Segmentation of teeth in panoramic dental X-ray images using U-Net with a loss function weighted on the tooth edge

Panoramic dental X-ray imaging is an established method for the diagnosis of dental problems. However, the resolution of panoramic dental X-ray images is relatively low. Thus, early lesions are often overlooked. As the first step in the development of a computer-aided diagnosis scheme for panoramic...

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Bibliographic Details
Published in:	Radiological physics and technology Vol. 14; no. 1; pp. 64 - 69
Main Authors:	Nishitani, Yuya, Nakayama, Ryohei, Hayashi, Daisei, Hizukuri, Akiyoshi, Murata, Kan
Format:	Journal Article
Language:	English
Published:	Singapore Springer Singapore 01-03-2021 Springer Nature B.V
Subjects:	Datasets Diagnosis Image segmentation Imaging Medical and Radiation Physics Medicine Medicine & Public Health Nuclear Medicine Radiology Radiotherapy Teeth Training X ray imagery Panoramic dental X-ray Loss function Tooth segmentation U-net
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Summary:	Panoramic dental X-ray imaging is an established method for the diagnosis of dental problems. However, the resolution of panoramic dental X-ray images is relatively low. Thus, early lesions are often overlooked. As the first step in the development of a computer-aided diagnosis scheme for panoramic dental X-ray images, we propose a computerized method for the segmentation of teeth using U-Net with a loss function weighted on the tooth edge. Our database consisted of 162 panoramic dental X-ray images. The training dataset consisted of 102 images, while the remaining 60 images were used as the test dataset. The loss function obtained by the cross entropy (CE) in the entire image is usually used in training U-Net. To improve the segmentation accuracy of the tooth edge, a loss function weighted on the tooth edge is proposed by adding the CE in the tooth edge region to the CE for the entire image. The mean Jaccard index and Dice index for U-Net with the loss function combining the CEs for the entire image and tooth edge were 0.864 and 0.927, respectively, which were significantly larger than those for U-Net with the CE for the entire image (0.802 and 0.890, p < 0.001) and U-Net with the CE for the tooth edge (0.826 and 0.905, p < 0.001). U-Net with the new loss function exhibited a higher segmentation accuracy of the tooth in panoramic dental X-ray images than that obtained by U-Net with the conventional loss function.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1865-0333 1865-0341
DOI:	10.1007/s12194-020-00603-1