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Semi-supervised segmentation of metal-artifact contaminated industrial CT images using improved CycleGAN.
Jiang, Shi Bo; Sun, Yue Wen; Xu, Shuo; Zhang, Hua Xia; Wu, Zhi Fang.
Afiliación
  • Jiang SB; Institute of Nuclear and New Energy Technology, Tsinghua University, BeiJing, China.
  • Sun YW; Tsinghua University-Beijing Key Laboratory of Nuclear Detection Technology.
  • Xu S; Institute of Nuclear and New Energy Technology, Tsinghua University, BeiJing, China.
  • Zhang HX; Tsinghua University-Beijing Key Laboratory of Nuclear Detection Technology.
  • Wu ZF; Institute of Nuclear and New Energy Technology, Tsinghua University, BeiJing, China.
J Xray Sci Technol ; 32(2): 271-283, 2024.
Article en En | MEDLINE | ID: mdl-38217629
ABSTRACT
Accurate segmentation of industrial CT images is of great significance in industrial fields such as quality inspection and defect analysis. However, reconstruction of industrial CT images often suffers from typical metal artifacts caused by factors like beam hardening, scattering, statistical noise, and partial volume effects. Traditional segmentation methods are difficult to achieve precise segmentation of CT images mainly due to the presence of these metal artifacts. Furthermore, acquiring paired CT image data required by fully supervised networks proves to be extremely challenging. To address these issues, this paper introduces an improved CycleGAN approach for achieving semi-supervised segmentation of industrial CT images. This method not only eliminates the need for removing metal artifacts and noise, but also enables the direct conversion of metal artifact-contaminated images into segmented images without the requirement of paired data. The average values of quantitative assessment of image segmentation performance can reach 0.96645 for Dice Similarity Coefficient(Dice) and 0.93718 for Intersection over Union(IoU). In comparison to traditional segmentation methods, it presents significant improvements in both quantitative metrics and visual quality, provides valuable insights for further research.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Tomografía Computarizada por Rayos X / Artefactos Idioma: En Revista: J Xray Sci Technol Asunto de la revista: RADIOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Tomografía Computarizada por Rayos X / Artefactos Idioma: En Revista: J Xray Sci Technol Asunto de la revista: RADIOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Países Bajos