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Abstract：
Objective: To apply the technique of deep learning on a small dataset of panoramic images for the detection and segmentation of the mental foramen (MF). Study design: In this study we used in-house dataset created within the School of Dental Medicine, Tel Aviv University. The dataset contained randomly chosen and anonymized 112 digital panoramic X-ray images and corresponding segmentations of MF. In order to solve the task of segmentation of the MF we used a single fully convolution neural network, that was based on U-net as well as a cascade architecture. 70% of the data were randomly chosen for training, 15% for validation and accuracy was tested on 15%. The model was trained using NVIDIA GeForce GTX 1080 GPU. The SPSS software, version 17.0 (Chicago, IL, USA) was used for the statistical analysis. The study was approved by the ethical committee of Tel Aviv University. Results: The best results of the dice similarity coefficient ( DSC), precision, recall, MF-wise true positive rate (MFTPR) and MF-wise false positive rate (MFFPR) in single networks were 49.51%, 71.13%, 68.24%, 87.81% and 14.08%, respectively. The cascade of networks has shown better results than simple networks in recall and MFTPR, which were 88.83%, 93.75%, respectively, while DSC and precision achieved the lowest values, 31.77% and 23.92%, respectively. Conclusions: Currently, the U-net, one of the most used neural network architectures for biomedical application, was effectively used in this study. Methods based on deep learning are extremely important for automatic detection and segmentation in radiology and require further development.
摘要：
目的：将深度学习技术应用于小数据集的全景图像中，对精神孔（MF）进行检测和分割。研究设计：在这项研究中，我们使用了牙科医学院内部创建的内部数据集，特拉维夫大学。数据集包含随机选择和匿名化的112个数字全景X射线图像和MF的相应分割。为了解决MF的分割任务，我们使用了单个完全卷积神经网络，这是基于U网以及级联架构。70%的数据是随机选择进行训练的，15%进行验证，15%进行准确性测试。该模型使用NVIDIAGeForceGTX1080GPU进行训练。SPSS软件,版本17.0（芝加哥，IL,美国)用于统计分析。这项研究得到了特拉维夫大学伦理委员会的批准。结果:骰子相似系数(DSC)结果最好,精度,召回，单网络MF型真阳性率(MFTPR)和MF型假阳性率(MFFPR)分别为49.51%,71.13%，68.24%，87.81%和14.08%，分别。级联网络在召回和MFTPR方面比简单网络显示出更好的结果，占88.83%，93.75%,分别,而DSC和精度达到了最低值，31.77%和23.92%，分别。结论：目前，U-net,生物医学应用中最常用的神经网络架构之一，在这项研究中得到了有效的应用。基于深度学习的方法对于放射学中的自动检测和分割极其重要，需要进一步发展。
