Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
J Digit Imaging. 2017 Oct;30(5):629-639. doi: 10.1007/s10278-017-9968-3.
We propose a generalized framework for developing computer-aided detection (CADe) systems whose characteristics depend only on those of the training dataset. The purpose of this study is to show the feasibility of the framework. Two different CADe systems were experimentally developed by a prototype of the framework, but with different training datasets. The CADe systems include four components; preprocessing, candidate area extraction, candidate detection, and candidate classification. Four pretrained algorithms with dedicated optimization/setting methods corresponding to the respective components were prepared in advance. The pretrained algorithms were sequentially trained in the order of processing of the components. In this study, two different datasets, brain MRA with cerebral aneurysms and chest CT with lung nodules, were collected to develop two different types of CADe systems in the framework. The performances of the developed CADe systems were evaluated by threefold cross-validation. The CADe systems for detecting cerebral aneurysms in brain MRAs and for detecting lung nodules in chest CTs were successfully developed using the respective datasets. The framework was shown to be feasible by the successful development of the two different types of CADe systems. The feasibility of this framework shows promise for a new paradigm in the development of CADe systems: development of CADe systems without any lesion specific algorithm designing.
我们提出了一个用于开发计算机辅助检测（CADe）系统的通用框架，其特性仅取决于训练数据集的特性。本研究的目的是展示该框架的可行性。通过该框架的原型，我们实验性地开发了两个不同的 CADe 系统，但使用了不同的训练数据集。CADe 系统包括四个组件：预处理、候选区域提取、候选检测和候选分类。我们预先准备了四个具有针对各个组件的专用优化/设置方法的预训练算法。预训练算法按照组件的处理顺序依次进行训练。在这项研究中，我们收集了两个不同的数据集，即带有脑动脉瘤的脑部 MRA 和带有肺结节的胸部 CT，以在框架中开发两种不同类型的 CADe 系统。通过三折交叉验证评估了开发的 CADe 系统的性能。成功地使用各自的数据集开发了用于检测脑部 MRA 中的脑动脉瘤和用于检测胸部 CT 中的肺结节的 CADe 系统。该框架通过成功开发两种不同类型的 CADe 系统证明了其可行性。该框架的可行性为 CADe 系统的开发提供了一种新的范例：无需任何特定于病变的算法设计即可开发 CADe 系统。
