深度学习下儿童髓母放疗脑及椎骨亚结构自动分割算法研究

王为; 陈淑贤; 蒋马伟

doi:10.12455/j.issn.1671-7104.250219

深度学习下儿童髓母放疗脑及椎骨亚结构自动分割算法研究

Deep Learning-Based Automated Segmentation of Brain and Vertebral Substructures for Radiotherapy in Pediatric Medulloblastoma

摘要

摘要: 为评估nnU-Net和FuseNet模型在儿童髓母亚结构自动分割中应用可行性，回顾分析60例按5岁分组的放疗患儿(≤5岁组和>5岁组），基于CT-MRI融合图像勾画脑亚结构、CT图像勾画椎骨亚结构，训练U-Net、nnU-Net和FuseNet三种卷积神经网络模型并评估结果，每组设训练集24例，测试与验证集6例，另经20例外部独立队列验证泛化性。比较三种模型与图谱库法（Atlas）的DSC，评估nnU-Net和FuseNet的HD95、RAVD等几何指标及人工修正耗时。结果显示，FuseNet在脑亚结构分割中表现最优，其在两组椎骨亚结构分割上均优于Atlas、U-Net（P=0.028、0.005 和P=0.005、0.005），与nnU-Net无显著差异（p=0.107、0.236）。≤5岁组中，FuseNet除小脑前叶和海马外，>5岁组中除海马外，其余亚结构DSC均值均>0.8，且两组人工修正耗时均最短。结论表明，nnU-Net可实现较好分割，FuseNet通过多模态特征动态融合提升脑亚结构分割精度，且修正效率最高。

Abstract: To evaluate nnU-Net and FuseNet for automatic segmentation of pediatric medulloblastoma substructures, 60 radiotherapy patients (≤5 and >5 years) were analyzed. Brain substructures were delineated on CT-MRI fusion images, vertebral ones on CT. U-Net, nnU-Net and FuseNet were trained (24 cases/group) and tested/validated (6 cases/group), with 20 external cases verifying generalization. DSC was compared with Atlas; nnU-Net/FuseNet’s HD95, RAVD and manual correction time were evaluated. FuseNet performed best in brain segmentation, outperforming Atlas and U-Net in vertebral substructures for both age groups (P=0.028/0.005 and 0.005/0.005) but not differing from nnU-Net (P=0.107/0.236). Its DSC exceeded 0.8 for most substructures (except cerebellar anterior lobe/hippocampus in ≤5 years; hippocampus in >5 years), with shortest correction time. nnU-Net achieved good segmentation; FuseNet improved brain segmentation accuracy via dynamic multimodal feature fusion, with highest correction efficiency.

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