BACKGROUND:Precise teeth segmentation from dental panoramic X-ray images is an important task in dental practice. However, several issues including poor image contrast, blurring borders of teeth, presence of jaw bones and other mouth elements, makes reading and examining such images a challenging and time-consuming task for dentists. Thus, developing a precise and automated segmentation technique is required. OBJECTIVE:This study aims to develop and test a novel multi-fusion deep neural net consisting of encoder-decoder architecture for automatic and accurate teeth region segmentation from panoramic X-ray images. METHODS:The encoder has two different streams based on CNN which include the conventional CNN stream and the Atrous net stream. Next, the fusion of features from these streams is done at each stage to encode the contextual rich information of teeth. A dual-type skip connection is then added between the encoder and decoder to minimise semantic information gaps. Last, the decoder comprises deconvolutional layers for reconstructing the segmented teeth map. RESULTS:The assessment of the proposed model is performed on two different dental datasets consisting of 1,500 and 1,000 panoramic X-ray images, respectively. The new model yields accuracy of 97.0% and 97.7%, intersection over union (IoU) score of 91.1% and 90.2%, and dice coefficient score (DCS) of 92.4% and 90.7% for datasets 1 and 2, respectively. CONCLUSION:Applying the proposed model to two datasets outperforms the recent state-of-the-art deep models with a relatively smaller number of parameters and higher accuracy, which demonstrates the potential of the new model to help dentists more accurately and efficiently diagnose dental diseases in future clinical practice.

中文翻译：

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使用多融合深度神经网络在全景 X 射线图像中增强牙齿分割

背景：从牙科全景 X 射线图像中精确分割牙齿是牙科实践中的一项重要任务。然而，图像对比度差、牙齿边界模糊、颌骨和其他口腔元素的存在等几个问题使得阅读和检查此类图像对于牙医来说是一项具有挑战性且耗时的任务。因此，需要开发精确且自动化的分割技术。目的：本研究旨在开发和测试一种由编码器-解码器架构组成的新型多融合深度神经网络，用于从全景 X 射线图像中自动准确地分割牙齿区域。方法：编码器有两种基于CNN的不同流，包括传统的CNN流和Atrous网络流。接下来，在每个阶段对这些流的特征进行融合，以对牙齿的丰富上下文信息进行编码。然后在编码器和解码器之间添加双类型跳跃连接以最小化语义信息间隙。最后，解码器包括用于重建分段牙齿图的反卷积层。结果：对所提出模型的评估是在两个不同的牙科数据集上进行的，这两个数据集分别包含 1,500 张和 1,000 张全景 X 射线图像。对于数据集 1 和 2，新模型的准确率分别为 97.0% 和 97.7%，交并集 (IoU) 得分为 91.1% 和 90.2%，骰子系数得分 (DCS) 分别为 92.4% 和 90.7%。结论：将所提出的模型应用于两个数据集，其参数数量相对较少且准确度更高，优于最近最先进的深度模型，这表明新模型有潜力帮助牙医更准确、更有效地诊断牙科疾病在今后的临床实践中。