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We developed a new de-identification technique that distorts the faces of head CT images. Head CT images that were distorted were labeled as \"original images\" and the others as \"reference images.\" Reconstructed face models of both were created, with 400 control points on the facial surfaces. All voxel positions in the original image were moved and deformed according to the deformation vectors required to move to corresponding control points on the reference image. Three face detection and identification programs were used to determine face detection rates and match confidence scores. Intracranial volume equivalence tests were performed before and after deformation, and correlation coefficients between intracranial pixel value histograms were calculated. Output accuracy of the deep learning model for intracranial segmentation was determined using Dice Similarity Coefficient before and after deformation. The face detection rate was 100%, and match confidence scores were &lt; 90. Equivalence testing of the intracranial volume revealed statistical equivalence before and after deformation. The median correlation coefficient between intracranial pixel value histograms before and after deformation was 0.9965, indicating high similarity. Dice Similarity Coefficient values of original and deformed images were statistically equivalent. We developed a technique to de-identify head CT images while maintaining the accuracy of deep-learning models. The technique involves deforming images to prevent face identification, with minimal changes to the original information.<\/jats:p>","DOI":"10.1007\/s12021-023-09631-9","type":"journal-article","created":{"date-parts":[[2023,5,24]],"date-time":"2023-05-24T23:46:01Z","timestamp":1684971961000},"page":"575-587","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["De-Identification Technique with Facial Deformation in Head CT Images"],"prefix":"10.1007","volume":"21","author":[{"given":"Tatsuya","family":"Uchida","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taichi","family":"Kin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Toki","family":"Saito","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naoyuki","family":"Shono","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Satoshi","family":"Kiyofuji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tsukasa","family":"Koike","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Katsuya","family":"Sato","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryoko","family":"Niwa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ikumi","family":"Takashima","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiroshi","family":"Oyama","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nobuhito","family":"Saito","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,25]]},"reference":[{"key":"9631_CR1","first-page":"530","volume":"1","author":"H Abdi","year":"2007","unstructured":"Abdi, H. 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Amira 3D\u00ae is available at\n                      \n                      . Face API is available at\n                      \n                      . Rekognition is available at\n                      \n                      . NeoFace KAOATO is available at\n                      \n                      . Dragonfly is available at\n                      \n                      .","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Information Sharing Statement"}},{"value":"The internal review board of the University of Tokyo Hospital approved the study protocol (consent number #2021107NI), and written informed consent was obtained from all patients prior to participation.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval and Consent"}},{"value":"All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}