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Hybrid Multiple-Organ Segmentation Method Using Multiple U-Nets in PET/CT Images

Applied Sciences (Switzerland), ISSN: 2076-3417, Vol: 13, Issue: 19
2023
  • 1
    Citations
  • 0
    Usage
  • 4
    Captures
  • 2
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    1
  • Captures
    4
  • Mentions
    2
    • Blog Mentions
      1
      • Blog
        1
    • News Mentions
      1
      • 1

Most Recent News

Research Reports from Fujita Health University Provide New Insights into Applied Sciences (Hybrid Multiple-Organ Segmentation Method Using Multiple U-Nets in PET/CT Images)

2023 OCT 18 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Daily News -- Investigators publish new report on applied sciences. According

Article Description

PET/CT can scan low-dose computed tomography (LDCT) images with morphological information and PET images with functional information. Because the whole body is targeted for imaging, PET/CT examinations are important in cancer diagnosis. However, the several images obtained by PET/CT place a heavy burden on radiologists during diagnosis. Thus, the development of computer-aided diagnosis (CAD) and technologies assisting in diagnosis has been requested. However, because FDG accumulation in PET images differs for each organ, recognizing organ regions is essential for developing lesion detection and analysis algorithms for PET/CT images. Therefore, we developed a method for automatically extracting organ regions from PET/CT images using U-Net or DenseUNet, which are deep-learning-based segmentation networks. The proposed method is a hybrid approach combining morphological and functional information obtained from LDCT and PET images. Moreover, pre-training using ImageNet and RadImageNet was performed and compared. The best extraction accuracy was obtained by pre-training ImageNet with Dice indices of 94.1, 93.9, 91.3, and 75.1% for the liver, kidney, spleen, and pancreas, respectively. This method obtained better extraction accuracy for low-quality PET/CT images than did existing studies on PET/CT images and was comparable to existing studies on diagnostic contrast-enhanced CT images using the hybrid method and pre-training.

Bibliographic Details

Yuta Suganuma; Kuniaki Saito; Atsushi Teramoto; Hiroshi Fujita; Yuki Suzuki; Shoji Kido; Noriyuki Tomiyama

MDPI AG

Materials Science; Physics and Astronomy; Engineering; Chemical Engineering; Computer Science

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