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. Note to Practitioners —This article proposes a novel deep learning algorithm to enable the automated capability for cone beam computed tomography (CBCT) segmentation and lesion detection. Despite the growing adoption of CBCT in various dental specialties, such capability is currently lacking. The proposed work will provide tools to help reduce subjectivity and human errors, as well as streamline and expedite the clinical workflow. This will greatly facilitate dental care automation. Furthermore, due to the capacity of integrating oral-anatomical knowledge into the deep learning design, the proposed algorithm does not require many high-quality labeled images to train. The algorithm can provide good accuracy under limited training samples. This ability is highly desirable for practitioners by saving labor-intensive, costly labeling efforts, and enjoying the benefits provided by AI.

中文翻译：

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解剖学约束的深度学习可自动进行牙科CBCT分割和病变检测

与医疗保健其他部门中快速增长的人工智能（AI）研究相比，在牙科护理领域发展AI能力的步伐相对较慢。急需牙科护理自动化，尤其是用于牙锥束计算机断层扫描（CBCT）分割和病变检测的自动化功能。CBCT是一种重要的成像方式，正在各种牙科专业中得到越来越多的利用。但是，很少有研究使用深度学习来分割不同的结构，修复材料和病变。这是由于多重挑战，例如内容丰富的口腔和每个CBCT图像上的标签内显着变化，以及获得许多高质量的标签图像进行训练所固有的困难。另一方面，牙科中存在口腔解剖学知识，应加以利用并整合到深度学习设计中。在本文中，我们提出了一种新颖的解剖学约束的Dense U-Net，用于将口腔解剖学知识与数据驱动的Dense U-Net集成在一起。提出的算法被公式化为正则化或约束优化，并使用均值场变分逼近法求解以实现计算效率。还提出了用于将描述性知识转换为定量形式的数学编码。我们的实验表明，在多标签分割中，该算法在病变检测准确性和骰子系数（DICE）指标上均优于标准的Dense U-Net。得益于与解剖领域知识的整合，执业者须知 —本文提出了一种新颖的深度学习算法，以实现锥束计算机断层扫描（CBCT）分割和病变检测的自动化功能。尽管CBCT在各种牙科专业中越来越多地采用，但目前仍缺乏这种能力。拟议的工作将提供工具，以帮助减少主观性和人为错误，以及简化和加快临床工作流程。这将极大地促进牙科护理自动化。此外，由于能够将口腔解剖学知识整合到深度学习设计中，因此该算法不需要训练许多高质量的标记图像。在有限的训练样本下，该算法可以提供良好的准确性。节省劳动密集型，昂贵的标签工作，对于从业者来说，此功能是非常理想的，