  Automatic teeth segmentation in panoramic x-ray images is an important
research subject of the image analysis in dentistry. In this study, we propose
a post-processing stage to obtain a segmentation map in which the objects in
the image are separated, and apply this technique to tooth instance
segmentation with U-Net network. The post-processing consists of grayscale
morphological and filtering operations, which are applied to the sigmoid output
of the network before binarization. A dice overlap score of 95.4 - 0.3% is
obtained in overall teeth segmentation. The proposed post-processing stages
reduce the mean error of tooth count to 6.15%, whereas the error without
post-processing is 26.81%. The performances of both segmentation and tooth
counting are the highest in the literature, to our knowledge. Moreover, this is
achieved by using a relatively small training dataset, which consists of 105
images. Although the aim in this study is to segment tooth instances, the
presented method is applicable to similar problems in other domains, such as
separating the cell instances
