论文标题
使用轻型伪3D 3D卷积和表面重点回归的深容量通用病变检测
Deep Volumetric Universal Lesion Detection using Light-Weight Pseudo 3D Convolution and Surface Point Regression
论文作者
论文摘要
从患者CT扫描中准确，全面地识别，测量和报告病变是医生重要但耗时的程序。计算机辅助病变/重要发现检测技术是医学成像的核心，由于3D成像中病变的外观，位置和大小分布的差异很大，因此仍然非常具有挑战性。 In this work, we propose a novel deep anchor-free one-stage VULD framework that incorporates (1) P3DC operators to recycle the architectural configurations and pre-trained weights from the off-the-shelf 2D networks, especially ones with large capacities to cope with data variance, and (2) a new SPR method to effectively regress the 3D lesion spatial extents by pinpointing their representative key points on lesion surfaces.实验验证首先是对公共大规模NIH DeepLeperion数据集进行的，我们提出的方法可提供新的最先进的定量性能。我们还在内部数据集上测试了VLAD，以进行肝肿瘤检测。在CT成像中的大尺度和小型肿瘤数据集中，沃尔德概括了。
Identifying, measuring and reporting lesions accurately and comprehensively from patient CT scans are important yet time-consuming procedures for physicians. Computer-aided lesion/significant-findings detection techniques are at the core of medical imaging, which remain very challenging due to the tremendously large variability of lesion appearance, location and size distributions in 3D imaging. In this work, we propose a novel deep anchor-free one-stage VULD framework that incorporates (1) P3DC operators to recycle the architectural configurations and pre-trained weights from the off-the-shelf 2D networks, especially ones with large capacities to cope with data variance, and (2) a new SPR method to effectively regress the 3D lesion spatial extents by pinpointing their representative key points on lesion surfaces. Experimental validations are first conducted on the public large-scale NIH DeepLesion dataset where our proposed method delivers new state-of-the-art quantitative performance. We also test VULD on our in-house dataset for liver tumor detection. VULD generalizes well in both large-scale and small-sized tumor datasets in CT imaging.
