Portela R D S, Pereira J R G, Costa M G F, Filho C F F Costa
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1246-1249. doi: 10.1109/EMBC44109.2020.9175478.
Lung cancer is, by far, the leading cause of cancer death in the world. Tools for automated medical imaging analysis development of a Computer-Aided Diagnosis method comprises several tasks. In general, the first one is the segmentation of region of interest, for example, lung region segmentation from Chest X-ray imaging in the task of detecting lung cancer. Deep Convolutional Neural Networks (DCNN) have shown promising results in the task of segmentation in medical images. In this paper, to implement the lung region segmentation task on chest X-ray images, was evaluated three different DCNN architectures in association with different regularization (Dropout, L2, and Dropout + L2) and optimization methods (SGDM, RMSPROP and ADAM). All networks were applied in the Japanese Society of Radiological Technology (JSRT) database. The best results were obtained using Dropout + L2 as regularization method and ADAM as optimization method. Considering the Jaccard Coefficient obtained (0.97967 ± 0.00232) the proposal outperforms the state of the art.Clinical Relevance- The presented method reduces the time that a professional takes to perform lung segmentation, improving the effectiveness.
肺癌是目前全球癌症死亡的主要原因。用于自动医学影像分析的计算机辅助诊断方法的开发包括多个任务。一般来说，第一个任务是感兴趣区域的分割，例如，在肺癌检测任务中从胸部X光影像中分割出肺部区域。深度卷积神经网络（DCNN）在医学图像分割任务中已显示出有前景的结果。在本文中，为了在胸部X光图像上实现肺部区域分割任务，评估了三种不同的DCNN架构，并结合不同的正则化方法（随机失活、L2和随机失活+L2）以及优化方法（随机梯度下降动量法、均方根传播法和自适应矩估计法）。所有网络都应用于日本放射技术学会（JSRT）数据库。使用随机失活+L2作为正则化方法和自适应矩估计法作为优化方法时获得了最佳结果。考虑到所获得的杰卡德系数（0.97967±0.00232），该方法优于现有技术。临床相关性——所提出的方法减少了专业人员进行肺部分割所需的时间，提高了效率。
