Suzuki Kenji, Armato Samuel G, Li Feng, Sone Shusuke, Doi Kunio
Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, The University of Chicago, Chicago, Illinois 60637, USA.
Med Phys. 2003 Jul;30(7):1602-17. doi: 10.1118/1.1580485.
In this study, we investigated a pattern-recognition technique based on an artificial neural network (ANN), which is called a massive training artificial neural network (MTANN), for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography (CT) images. The MTANN consists of a modified multilayer ANN, which is capable of operating on image data directly. The MTANN is trained by use of a large number of subregions extracted from input images together with the teacher images containing the distribution for the "likelihood of being a nodule." The output image is obtained by scanning an input image with the MTANN. The distinction between a nodule and a non-nodule is made by use of a score which is defined from the output image of the trained MTANN. In order to eliminate various types of non-nodules, we extended the capability of a single MTANN, and developed a multiple MTANN (Multi-MTANN). The Multi-MTANN consists of plural MTANNs that are arranged in parallel. Each MTANN is trained by using the same nodules, but with a different type of non-nodule. Each MTANN acts as an expert for a specific type of non-nodule, e.g., five different MTANNs were trained to distinguish nodules from various-sized vessels; four other MTANNs were applied to eliminate some other opacities. The outputs of the MTANNs were combined by using the logical AND operation such that each of the trained MTANNs eliminated none of the nodules, but removed the specific type of non-nodule with which the MTANN was trained, and thus removed various types of non-nodules. The Multi-MTANN consisting of nine MTANNs was trained with 10 typical nodules and 10 non-nodules representing each of nine different non-nodule types (90 training non-nodules overall) in a training set. The trained Multi-MTANN was applied to the reduction of false positives reported by our current computerized scheme for lung nodule detection based on a database of 63 low-dose CT scans (1765 sections), which contained 71 confirmed nodules including 66 biopsy-confirmed primary cancers, from a lung cancer screening program. The Multi-MTANN was applied to 58 true positives (nodules from 54 patients) and 1726 false positives (non-nodules) reported by our current scheme in a validation test; these were different from the training set. The results indicated that 83% (1424/1726) of non-nodules were removed with a reduction of one true positive (nodule), i.e., a classification sensitivity of 98.3% (57 of 58 nodules). By using the Multi-MTANN, the false-positive rate of our current scheme was improved from 0.98 to 0.18 false positives per section (from 27.4 to 4.8 per patient) at an overall sensitivity of 80.3% (57/71).
在本研究中，我们调查了一种基于人工神经网络（ANN）的模式识别技术，即大规模训练人工神经网络（MTANN），用于减少低剂量计算机断层扫描（CT）图像中肺结节计算机检测的假阳性。MTANN由一个经过修改的多层ANN组成，它能够直接对图像数据进行操作。MTANN通过使用从输入图像中提取的大量子区域以及包含“为结节的可能性”分布的教师图像进行训练。通过用MTANN扫描输入图像来获得输出图像。通过使用从训练后的MTANN的输出图像定义的分数来区分结节和非结节。为了消除各种类型的非结节，我们扩展了单个MTANN的能力，并开发了一个多重MTANN（Multi-MTANN）。Multi-MTANN由多个并行排列的MTANN组成。每个MTANN使用相同的结节进行训练，但使用不同类型的非结节。每个MTANN充当特定类型非结节的专家，例如，训练了五个不同的MTANN以区分结节与各种大小的血管；另外四个MTANN用于消除其他一些不透明物。通过使用逻辑与运算来组合MTANN的输出，使得每个训练后的MTANN都不会消除任何结节，但会去除其训练所针对的特定类型的非结节，从而去除各种类型的非结节。由九个MTANN组成的Multi-MTANN在一个训练集中用10个典型结节和10个非结节进行训练，这10个非结节代表九种不同非结节类型中的每一种（总共90个训练非结节）。将训练后的Multi-MTANN应用于减少我们当前基于63次低剂量CT扫描（1765个切片）数据库的肺结节检测计算机方案报告的假阳性，该数据库来自肺癌筛查项目，包含71个确诊结节，其中包括66个经活检确诊的原发性癌症。在一次验证测试中，将Multi-MTANN应用于我们当前方案报告的58个真阳性（来自54名患者的结节）和1726个假阳性（非结节）；这些与训练集不同。结果表明，83%（1424/1726）的非结节被去除，同时减少了一个真阳性（结节），即分类敏感性为98.3%（58个结节中的57个）。通过使用Multi-MTANN，我们当前方案的假阳性率从每切片0.98提高到0.18个假阳性（从每名患者27.4个提高到4.8个），总体敏感性为80.3%（57/71）。
