基于扩散循环一致性生成对抗网络的骨盆活跃骨髓区域分割方法

doi:10.12122/j.issn.1673-4254.2026.01.24

南方医科大学学报 ›› 2026, Vol. 46 ›› Issue (1): 219-230.doi: 10.12122/j.issn.1673-4254.2026.01.24

基于扩散循环一致性生成对抗网络的骨盆活跃骨髓区域分割方法

卓俐¹(), 曾敏¹, 谭顺谦¹, 梁涛¹, 肖巍魏², 甄鑫¹()

^1.南方医科大学生物医学工程学院，广东广州 510515
^2.中山大学肿瘤防治中心//华南肿瘤学国家重点实验室//肿瘤医学省部共建协同创新中心，广东广州 510060

收稿日期:2025-06-13 出版日期:2026-01-20 发布日期:2026-01-16
通讯作者: 甄鑫 E-mail:zhuoli0901@163.com;xinzhen@smu.edu.cn
作者简介:卓俐，在读硕士研究生，E-mail: zhuoli0901@163.com
基金资助:
国家自然科学基金(82572381);国家自然科学基金(82573772);国家自然科学基金青年基金(62106058);广东省自然科学基金(2024A1515012100)

Diffusion cycle-consistent generative adversarial networks for pelvic active bone marrow segmentation

Li ZHUO¹(), Min ZENG¹, Shunqian TAN¹, Tao LIANG¹, Weiwei XIAO², Xin ZHEN¹()

^1.School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
^2.Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China

Received:2025-06-13 Online:2026-01-20 Published:2026-01-16
Contact: Xin ZHEN E-mail:zhuoli0901@163.com;xinzhen@smu.edu.cn
Supported by:
National Natural Science Foundation of China(82572381);Supported by Natural Science Foundation for the Youth (NSFY) of China(62106058)

摘要/Abstract

摘要：

目的建立基于扩散循环一致性生成对抗网络的骨盆活跃骨髓（ABM）分割方法，突破传统解剖图谱方法个体化精度不足的技术瓶颈。方法收集253例患者骨盆PET-CT数据，构建三阶段级联跨模态学习框架实现从CT到个体化ABM精准识别。首先通过循环一致性生成对抗网络建立CT-PET双向映射，采用9个残差模块学习跨模态特征关系。设计条件扩散模块基于1000步马尔可夫链实现渐进去噪，融合双向交叉注意力机制动态整合解剖与功能信息。最后构建多尺度渐进式特征金字塔分割网络，在4个尺度层级累积多模态特征实现ABM区域分割。采用峰值信噪比（PSNR）、结构相似性指数（SSIM）、归一化均方误差（NMSE）评估图像合成质量，Dice相似系数（DSC）、平均对称表面距离（ASSD）评估分割性能。结果本方法均优于现有方法，PSNR达到26.42±0.63 dB，SSIM达到0.894±0.011，NMSE降至0.0235±0.0026。在ABM分割任务中，平均Dice系数达到0.777±0.023，ASSD降至3.52±0.41 mm。结论与传统方法相比，该方法显著提高了个体化分割精度，适用于直肠癌个体化骨髓保护放疗的临床应用。

关键词: 直肠癌, 活跃骨髓, 扩散模型, 生成对抗网络, 图像分割

Abstract:

Objective 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. Conclusion 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.

Key words: rectal cancer, active bone marrow, diffusion models, generative adversarial networks, image segmentation

卓俐, 曾敏, 谭顺谦, 梁涛, 肖巍魏, 甄鑫. 基于扩散循环一致性生成对抗网络的骨盆活跃骨髓区域分割方法[J]. 南方医科大学学报, 2026, 46(1): 219-230.

Li ZHUO, Min ZENG, Shunqian TAN, Tao LIANG, Weiwei XIAO, Xin ZHEN. Diffusion cycle-consistent generative adversarial networks for pelvic active bone marrow segmentation[J]. Journal of Southern Medical University, 2026, 46(1): 219-230.

图/表 12

图1 基于扩散循环一致性生成对抗网络的骨盆活跃骨髓区分割方法整体架构图

Fig.1 Overall architecture of the pelvic active bone marrow segmentation method based on diffusion cycle-consistent generative adversarial network.

图2 循环一致性生成对抗网络模块架构图

Fig.2 Architecture diagram of the cycle-consistent generative adversarial network module. A: Overall architecture of the CycleGAN establishing bidirectional mapping. B: Generator network architecture containing 9 residual blocks. C: Discriminator network architecture employing spectral normalization.

图3 条件扩散模块跨模态动态去噪方法

Fig.3 Cross-modal dynamic denoising method of the conditional diffusion module. A: Forward noise addition and reverse denoising reconstruction processes based on Markov chain. B: Structure of the cross-modal dynamic denoising network integrating Adaptive Weight Fusion Module and Bidirectional Cross-Attention

图4 多尺度渐进式特征金字塔融合分割网络架构

Fig.4 Architecture of multi-scale progressive feature pyramid fusion segmentation network.

图5 典型PET-CT图像数据及ABM标注示例

Fig.5 Typical PET-CT image data and ABM annotation examples.

表1 各图像合成方法的性能比较

Tab.1 Quantitative comparison of different methods for image synthesis quality

Methods	PSNR	SSIM	NMSE	Average time (Epoch/s)
U-net^[20]	21.27±1.31	0.787±0.031	0.0438±0.0062	57.1
Pix2pix^[16]	22.61±1.04	0.815±0.026	0.0359±0.0045	48.9
CycleGAN^[21]	24.33±0.91	0.861±0.022	0.0297±0.0041	53.4
WGAN^[22]	20.42±1.52	0.771±0.035	0.0481±0.0073	65.3
DCGAN^[23]	19.08±1.89	0.738±0.044	0.0541±0.0095	44.8
DDPM^[18]	23.71±0.97	0.849±0.024	0.0324±0.0042	197.2
Ours	26.42±0.63	0.894±0.011	0.0235±0.0026	239.8

图6 不同方法生成图像的可视化对比

Fig.6 Visual comparison of synthesized images with different methods.

表2 多种方法在不同ABM区域的分割性能比较

Tab.2 Comparison of segmentation performance of different methods in different ABM regions.

Methods	Dice				ASSD
Methods	Lumbosacral spine	Ilium	Lower pelvis	Average	Lumbosacral spine	Ilium	Lower pelvis	Average
GAN+U-net^[20]	0.696±0.031	0.652±0.039	0.638±0.035	0.662±0.035	4.21±0.48	5.08±0.63	5.61±0.71	4.97±0.61
SynSeg-Net^[24]	0.718±0.027	0.675±0.034	0.661±0.032	0.685±0.031	3.89±0.44	4.68±0.58	5.15±0.64	4.57±0.55
Robust-Mseg^[25]	0.731±0.024	0.693±0.030	0.675±0.028	0.700±0.027	3.67±0.40	4.35±0.52	4.83±0.58	4.28±0.50
SCM^[26]	0.715±0.029	0.677±0.033	0.661±0.031	0.684±0.031	3.87±0.43	4.62±0.56	5.10±0.62	4.53±0.54
PEMMA^[27]	0.748±0.022	0.709±0.026	0.688±0.027	0.715±0.025	3.45±0.38	4.12±0.49	4.56±0.55	4.04±0.47
Ours	0.806±0.019	0.771±0.023	0.754±0.026	0.777±0.023	3.02±0.33	3.58±0.41	3.95±0.49	3.52±0.41

图7 ABM分割结果的可视化示例

Fig.7 Visual examples of ABM segmentation results. (A) Lumbosacral spine region; (B) Ilium region; (C) Lower pelvis region; (D) Representative case with ground truth (green) and predictions (blue).

表3 不同扩散步数下的图像生成效果及分割预测对比

Tab.3 Comparison of image generation effects and segmentation prediction under different diffusion steps

Diffusion steps T	PSNR (dB)	SSIM	NMSE	Dice	Average time (Epoch/s)
100	22.28±1.29	0.797±0.043	0.0449±0.0076	0.548±0.049	113.5
250	23.94±1.05	0.827±0.036	0.0383±0.0063	0.625±0.041	142.6
500	25.58±0.86	0.861±0.029	0.0305±0.0050	0.715±0.020	184.0
1000	26.42±0.63	0.894±0.011	0.0235±0.0026	0.777±0.023	239.8
1500	26.21±0.70	0.888±0.025	0.0238±0.0039	0.768±0.030	337.1
2000	26.03±0.76	0.884±0.028	0.0245±0.0043	0.761±0.033	431.0

表4 不同权重策略对模型性能的影响

Tab.4 Impact of different weighting strategies on model performance

Weight strategy	Weight function	PSNR (dB)	SSIM	NMSE	Dice
Fixed weight (0.3:0.7)	$W C T = 0.3$	24.91±0.92	0.851±0.026	0.0339±0.0048	0.728±0.037
Fixed weight (0.5:0.5)	$W C T = 0.5$	25.07±0.84	0.862±0.022	0.0323±0.0044	0.745±0.033
Fixed weight (0.7:0.3)	$W C T = 0.7$	24.73±0.99	0.845±0.029	0.0357±0.0055	0.719±0.040
Linear decay	$W C T = 1 - t T$	25.58±0.75	0.871±0.018	0.0287±0.0036	0.763±0.026
Cosine decay	$W C T = 1 + c o s (π t T) 2$	25.84±0.70	0.878±0.016	0.0271±0.0033	0.769±0.024
Exponential decay	$W C T = e - λ t$	26.08±0.66	0.885±0.014	0.0253±0.0030	0.772±0.023
Exponential decay+error feedback	$W C T = e - λ t (1 + β ϵ)$	26.42±0.63	0.894±0.011	0.0235±0.0026	0.777±0.023

表4 不同权重策略对模型性能的影响

Tab.4 Impact of different weighting strategies on model performance

Weight strategy	Weight function	PSNR (dB)	SSIM	NMSE	Dice
Fixed weight (0.3:0.7)	$W C T = 0.3$	24.91±0.92	0.851±0.026	0.0339±0.0048	0.728±0.037
Fixed weight (0.5:0.5)	$W C T = 0.5$	25.07±0.84	0.862±0.022	0.0323±0.0044	0.745±0.033
Fixed weight (0.7:0.3)	$W C T = 0.7$	24.73±0.99	0.845±0.029	0.0357±0.0055	0.719±0.040
Linear decay	$W C T = 1 - t T$	25.58±0.75	0.871±0.018	0.0287±0.0036	0.763±0.026
Cosine decay	$W C T = 1 + c o s (π t T) 2$	25.84±0.70	0.878±0.016	0.0271±0.0033	0.769±0.024
Exponential decay	$W C T = e - λ t$	26.08±0.66	0.885±0.014	0.0253±0.0030	0.772±0.023
Exponential decay+error feedback	$W C T = e - λ t (1 + β ϵ)$	26.42±0.63	0.894±0.011	0.0235±0.0026	0.777±0.023

表5 消融实验结果

Tab.5 Ablation study results

Configuration	Input modality	Dice	ASSD	PSNR (dB)	SSIM	NMSE
Baseline	CT only	0.664±0.036	4.97±0.62	-	-	-
+CycleGAN	CT+pseudo-PET	0.702±0.028	4.28±0.51	24.35±0.81	0.861±0.017	0.0281±0.0033
+Conditional diffusion	CT+enhanced-PET	0.726±0.031	4.11±0.55	26.42±0.63	0.894±0.011	0.0235±0.0026
+Multi-scale fusion	CT+enhanced-PET+advanced fusion	0.758±0.025	3.76±0.44	26.42±0.63	0.894±0.011	0.0235±0.0026
Complete method	Full pipeline with joint optimization	0.777±0.023	3.52±0.41	26.42±0.63	0.894±0.011	0.0235±0.0026

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基于扩散循环一致性生成对抗网络的骨盆活跃骨髓区域分割方法

Diffusion cycle-consistent generative adversarial networks for pelvic active bone marrow segmentation

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