Universal lesion detection in computed tomography (CT) images is an important yet challenging task due to the large variations in lesion type, size, shape, and appearance. Considering that data in clinical routine (such as the DeepLesion dataset) are usually annotated with a long and a short diameter according to the standard of Response Evaluation Criteria in Solid Tumors (RECIST) diameters, we propose RECIST-Net, a new approach to lesion detection in which the four extreme points and center point of the RECIST diameters are detected. By detecting a lesion as keypoints, we provide a more conceptually straightforward formulation for detection, and overcome several drawbacks (e.g., requiring extensive effort in designing data-appropriate anchors and losing shape information) of existing bounding-box-based methods while exploring a single-task, one-stage approach compared to other RECIST-based approaches. Experiments show that RECIST-Net achieves a sensitivity of 92.49% at four false positives per image, outperforming other recent methods including those using multi-task learning.
翻译:计算断层成像(CT)图象中的普遍损伤检测是一项重要但具有挑战性的任务,因为损伤类型、大小、形状和外观差异很大。考虑到临床常规数据(如深层数据集)通常根据固体温度直径反应评价标准标准,用长直径和短直径附加说明,我们建议使用RECIST-Net,这是检测损害的新方法,其中检测到RECIST直径的四个极端点和中点。通过检测作为关键点的损害,我们提供了更直接的概念化的检测配方,并克服了现有捆绑式方法的若干缺陷(例如,在设计适合数据的锚和丢失形状信息方面需要大量努力),同时探索单任务、一阶段方法,而与其他RECIST-Net方法相比,该方法的敏感度为92.49%,每图象有4个假阳性,优于最近采用的其他方法,包括多塔学习的方法。