendors

Ueda, Yasuyuki; Ogawa, Daiki; Ishida, Takayuki

Journal of Imaging Informatics in Medicine, 2024, 141, 931

Number of Access:851（2026-04-05 07:53 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/94700

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Title
Patient Re-Identification Based on Deep Metric Learning in Trunk Computed Tomography Images Acquired from Devices from Different Vendors
Creator
NRID
1000050780805 1000050780805
ORCID
0000-0003-0975-2164 0000-0003-0975-2164
researchmap permalink
uedacci uedacci
Scopus Author ID
37001274700 37001274700
CreatorName
Ueda, Yasuyuki
CreatorName
Ogawa, Daiki
NRID
1000060309656 1000060309656
researchmap permalink
Taka_Ishida00 Taka_Ishida00
Scopus Author ID
35406489400 35406489400
ORCID
0000-0002-6851-0598 0000-0002-6851-0598
CreatorName
Ishida, Takayuki
Subject
Deep metric learning
Medical imaging
Patient re-identification
Augmentation technique
Convolutional neural network
Description
The version of record of this article, first published in Journal of Imaging Informatics in Medicine, is available online at Publisher’s website: https://doi.org/10.1007/s10278-024-01017-w.
Abstract
During radiologic interpretation, radiologists read patient identifiers from the metadata of medical images to recognize the patient being examined. However, it is challenging for radiologists to identify “incorrect” metadata and patient identification errors. We propose a method that uses a patient re-identification technique to link correct metadata to an image set of computed tomography images of a trunk with lost or wrongly assigned metadata. This method is based on a feature vector matching technique that uses a deep feature extractor to adapt to the cross-vendor domain contained in the scout computed tomography image dataset. To identify “incorrect” metadata, we calculated the highest similarity score between a follow-up image and a stored baseline image linked to the correct metadata. The re-identification performance tests whether the image with the highest similarity score belongs to the same patient, i.e., whether the metadata attached to the image are correct. The similarity scores between the follow-up and baseline images for the same “correct” patients were generally greater than those for “incorrect” patients. The proposed feature extractor was sufficiently robust to extract individual distinguishable features without additional training, even for unknown scout computed tomography images. Furthermore, the proposed augmentation technique further improved the re-identification performance of the subset for different vendors by incorporating changes in width magnification due to changes in patient table height during each examination. We believe that metadata checking using the proposed method would help detect the metadata with an “incorrect” patient identifier assigned due to unavoidable errors such as human error.
Publisher
Springer Science and Business Media LLC
Source Title
Journal of Imaging Informatics in Medicine
Volume (Issue)
141
Page
931
Date of Issued
2024-02-16
Language
English
Handle URL
https://hdl.handle.net/11094/94700
eISSN
29482933
Relation.isIdenticalTo
DOI (Publisher Version)
https://doi.org/10.1007/s10278-024-01017-w
Access Rights
open access
CopyRight
This article is licensed under a Creative Commons Attribution 4.0 International License.
oaire:version
Version of Record
Category
学術雑誌論文 Journal Article