Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026
[Diffusion cycle-consistent generative adversarial networks for pelvic active bone marrow segmentation].
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Zhuo Li, Zeng Min, Tan Shunqian, Liang Tao, Xiao Weiwei, Zhen Xin
Аннотация
OBJECTIVES: To establish a pelvic active bone marrow (ABM) segmentation method based on diffusion cycle-consistent generative adversarial networks for improving individualized precision of conventional anatomical atlas-based methods.
METHODS: We collected pelvic PET-CT data from 253 patients and constructed a 3-stage cascaded cross-modal learning framework for precise individualized ABM identification from CT images. The framework used cycle-consistent generative adversarial networks for bidirectional CT-PET mapping, conditional diffusion modules with 1000-step Markov chains for progressive denoising, and multi-scale progressive feature pyramid fusion networks for segmentation. The peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), normalized mean square error (NMSE), Dice similarity coefficient (DSC), and average symmetric surface distance (ASSD) were used for evaluation of the model performance for ABM segmentation.
RESULTS: The proposed method outperformed the existing methods with a PSNR of 26.42±0.63 dB, an SSIM of 0.894±0.011, and an NMSE of 0.0235±0.0026. For ABM segmentation, the average Dice coefficient of the model reached 0.777±0.023 with an ASSD of 3.52±0.41 mm.
CONCLUSIONS: Compared with the conventional methods, the propose method significantly improves individualized segmentation accuracy of the ABM and is thus suitable use in individualized bone marrow protection radiotherapy for rectal cancer.
METHODS: We collected pelvic PET-CT data from 253 patients and constructed a 3-stage cascaded cross-modal learning framework for precise individualized ABM identification from CT images. The framework used cycle-consistent generative adversarial networks for bidirectional CT-PET mapping, conditional diffusion modules with 1000-step Markov chains for progressive denoising, and multi-scale progressive feature pyramid fusion networks for segmentation. The peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), normalized mean square error (NMSE), Dice similarity coefficient (DSC), and average symmetric surface distance (ASSD) were used for evaluation of the model performance for ABM segmentation.
RESULTS: The proposed method outperformed the existing methods with a PSNR of 26.42±0.63 dB, an SSIM of 0.894±0.011, and an NMSE of 0.0235±0.0026. For ABM segmentation, the average Dice coefficient of the model reached 0.777±0.023 with an ASSD of 3.52±0.41 mm.
CONCLUSIONS: Compared with the conventional methods, the propose method significantly improves individualized segmentation accuracy of the ABM and is thus suitable use in individualized bone marrow protection radiotherapy for rectal cancer.