대한임베디드공학회논문지 (IEMEK Journal of Embedded Systems and Applications)

대한임베디드공학회 (Institute of Embedded Engineering of Korea)
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Efficient Semi-automatic Annotation System based on Deep Learning

  • Hyunseok Lee (Daegu Gyeongbuk Medical Innovation Foundation) ;
  • Hwa Hui Shin (Daegu Gyeongbuk Medical Innovation Foundation ) ;
  • Soohoon Maeng (Daegu Gyeongbuk Medical Innovation Foundation) ;
  • Dae Gwan Kim (Daegu Gyeongbuk Medical Innovation Foundation ) ;
  • Hyojeong Moon (Daegu Gyeongbuk Medical Innovation Foundation)
  • 투고 : 2023.09.18
  • 심사 : 2023.11.23
  • 발행 : 2023.12.31
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초록

This paper presents the development of specialized software for annotating volume-of-interest on 18F-FDG PET/CT images with the goal of facilitating the studies and diagnosis of head and neck cancer (HNC). To achieve an efficient annotation process, we employed the SE-Norm-Residual Layer-based U-Net model. This model exhibited outstanding proficiency to segment cancerous regions within 18F-FDG PET/CT scans of HNC cases. Manual annotation function was also integrated, allowing researchers and clinicians to validate and refine annotations based on dataset characteristics. Workspace has a display with fusion of both PET and CT images, providing enhance user convenience through simultaneous visualization. The performance of deeplearning model was validated using a Hecktor 2021 dataset, and subsequently developed semi-automatic annotation functionalities. We began by performing image preprocessing including resampling, normalization, and co-registration, followed by an evaluation of the deep learning model performance. This model was integrated into the software, serving as an initial automatic segmentation step. Users can manually refine pre-segmented regions to correct false positives and false negatives. Annotation images are subsequently saved along with their corresponding 18F-FDG PET/CT fusion images, enabling their application across various domains. In this study, we developed a semi-automatic annotation software designed for efficiently generating annotated lesion images, with applications in HNC research and diagnosis. The findings indicated that this software surpasses conventional tools, particularly in the context of HNC-specific annotation with 18F-FDG PET/CT data. Consequently, developed software offers a robust solution for producing annotated datasets, driving advances in the studies and diagnosis of HNC.

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과제정보

This research was supported by core technology development project for reorganization of new industries through the Korea Institute for Advancement of Technology (KIAT) funded by the Ministry of Trade, Industry and Energy (MOTIE) (grant number: P0018663).

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