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<p>Defacing of brain magnetic resonance imaging (MRI) scans by removing identifiable facial features is essential for protecting patient privacy, yet assessing defacing quality remains challenging. While deep learning methods offer solutions, they require large labeled datasets, limiting their practical applicability. This study presents <i>DefaceQA</i>, a machine learning (ML) approach for automated defacing quality assessment using quantitative image features. A dataset of 200 MRI scans from the Leukodystrophy Registry at the Leipzig University Medical Center was processed using four defacing algorithms: <i>PyDeface</i>, <i>QuickShear</i>, <i>FSL-Deface</i>, and <i>MRI-Deface</i>. Image features extracted from original and defaced scans were used to classify defacing efficacy. The ML classifiers achieved an AUROC of 0.84 and an accuracy of 0.85 under a lenient criterion for successful/unsuccessful defacing, with the Feature Similarity Index Measure (FSIM) emerging as a key predictor. The findings demonstrate ML’s potential for defacing evaluation while highlighting challenges related to dataset limitations and generalizability.</p>

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DefaceQA - automated quality assessment of brain MRI defacing software

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月7日

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  • Maryam Khodaei Dolouei,
  • Sina Sadeghi,
  • Toralf Kirsten

摘要

Defacing of brain magnetic resonance imaging (MRI) scans by removing identifiable facial features is essential for protecting patient privacy, yet assessing defacing quality remains challenging. While deep learning methods offer solutions, they require large labeled datasets, limiting their practical applicability. This study presents DefaceQA, a machine learning (ML) approach for automated defacing quality assessment using quantitative image features. A dataset of 200 MRI scans from the Leukodystrophy Registry at the Leipzig University Medical Center was processed using four defacing algorithms: PyDeface, QuickShear, FSL-Deface, and MRI-Deface. Image features extracted from original and defaced scans were used to classify defacing efficacy. The ML classifiers achieved an AUROC of 0.84 and an accuracy of 0.85 under a lenient criterion for successful/unsuccessful defacing, with the Feature Similarity Index Measure (FSIM) emerging as a key predictor. The findings demonstrate ML’s potential for defacing evaluation while highlighting challenges related to dataset limitations and generalizability.