Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 510640, China.
Division of Thoracic Surgery, Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital and Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.
Sci Rep. 2020 Nov 20;10(1):20294. doi: 10.1038/s41598-020-77361-y.
Pectus excavatum (PE) is one of the most common chest wall defects. Accurate assessment of PE deformities is critical for effective surgical intervention. Index-based evaluations have become the standard for objectively estimating PE, however, these indexes cannot represent the whole information of chest CT images and may associated with significant error due to the individual differences. To overcome these limitations, this paper developed a computer-aided diagnosis (CAD) system based on the convolutional neural network (CNN) to automatically learn discriminative features and classify PE images. We also adopted block-wise fine-tuning methods based on the transfer learning strategy to reduce the potential risk of overfitting caused by limited data and experimentally explored the best fine-tuning degree. Our method achieved a high level of classification accuracy with 94.76% for PE diagnosis. Furthermore, we proposed a majority rule-based voting method to provide a comprehensively diagnostic results for each patient, which integrated the classification results of the whole thorax. The promising results support the feasibility of our proposed CNN-based CAD system for automatic PE diagnosis, which paves a way for comprehensive assessments of PE in clinics.
漏斗胸（PE）是最常见的胸廓畸形之一。准确评估 PE 畸形对于有效的手术干预至关重要。基于指数的评估已成为客观估计 PE 的标准，然而，这些指数不能代表胸部 CT 图像的全部信息，并且由于个体差异可能会导致显著的误差。为了克服这些限制，本文开发了一种基于卷积神经网络（CNN）的计算机辅助诊断（CAD）系统，以自动学习有区别的特征并对 PE 图像进行分类。我们还采用基于迁移学习策略的分块精细调整方法，以降低由于数据有限而导致的潜在过度拟合风险，并在实验中探索了最佳的精细调整程度。我们的方法实现了 94.76%的 PE 诊断的高精度分类准确率。此外，我们提出了一种基于多数规则的投票方法，为每个患者提供全面的诊断结果，该方法整合了整个胸部的分类结果。有前途的结果支持了我们提出的基于 CNN 的 CAD 系统用于自动 PE 诊断的可行性，为临床中对 PE 的全面评估铺平了道路。
