论文标题
对点云的深度学习，用于在胸部CT扫描中的结节检测时假阳性减少
Deep Learning on Point Clouds for False Positive Reduction at Nodule Detection in Chest CT Scans
论文作者
论文摘要
本文着重于在可疑病变检测阶段进行计算机辅助检测（CADE）系统中结节候选物的假阳性减少（FPR）的新方法。与医学图像分析中的典型决策相反，所提出的方法认为输入数据不是2D或3D图像，而是作为点云，并为点云使用深度学习模型。我们发现，与传统的CNN 3D相比，Point Cloud模型需要更少的内存，并且在训练和推理中更快，它们具有更好的性能，并且不会对输入图像的大小施加限制，即对Nodule候选者的大小没有限制。我们提出了一种用于将3D CT扫描数据转换为点云的算法。在某些情况下，结节候选者的体积可能比周围的环境小得多，例如，在胸膜下定位的情况下。因此，我们从候选区域的3D图像构成的点云中开发了一种用于采样点的算法。该算法能够保证捕获上下文和候选信息，作为结节候选者点云的一部分。我们为FPR任务的功能设计并设置了一个从开放的LIDC-IDRI数据库创建数据集的实验，并在此详细描述。应用数据增强既可以避免过度拟合，又是一种UPS采样方法。实验是用PointNet，PointNet ++和DGCNN进行的。我们表明，所提出的方法的表现优于基线CNN 3D模型，对于基线模型而言，FROC与77.26的Froc相对于77.26。我们将我们的算法与已发表的SOTA进行了比较，并证明即使没有重大修改，它也可以在LUNA2016上适当的性能水平上起作用，并在LIDC-IDRI上显示SOTA。
This paper focuses on a novel approach for false-positive reduction (FPR) of nodule candidates in Computer-aided detection (CADe) systems following the suspicious lesions detection stage. Contrary to typical decisions in medical image analysis, the proposed approach considers input data not as a 2D or 3D image, but rather as a point cloud, and uses deep learning models for point clouds. We discovered that point cloud models require less memory and are faster both in training and inference compared to traditional CNN 3D, they achieve better performance and do not impose restrictions on the size of the input image, i.e. no restrictions on the size of the nodule candidate. We propose an algorithm for transforming 3D CT scan data to point cloud. In some cases, the volume of the nodule candidate can be much smaller than the surrounding context, for example, in the case of subpleural localization of the nodule. Therefore, we developed an algorithm for sampling points from a point cloud constructed from a 3D image of the candidate region. The algorithm is able to guarantee the capture of both context and candidate information as part of the point cloud of the nodule candidate. We designed and set up an experiment in creating a dataset from an open LIDC-IDRI database for a feature of the FPR task, and is herein described in detail. Data augmentation was applied both to avoid overfitting and as an upsampling method. Experiments were conducted with PointNet, PointNet++, and DGCNN. We show that the proposed approach outperforms baseline CNN 3D models and resulted in 85.98 FROC versus 77.26 FROC for baseline models. We compare our algorithm with published SOTA and demonstrate that even without significant modifications it works at the appropriate performance level on LUNA2016 and shows SOTA on LIDC-IDRI.
