数据稀缺环境下长尾出血性病变分割的合成训练
Synthetic training for long-tail haemorrhagic lesion segmentation in data-scarce settings
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中文总结 AI 辅助
针对数据稀缺的长尾出血性病变分割，提出无需真实标注的合成训练框架，利用放射学先验生成图像-标签对，在cSS和CMB上优于经典滤波基线。
中文摘要 AI 辅助
脑微出血（CMBs）和皮质浅表铁沉积（cSS）是脑小血管疾病的影像学标志物，但其自动分割受到阳性病例和体素级标注稀缺的限制。我们提出了一种用于长尾出血性病变分割的合成训练框架，该框架在训练时无需真实病变标注，并利用病变的放射学描述。从解剖学脑分区出发，该框架应用空间增强和体素重采样，利用病变位置和形态的临床先验知识程序化插入cSS和CMB标签，并通过随机强度分配、模糊和Rician噪声模拟合成图像。模型在动态生成的图像-标签对上训练，并在10例cSS和13例CMB病例中与手动勾画进行了评估。所提出的配置优于经典滤波基线。对于cSS，低信号约束模型比Frangi滤波器获得了更高的AUPRC和AUROC（AUPRC：0.284对比0.083；AUROC：0.907对比0.731）。对于CMB，将血管显式合成为病变模拟物，相比经典基线提高了性能（AUPRC：0.538对比0.004；AUROC：0.999对比0.968）。这些结果支持我们的提议作为数据稀缺出血性病变分割的可行策略。
英文摘要
Cerebral microbleeds (CMBs) and cortical superficial siderosis (cSS) are imaging markers of cerebral small vessel disease, but their automated segmentation is limited by the scarcity of positive cases and voxel-level annotations. We propose a synthetic training framework for long-tail haemorrhagic lesion segmentation that requires no real lesion annotations for training and leverages radiological description of the lesions. Starting from anatomical brain parcellations, the framework applies spatial augmentation and voxel resampling, procedurally inserts cSS and CMB labels using clinical priors on lesion location and morphology, and synthesises images through randomised intensity assignment, blurring, and Rician noise simulation. Models were trained on dynamically generated image-label pairs and evaluated against manual delineations in 10 cSS cases and 13 CMB cases. The proposed configurations outperformed classical filter baselines. For cSS, the hypointensity constrained model achieved higher AUPRC and AUROC than the Frangi filter (AUPRC: 0.284 vs 0.083; AUROC: 0.907 vs 0.731). For CMBs, explicit synthesis of blood vessels as lesion mimics improved performance over the classical baseline (AUPRC: 0.538 vs 0.004; AUROC: 0.999 vs 0.968). These results support our proposal as a feasible strategy for data-scarce haemorrhagic lesion segmentation.
发表机构
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)（埃尔朗根-纽伦堡弗里德里希-亚历山大大学（FAU））
- Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）
- German Centre for Neurodegenerative Diseases (DZNE)（德国神经退行性疾病中心（DZNE））
机构由 AI 辅助整理，请以论文原文为准。
