论文标题
一单击病变恢复测量和CT扫描的分割
One Click Lesion RECIST Measurement and Segmentation on CT Scans
论文作者
论文摘要
在临床试验中，放射科医生的常规工作之一是使用RECIST标准（实体瘤的响应评估标准）在医学图像上测量肿瘤大小。但是，手动测量是乏味的，可能会受到观察者间的变异性。我们提出了一个统一的框架，用于半自动性病变\ textit {se} gentration和recist \ textit {e}刺激对整个人体的各种病变。仅需要在病变附近单击一次来提供简单的指导。 Sient由两个主要部分组成。第一个通过一键指导提取了感兴趣的病变，大致将病变分割，并估计其恢复测量。基于第一个网络的结果，第二个网络完善了病变细分和恢复估计。 Sichet在病变细分中实现了最新的性能，并在大规模公共深度数据集上进行了估算。它为放射科医生提供了一种实用的工具，可以用最少的人力努力生成可靠的病变测量值（即分割面罩和恢复），并且大大减少了时间。
In clinical trials, one of the radiologists' routine work is to measure tumor sizes on medical images using the RECIST criteria (Response Evaluation Criteria In Solid Tumors). However, manual measurement is tedious and subject to inter-observer variability. We propose a unified framework named SEENet for semi-automatic lesion \textit{SE}gmentation and RECIST \textit{E}stimation on a variety of lesions over the entire human body. The user is only required to provide simple guidance by clicking once near the lesion. SEENet consists of two main parts. The first one extracts the lesion of interest with the one-click guidance, roughly segments the lesion, and estimates its RECIST measurement. Based on the results of the first network, the second one refines the lesion segmentation and RECIST estimation. SEENet achieves state-of-the-art performance in lesion segmentation and RECIST estimation on the large-scale public DeepLesion dataset. It offers a practical tool for radiologists to generate reliable lesion measurements (i.e. segmentation mask and RECIST) with minimal human effort and greatly reduced time.
