Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography
Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography
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DOI：
10.1118/1.1580485
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发表时间：
2003-07-01
期刊：
影响因子：
3.8
通讯作者：
Doi, K
中科院分区：
文献类型：
--
作者：
Suzuki, K;Armato, SG;Doi, K
在这项研究中，我们研究了一种模式识别技术的基础上，人工神经网络（ANN），这是所谓的大规模训练人工神经网络（MTANN），减少假阳性的计算机检测肺结节在低剂量计算机断层扫描（CT）图像。MTANN由一个修改后的多层ANN，这是能够直接对图像数据进行操作。通过使用从输入图像中提取的大量子区域以及包含“结节可能性”分布的教师图像来训练MTANN。“输出图像是通过用MTANN扫描输入图像获得的。结节和非结节之间的区别是通过使用根据训练的MTANN的输出图像定义的分数来进行的。为了消除各种类型的非结节，我们扩展了单个MTANN的能力，并开发了多个MTANN（多MTANN）。多MTANN由多个并联布置的MTANN组成。每个MTANN通过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充当特定类型的非结节的专家，例如，训练五个不同的MTANN以区分结节与不同大小的血管;应用另外四个MTANN以消除一些其他的不透明。MTANN的输出通过使用逻辑AND运算进行组合，使得每个训练的MTANN不消除任何结节，但去除了MTANN训练时使用的特定类型的非结节，从而去除了各种类型的非结节。由9个MTANN组成的Multi-MTANN在训练集中用10个典型结节和10个非结节进行训练，所述10个典型结节和10个非结节代表9种不同非结节类型中的每一种（总共90个训练非结节）。训练的Multi-MTANN应用于减少我们目前的计算机化肺结节检测方案报告的假阳性，该方案基于63个低剂量CT扫描（1765个切片）的数据库，其中包含71个确认的结节，包括66个活检确认的原发性癌症，来自肺癌筛查计划。Multi-MTANN应用于我们当前方案在验证测试中报告的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，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，我们目前的方案的假阳性率从0.98改善到0.18假阳性/切片（从27.4改善到4.8/患者），总体灵敏度为80.3%（57/71）。(C)2003年美国医学物理学家协会。
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). (C) 2003 American Association of Physicists in Medicine.