用增强方法提升多参数核磁共振淋巴结检测精度
Universal Lymph Node Detection in Multiparametric MRI with Selective Augmentation
- 采用VFNet模型结合选择性增强技术识别不同扫描仪的淋巴结
- 引入ILL增强后敏感度达83%，比无增强提升3个百分点
- 适合临床快速筛查潜在转移淋巴结，减轻医生负担
在多参数核磁共振（mpMRI）中稳健定位淋巴结对评估淋巴结肿大至关重要。放射科医生通常通过测量淋巴结大小区分良恶性，进而进行癌症分期。但淋巴结在mpMRI中形态多样，尺寸测量困难，小的潜在转移性淋巴结易被遗漏。为此，我们提出一种通用检测流程，可同时识别全身的良性与转移性淋巴结，便于后续测量。基于近期提出的VFNet神经网络，模型在多种扫描仪和检查协议下的T2抑脂及扩散加权成像序列中实现淋巴结检测。同时采用选择性增强技术Intra-Label LISA（ILL），丰富训练数据多样性，提升模型评估阶段鲁棒性。使用ILL时，4个假阳性/体积下敏感度达约83%，未使用则为约80%；相比现有mpMRI淋巴结检测方法，在相同条件下敏感度提升约9%。
原文摘要 · Abstract (English)
Robust localization of lymph nodes (LNs) in multiparametric MRI (mpMRI) is critical for the assessment of lymphadenopathy. Radiologists routinely measure the size of LN to distinguish benign from malignant nodes, which would require subsequent cancer staging. Sizing is a cumbersome task compounded by the diverse appearances of LNs in mpMRI, which renders their measurement difficult. Furthermore, smaller and potentially metastatic LNs could be missed during a busy clinical day. To alleviate these imaging and workflow problems, we propose a pipeline to universally detect both benign and metastatic nodes in the body for their ensuing measurement. The recently proposed VFNet neural network was employed to identify LN in T2 fat suppressed and diffusion weighted imaging (DWI) sequences acquired by various scanners with a variety of exam protocols. We also use a selective augmentation technique known as Intra-Label LISA (ILL) to diversify the input data samples the model sees during training, such that it improves its robustness during the evaluation phase. We achieved a sensitivity of $\sim$83\% with ILL vs. $\sim$80\% without ILL at 4 FP/vol. Compared with current LN detection approaches evaluated on mpMRI, we show a sensitivity improvement of $\sim$9\% at 4 FP/vol.
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