Compared with the rapidly growing artificial intelligence (AI) research in other branches of healthcare, the pace of developing AI capacities in dental care is relatively slow. Dental care automation, especially the automated capability for dental cone beam computed tomography (CBCT) segmentation and lesion detection, is highly needed. CBCT is an important imaging modality that is experiencing ever-growing utilization in various dental specialties. However, little research has been done for segmenting different structures, restorative materials, and lesions using deep learning. This is due to multifold challenges such as content-rich oral cavity and significant within-label variation on each CBCT image as well as the inherent difficulty of obtaining many high-quality labeled images for training. On the other hand, oral-anatomical knowledge exists in dentistry, which shall be leveraged and integrated into the deep learning design. In this article, we propose a novel anatomically constrained Dense U-Net for integrating oral-anatomical knowledge with data-driven Dense U-Net. The proposed algorithm is formulated as a regularized or constrained optimization and solved using mean-field variational approximation to achieve computational efficiency. Mathematical encoding for transforming descriptive knowledge into a quantitative form is also proposed. Our experiment demonstrates that the proposed algorithm outperforms the standard Dense U-Net in both lesion detection accuracy and dice coefficient (DICE) indices in multilabel segmentation. Benefited from the integration with anatomical domain knowledge, our algorithm performs well with data from a small number of patients included in the training.
|Original language||English (US)|
|Journal||IEEE Transactions on Automation Science and Engineering|
|State||Accepted/In press - 2020|
- Biomedical image segmentation
- Deep learning
- healthcare automation
- Image segmentation
- Knowledge engineering
- machine learning
- neural networks.
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering