标题： 多参数MRI中具有选择性增强的淋巴结检测 显示英文标题

标题： Universal Lymph Node Detection in Multiparametric MRI with Selective Augmentation

摘要： 在多参数磁共振成像（mpMRI）中，对淋巴结（LN）进行鲁棒定位对于评估淋巴结病至关重要。 放射科医生通常会测量淋巴结的大小以区分良性与恶性淋巴结，这需要后续的癌症分期。 由于淋巴结在mpMRI中的表现多样，测量任务变得繁琐，使得测量变得困难。 此外，在繁忙的临床日中，较小且可能是转移性的淋巴结可能会被遗漏。 为了解决这些成像和工作流程问题，我们提出了一种管道，用于检测体内良性和转移性淋巴结，以便随后进行测量。 我们采用了最近提出的VFNet神经网络来识别T2脂肪抑制序列和扩散加权成像（DWI）序列中的淋巴结，这些序列由各种扫描仪使用多种检查协议获取。 我们还使用了一种称为Intra-Label LISA（ILL）的选择性增强技术，以在训练期间多样化模型看到的输入数据样本，从而提高其在评估阶段的鲁棒性。 在每体积4个假阳性（FP/vol）的情况下，使用ILL的灵敏度达到$\sim$83%，而未使用ILL时为$\sim$80%。 与目前在mpMRI上评估的淋巴结检测方法相比，我们在每体积4个假阳性（FP/vol）的情况下显示灵敏度提高了$\sim$9%。 显示英文摘要

摘要： 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.