■从牙锥束计算机断层扫描(CBCT)精确分割牙源性囊性病变(OCL)对于有效的牙科诊断至关重要。尽管监督学习方法在分割各种疾病方面已经显示出实际的诊断结果,它们分割涵盖不同亚类品种的OCL的能力尚未得到广泛研究。
■在这项研究中,我们提出了一种新的监督学习方法,称为OCL-Net,它结合了多尺度U-Net模型,以及经过联合监督损失训练的自动适应机制。回顾性收集了一家医院的匿名CBCT图像。为了评估我们的模型提高颌面外科医生诊断效率的能力,我们进行了一项诊断评估,包括7名临床医生在有或没有自动分段面罩辅助的情况下进行诊断.
■我们收集了300张匿名CBCT图像,这些图像被手动注释以用于分割掩模。大量实验证明了我们的OCL-Net对CBCTOCL分割的有效性,实现88.84%的整体骰子得分,IoU得分为81.23%,AUC评分为92.37%。通过我们的诊断评估,我们发现,当临床医生得到来自OCL-Net的分割标签的辅助时,他们的平均诊断准确率从53.21%提高到55.71%,而平均花费时间从101s显著减少到47s(P<0.05)。
■这些发现证明了我们的方法作为CBCT图像中OCL的鲁棒自动分割系统的潜力,而分段面罩可用于进一步提高OCLs牙科诊断效率。
UNASSIGNED: Precise segmentation of Odontogenic Cystic Lesions (OCLs) from dental Cone-Beam Computed Tomography (CBCT) is critical for effective dental diagnosis. Although supervised learning methods have shown practical diagnostic results in segmenting various diseases, their ability to segment OCLs covering different sub-class varieties has not been extensively investigated.
UNASSIGNED: In this study, we propose a new supervised learning method termed OCL-Net that combines a Multi-Scaled U-Net model, along with an Auto-Adapting mechanism trained with a combined supervised loss. Anonymous CBCT images were collected retrospectively from one hospital. To assess the ability of our model to improve the diagnostic efficiency of maxillofacial surgeons, we conducted a diagnostic assessment where 7 clinicians were included to perform the diagnostic process with and without the assistance of auto-segmentation masks.
UNASSIGNED: We collected 300 anonymous CBCT images which were manually annotated for segmentation masks. Extensive experiments demonstrate the effectiveness of our OCL-Net for CBCT OCLs segmentation, achieving an overall Dice score of 88.84%, an IoU score of 81.23%, and an AUC score of 92.37%. Through our diagnostic assessment, we found that when clinicians were assisted with segmentation labels from OCL-Net, their average diagnostic accuracy increased from 53.21% to 55.71%, while the average time spent significantly decreased from 101s to 47s (P<0.05).
UNASSIGNED: The findings demonstrate the potential of our approach as a robust auto-segmentation system on OCLs in CBCT images, while the segmented masks can be used to further improve OCLs dental diagnostic efficiency.