کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4965006 1447940 2017 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Classification of teeth in cone-beam CT using deep convolutional neural network
ترجمه فارسی عنوان
طبقه بندی دندان ها در CT دایره ای مخروطی با استفاده از شبکه عصبی کانولوشن عمیق
کلمات کلیدی
شبکه های عصبی کانولوشن عمیق؛ طبقه بندی دندان؛ CT مخروطی پرتو دندانپزشکی ؛ شناسایی پزشکی قانونی؛ نمودار دندانپزشکی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی


- A new application of deep convolutional neural network to dental images was explored.
- Good classification performance was obtained even with a small number of data.
- Data augmentation, especially the intensity transformation was effective in improving the classification performance.
- Performance was not strongly dependent on resizing methods except for the crop method.
- DCNN was effective in tooth classification without the need for precise tooth segmentation

Dental records play an important role in forensic identification. To this end, postmortem dental findings and teeth conditions are recorded in a dental chart and compared with those of antemortem records. However, most dentists are inexperienced at recording the dental chart for corpses, and it is a physically and mentally laborious task, especially in large scale disasters. Our goal is to automate the dental filing process by using dental x-ray images. In this study, we investigated the application of a deep convolutional neural network (DCNN) for classifying tooth types on dental cone-beam computed tomography (CT) images. Regions of interest (ROIs) including single teeth were extracted from CT slices. Fifty two CT volumes were randomly divided into 42 training and 10 test cases, and the ROIs obtained from the training cases were used for training the DCNN. For examining the sampling effect, random sampling was performed 3 times, and training and testing were repeated. We used the AlexNet network architecture provided in the Caffe framework, which consists of 5 convolution layers, 3 pooling layers, and 2 full connection layers. For reducing the overtraining effect, we augmented the data by image rotation and intensity transformation. The test ROIs were classified into 7 tooth types by the trained network. The average classification accuracy using the augmented training data by image rotation and intensity transformation was 88.8%. Compared with the result without data augmentation, data augmentation resulted in an approximately 5% improvement in classification accuracy. This indicates that the further improvement can be expected by expanding the CT dataset. Unlike the conventional methods, the proposed method is advantageous in obtaining high classification accuracy without the need for precise tooth segmentation. The proposed tooth classification method can be useful in automatic filing of dental charts for forensic identification.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers in Biology and Medicine - Volume 80, 1 January 2017, Pages 24-29
نویسندگان
, , , , , , ,