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<p>Mid-infrared Optical Coherence Tomography (MIR-OCT) is a promising Non-Destructive Testing (NDT) technique due to its high-resolution imaging capabilities and extensive applicability across various industrial domains. Studies developing Deep Learning (DL) models to detect defects in MIR-OCT scans are scarce, and few have been used for ceramic quality assessment. To address this gap, we introduce the MIR-OCT Scan Dataset for Ceramic Quality Assessment (CeraMIRScan), including labels to detect and segment defects. The dataset comprises 29 volumes corresponding to MIR-OCT scans of 3D printed ceramic pieces, decomposed into 21,882 B-scan images, each paired with expert-annotated binary masks capturing defects such as pores, delaminations, and inclusions. Notably, 41.38% of the images contain visible anomalies. To illustrate the dataset’s applicability to DL, we provide baseline segmentation results using a U-Net architecture, achieving 80.55% precision, 80.00% recall, and an 80.27% Dice score. CeraMIRScan offers a benchmark resource for advancing automated quality assessment and supports the development of MIR-OCT-based defect characterization methods.</p>

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CeraMIRScan: Mid-infrared OCT Scan Dataset for Ceramic Quality Assessment

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月24日

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  • Natalia P. García-de-la-Puente,
  • Fernando García-Torres,
  • Andrés Laveda-Martínez,
  • Coraline Lapre,
  • Niels Møller Israelsen,
  • Ole Bang,
  • Dominik Brouczek,
  • Niels Benson,
  • Adrián Colomer,
  • Valery Naranjo

摘要

Mid-infrared Optical Coherence Tomography (MIR-OCT) is a promising Non-Destructive Testing (NDT) technique due to its high-resolution imaging capabilities and extensive applicability across various industrial domains. Studies developing Deep Learning (DL) models to detect defects in MIR-OCT scans are scarce, and few have been used for ceramic quality assessment. To address this gap, we introduce the MIR-OCT Scan Dataset for Ceramic Quality Assessment (CeraMIRScan), including labels to detect and segment defects. The dataset comprises 29 volumes corresponding to MIR-OCT scans of 3D printed ceramic pieces, decomposed into 21,882 B-scan images, each paired with expert-annotated binary masks capturing defects such as pores, delaminations, and inclusions. Notably, 41.38% of the images contain visible anomalies. To illustrate the dataset’s applicability to DL, we provide baseline segmentation results using a U-Net architecture, achieving 80.55% precision, 80.00% recall, and an 80.27% Dice score. CeraMIRScan offers a benchmark resource for advancing automated quality assessment and supports the development of MIR-OCT-based defect characterization methods.