同时分割脑室与病灶，区分正常与异常高信号，提升多发性硬化诊断精度。
Adversarial Deep Learning for Simultaneous Segmentation of Ventricular and White Matter Hyperintensities in Clinical MRI
- 联合训练模型同步分割脑室和白质高信号，引入注意力机制增强区分能力。
- 平均Dice达0.852，对异常病灶分割准确率显著优于现有方法。
- 适合临床部署，单例处理仅需4秒，效率提升超36倍。
目的：多发性硬化（MS）诊断需准确评估脑部MRI中的白质高信号（WMH）和脑室变化。现有方法独立处理两类结构，难以区分正常与病理性高信号，且在非各向同性临床数据上表现不佳。本文提出一种深度学习框架，可同步分割脑室与WMH，同时区分正常周围脑室高信号与病理性MS病灶。方法：基于300例MS患者FLAIR扫描及MSSEG2016基准数据集（15例），构建2D pix2pix架构。通过五折交叉验证并按患者分层，系统比较五种结构变体，逐步整合对抗训练、注意力加权判别与自适应混合损失。性能对比六种主流方法，评估指标包括Dice系数、Hausdorff距离、精确率与召回率。结果：最终架构（V5）在所有类别上平均Dice为0.852±0.004，HD95为4.87±0.13mm；脑室分割Dice为0.907±0.002，HD95为3.00±0.51mm；异常WMH Dice为0.825±0.009，HD95为4.51±0.32mm；正常WMH Dice为0.677±0.007。V5在本地数据上全面超越所有基线方法。消融分析表明，对抗训练带来最大单点提升（Dice+0.109）。端到端处理每例仅需约4秒，较基线快至多36倍。结论：该经系统验证的框架融合对抗训练、注意力加权判别与自适应损失调度，在精度、病灶可解释性与计算效率方面均表现优异，适用于常规临床工作流程。
原文摘要 · Abstract (English)
Purpose: Multiple sclerosis (MS) diagnosis requires accurate assessment of white matter hyperintensities (WMH) and ventricular changes on brain MRI. Current methods treat these structures independently, struggle to differentiate normal from pathological hyperintensities, and perform poorly on anisotropic clinical data. We present a deep learning framework that simultaneously segments ventricles and WMH while distinguishing normal periventricular hyperintensities from pathological MS lesions. Methods: We developed a 2D pix2pix architecture trained on FLAIR scans from 300 MS patients combined with the MSSEG2016 benchmark (15 patients). Five architectural variants were compared through systematic ablation using 5-fold cross-validation with patient-level stratification, progressively integrating adversarial training, attention-weighted discrimination, and adaptive hybrid loss. Performance was assessed against six established methods using Dice coefficient, Hausdorff distance, precision, and recall. Results: The final architecture (V5) achieved mean Dice 0.852+/-0.004 and HD95 4.87+/-0.13mm across all classes. Per-class performance: ventricles (Dice 0.907+/-0.002, HD95 3.00+/-0.51mm), abnormal WMH (Dice 0.825+/-0.009, HD95 4.51+/-0.32mm), normal WMH (Dice 0.677+/-0.007). V5 outperformed all baselines on local data for both ventricle and WMH segmentation. Ablation analysis confirmed adversarial training provided the largest single gain (+0.109 Dice). End-to-end processing required ~4 seconds per case-up to 36x faster than baseline methods. Conclusions: This systematically validated framework combines adversarial training, attention-weighted discrimination, and adaptive loss scheduling to achieve improved accuracy, clinically relevant lesion differentiation, and computational efficiency suitable for routine clinical workflows.
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