A supervised 'lesion-enhancement' filter by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD).
A supervised 'lesion-enhancement' filter by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD).
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DOI：
10.1088/0031-9155/54/18/s03
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发表时间：
2009-09-21
影响因子：
3.5
通讯作者：
Suzuki K
中科院分区：
文献类型：
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作者：
Suzuki K
计算机辅助诊断（CAD）一直是医学图像分析中的一个活跃的研究领域。用于增强病变的滤波器对于提高CAD方案中的灵敏度和特异性起着重要作用。滤波器增强与滤波器中采用的模型相似的对象;例如，基于Hessian矩阵的斑点增强滤波器增强类球形对象。然而，实际病变通常不同于简单模型，例如，肺结节通常被建模为实心球体，但是存在各种形状的结节，并且在内部具有不均匀性，例如具有毛刺和毛玻璃样混浊的结节。因此，常规滤波器通常不能增强实际病变。我们在这项研究中的目的是开发一个监督过滤器，用于增强实际病变（而不是病变模型），通过使用一个重复训练的人工神经网络（MTANN）在CAD计划中检测肺结节的CT。用CT图像中的实际结节训练MTANN滤波器，以增强结节的实际模式。通过使用MTANN滤波器，我们的CAD方案的灵敏度和特异性大大提高。在69个肺癌的数据库中，MTANN滤波器的结节候选检测达到了97%的灵敏度，每个切片有6.7个假阳性（FP），而差分图像技术的结节候选检测达到了96%的灵敏度，每个切片有19.3个FP。分类MTANN应用于进一步减少FP。分类MTANN去除了60%的FP，损失了1个真阳性;因此，它实现了96%的灵敏度，每个切片有2.7个FP。总的来说，我们的CAD方案的基础上的MTANN过滤器和分类MTANN，84%的灵敏度与0.5 FP每节实现。
Computer-aided diagnosis (CAD) has been an active area of study in medical image analysis. A filter for enhancement of lesions plays an important role for improving the sensitivity and specificity in CAD schemes. The filter enhances objects similar to a model employed in the filter; e.g., a blob-enhancement filter based on the Hessian matrix enhances sphere-like objects. Actual lesions, however, often differ from a simple model, e.g., a lung nodule is generally modeled as a solid sphere, but there are nodules of various shapes and with inhomogeneities inside such as a nodule with spiculations and ground-glass opacity. Thus, conventional filters often fail to enhance actual lesions. Our purpose in this study was to develop a supervised filter for enhancement of actual lesions (as opposed to a lesion model) by use of a massive-training artificial neural network (MTANN) in a CAD scheme for detection of lung nodules in CT. The MTANN filter was trained with actual nodules in CT images to enhance actual patterns of nodules. By use of the MTANN filter, the sensitivity and specificity of our CAD scheme were improved substantially. With a database of 69 lung cancers, nodule candidate detection by the MTANN filter achieved a 97% sensitivity with 6.7 false positives (FPs) per section, whereas nodule candidate detection by a difference-image technique achieved a 96% sensitivity with 19.3 FPs per section. Classification MTANNs were applied for further reduction of the FPs. The classification MTANNs removed 60% of the FPs with a loss of 1 true positive; thus, it achieved a 96% sensitivity with 2.7 FPs per section. Overall, with our CAD scheme based on the MTANN filter and classification MTANNs, an 84% sensitivity with 0.5 FPs per section was achieved.