Automatic contour correction of pectus excavatum using computer-aided diagnosis and convolutional neural network

作者：Cai, Siqi; Liao, Yizhi; Lai, Lixuan; Zhou, Haiyu^*; Xie, Longhan^*

来源：ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2023, 126: 107032.

DOI：10.1016/j.engappai.2023.107032

• Pectus excavatum
• Convolutional neural network
• Computer-aided diagnosis
• Computed tomography
• Deep learning

Pectus excavatum (PE) is a common congenital sternal malformation disease that significantly impacts the physical and psychological well-being of affected individuals. Traditional methods for diagnosing and correcting PE rely heavily on physician expertise, leading to potential uncertainties and errors. While current research has primarily focused on automatic indices extraction and deformity evaluation, there is a lack of emphasis on generating corrective solutions for patients with PE. To address these limitations, we present a novel convolutional neural network (CNN)-based computer-aided diagnosis (CAD) approach for automatically generating recommended corrections for patients with PE. Specifically, our approach involves training a CNN model using sternum contours from normal individuals to predict corrected sternum contours for patients. Through block-wise fine-tuning using transfer learning, we optimize the regression performance for three PE indices. The complete contours of patients are then depicted based on the predicted indices using our CAD method. To validate our approach, we collected a dataset comprising 11,755 chest CT images from 40 PE patients and 40 healthy individuals. The results suggest there is no significant difference between the predicted contours generated by our model and the actual postoperative contours by skilled surgeons, underscoring the promising efficacy of our model. In summary, our novel approach goes beyond the limitations of existing techniques and offers a significant advancement in the diagnosis and correction of PE.

• 单位
  广东省人民医院