Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, USA.
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, USA.
Comput Med Imaging Graph. 2024 Jun;114:102363. doi: 10.1016/j.compmedimag.2024.102363. Epub 2024 Mar 1.
Reliable localization of lymph nodes (LNs) in multi-parametric MRI (mpMRI) studies plays a major role in the assessment of lymphadenopathy and staging of metastatic disease. Radiologists routinely measure the nodal size in order to distinguish benign from malignant nodes, which require subsequent cancer staging. However, identification of lymph nodes is a cumbersome task due to their myriad appearances in mpMRI studies. Multiple sequences are acquired in mpMRI studies, including T2 fat suppressed (T2FS) and diffusion weighted imaging (DWI) sequences among others; consequently, the sizing of LNs is rendered challenging due to the variety of signal intensities in these sequences. Furthermore, radiologists can miss potentially metastatic LNs during a busy clinical day. To lighten these imaging and workflow challenges, we propose a computer-aided detection (CAD) pipeline to detect both benign and malignant LNs in the body for their subsequent measurement. We employed the recently proposed Dynamic Head (DyHead) neural network to detect LNs in mpMRI studies that were acquired using a variety of scanners and exam protocols. The T2FS and DWI series were co-registered, and a selective augmentation technique called Intra-Label LISA (ILL) was used to blend the two volumes with the interpolation factor drawn from a Beta distribution. In this way, ILL diversified the samples that the model encountered during the training phase, while the requirement for both sequences to be present at test time was nullified. Our results showed a mean average precision (mAP) of 53.5% and a sensitivity of ∼78% with ILL at 4 FP/vol. This corresponded to an improvement of ≥10% in mAP and ≥12% in sensitivity at 4FP (p ¡ 0.05) respectively over current LN detection approaches evaluated on the same dataset. We also established the out-of-distribution robustness of the DyHead model by training it on data acquired by a Siemens Aera scanner and testing it on data from the Siemens Verio, Siemens Biograph mMR, and Philips Achieva scanners. Our pilot work represents an important first step towards automated detection, segmentation, and classification of lymph nodes in mpMRI.
在多参数 MRI（mpMRI）研究中，可靠地定位淋巴结（LNs）对于评估淋巴结病和转移性疾病的分期起着重要作用。放射科医生通常会测量淋巴结的大小，以区分良性和恶性淋巴结，从而需要进一步进行癌症分期。然而，由于在 mpMRI 研究中淋巴结的形态多种多样，因此识别淋巴结是一项繁琐的任务。mpMRI 研究中会获取多种序列，包括 T2 脂肪抑制（T2FS）和弥散加权成像（DWI）等序列；因此，由于这些序列中的信号强度多种多样，因此对淋巴结进行测量具有挑战性。此外，放射科医生在忙碌的临床日可能会错过潜在的转移性淋巴结。为了减轻这些成像和工作流程的挑战，我们提出了一种计算机辅助检测（CAD）管道，用于检测身体中的良性和恶性淋巴结，以便对其进行后续测量。我们使用最近提出的动态头部（DyHead）神经网络来检测使用各种扫描仪和检查协议获取的 mpMRI 研究中的淋巴结。T2FS 和 DWI 系列进行了配准，并使用一种称为内标签 LISA（ILL）的选择性增强技术来融合两个体积，插值因子取自 Beta 分布。通过这种方式，ILL 在训练阶段使模型遇到的样本多样化，同时在测试时不需要同时存在两个序列。我们的结果表明，使用 ILL 时，平均精度（mAP）为 53.5%，灵敏度约为 78%，4 FP/vol 时的假阳性率为 4 个。这对应于在相同数据集上评估的当前 LN 检测方法的 mAP 提高了≥10%，灵敏度提高了≥12%，假阳性率降低了 4 个（p ¡ 0.05）。我们还通过在西门子 Aera 扫描仪上获取的数据对 DyHead 模型进行训练，并在西门子 Verio、西门子 Biograph mMR 和飞利浦 Achieva 扫描仪上的数据进行测试，从而证明了 DyHead 模型的分布外稳健性。我们的初步工作代表了迈向自动检测、分割和分类 mpMRI 中淋巴结的重要第一步。
