Computed tomography (CT) scanning is a fast and painless procedure that can capture clear imaging information beneath the abdomen and is widely used to help diagnose and monitor disease progress. The pelvic lymph node is a key indicator of colorectal cancer metastasis. In the traditional process, an experienced radiologist must read all the CT scanning images slice by slice to track the lymph nodes for future diagnosis. However, this process is time-consuming, exhausting, and subjective due to the complex pelvic structure, numerous blood vessels, and small lymph nodes. Therefore, automated methods are desirable to make this process easier. Currently, the available open-source CTLNDataset only contains large lymph nodes. Consequently, a new data set called PLNDataset, which is dedicated to lymph nodes within the pelvis, is constructed to solve this issue. A two-level annotation calibration method is proposed to guarantee the quality and correctness of pelvic lymph node annotation. Moreover, a novel system composed of a keyframe localization network and a lymph node detection network is proposed to detect pelvic lymph nodes in CT scanning images. The proposed method makes full use of two kinds of prior knowledge: spatial prior knowledge for keyframe localization and anchor prior knowledge for lymph node detection. A series of experiments are carried out to evaluate the proposed method, including ablation experiments, comparing other state-of-the-art methods, and visualization of results. The experimental results demonstrate that our proposed method outperforms other methods on PLNDataset and CTLNDataset. This system is expected to be applied in future clinical practice.

中文翻译：

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盆腔淋巴结智能检测系统

计算机断层扫描 (CT) 扫描是一种快速且无痛的程序，可以捕获腹部下方的清晰成像信息，广泛用于帮助诊断和监测疾病进展。盆腔淋巴结是结直肠癌转移的关键指标。在传统过程中，经验丰富的放射科医师必须逐片读取所有CT扫描图像，以跟踪淋巴结以供将来诊断。然而，由于盆腔结构复杂、血管众多、淋巴结细小，该过程耗时、费力且主观。因此，需要自动化方法来简化此过程。目前，可用的开源 CTLNDataset 仅包含大淋巴结。因此，一个名为 PLNDataset 的新数据集专门用于骨盆内的淋巴结，是为了解决这个问题而构建的。提出了一种二级注释校准方法，以保证盆腔淋巴结注释的质量和正确性。此外，提出了一种由关键帧定位网络和淋巴结检测网络组成的新系统，用于检测 CT 扫描图像中的盆腔淋巴结。该方法充分利用了两种先验知识：用于关键帧定位的空间先验知识和用于淋巴结检测的锚点先验知识。进行了一系列实验来评估所提出的方法，包括消融实验、比较其他最先进的方法以及结果的可视化。实验结果表明，我们提出的方法在 PLNDataset 和 CTLNDataset 上优于其他方法。